Respan Dataset Explorer

corrige en un ingles formal y tecnico para un articulo cientifico el siguiente texto: "A medida que aumenta el valor de K, la puntuación de estereotipo tiende a incrementarse, pasando de 0.933 con K=1 a 0.989 con K=10. En contraste, la puntuación de anti-estereotipo disminuye de 0.886 con K=1 a 0.711 con K=10. Asimismo, la neutralidad también disminuye con el aumento de K, de 0.778 con K=1 a 0.553 con K=10, lo que sugiere respuestas menos neutrales y más polarizadas. La efectividad en la generación de respuestas no ofensivas, medida por la puntuación de NON\_HATE, disminuye de 0.949 con K=1 a 0.754 con K=10. Por otro lado, la puntuación de HATE muestra variabilidad, aumentando de 0.084 con K=1 a 0.218 con K=10, lo que sugiere que con un mayor número de vecinos, el modelo puede capturar más contenido ofensivo presente en los datos."

As the value of K increases, the stereotype score tends to rise, moving from 0.933 at K=1 to 0.989 at K=10. In contrast, the anti-stereotype score decreases from 0.886 at K=1 to 0.711 at K=10. Similarly, neutrality also declines with increasing K, from 0.778 at K=1 to 0.553 at K=10, suggesting less neutral and more polarized responses. The effectiveness in generating non-offensive responses, measured by the NON_HATE score, decreases from 0.949 at K=1 to 0.754 at K=10. Meanwhile, the HATE score shows variability, increasing from 0.084 at K=1 to 0.218 at K=10, which suggests that with a higher number of neighbors, the model may capture more offensive content present in the data.

corrige en un ingles formal y tecnico para un articulo cientifico el siguiente texto: "Evaluación de Métricas relacionadas con la Etica, Sesgo, Rendimiento y Eficiencia Energética en Función de 'K' y Temperatura."

Assessment of Metrics Related to Ethics, Bias, Performance, and Energy Efficiency as a Function of 'K' and Temperature

Faltaria poner que la metrica Chunk Size se fija a un valor de 50

Assessment of Metrics Related to Ethics, Bias, Performance, and Energy Efficiency as a Function of 'K' and Temperature, with Chunk Size Fixed at 50

Me podrias facilitar una conclusiones exaustivas, concisas y precisas para un artículo cientifico dela siguiente experimentacion: "The analysis of the experimental results from embedding an RGA into an LLM presents several evaluation metrics for different parameter configurations, such as chunk size, the K value, temperature, semantic similarity, bleu, rouge1, rouge2, rougeN, esterotipos y odio \cite{castellanos2024evaluation}.

The results presented demonstrate a complex interaction between the parameters of the RAG system and performance metrics, suggesting important considerations for optimizing text generation. First, the analysis reveals that a small 'Chunk Size' (such as 50) combined with an increasing 'K' from 1 to 10 leads to a decrease in similarity and ROUGE metrics, indicating that the integration of context into smaller processing units may compromise the necessary textual cohesion (Table \ref{table:Evalucion10}). This may be due to the fact that a higher 'K' introduces noise by incorporating an excess of contextual information that the system cannot efficiently manage in small data blocks. Conversely, when the 'Chunk Size' is increased to 100, it is observed that raising 'K' to 5 results in an improvement in similarity, indicating a balanced compromise that allows for effective context integration without overwhelming the model with irrelevant information.

However, when the 'Chunk Size' is expanded to ranges between 300 and 1000, a high 'K' combined with low temperatures tends to be detrimental to performance, likely because larger data blocks, when combined with excessive contextualization, overload the model, adversely affecting text quality and reducing the effectiveness of semantic relationships in the generated content. In this regard, intermediate values of 'K' (2 and 5) appear to offer an optimal balance within chunks of size 100 to 300, maximizing performance without introducing significant levels of noise or redundancy (Figure  \ref{fig:/grafica_varia}).

Regarding temperature, it is observed that a low value (0.1) tends to stabilize the generated text, leading to higher similarity and BLEU scores with lower values of "K." This indicates a more controlled processing, less susceptible to irrelevant random deviations. In contrast, a temperature of 0.5 offers a balance between the model's accuracy and creativity, though it exhibits some volatility in configurations with larger chunk sizes. The results suggest that configurations with larger chunk sizes (such as 300 or 500), lower K values (2 or 5), and lower temperatures (0.1 or 0.5) tend to produce improved scores in metrics such as BLEU, similarity, and certain ROUGE metrics, while maintaining relatively lower computational costs and CO2e emissions. However, to optimize specific metrics, such as ROUGE-1 or ROUGE-L, it may be advantageous to explore configurations with higher temperatures. Higher temperatures (0.9) degrade quality metrics due to the introduction of excessive randomness, which negatively affects cohesion in configurations with large chunks and high "K" values. Additionally, the increase in CO2e emissions associated with chunk size and "K" indicates a higher computational cost, underscoring the need for a trade-off between efficiency and performance. Perplexity levels, which measure model uncertainty, rise with higher temperatures, potentially impairing performance in configurations requiring high precision. These findings suggest that a moderate chunk size, combined with an intermediate "K" value and medium temperature, optimizes the quality of generated text while efficiently managing computational resources (Figure \ref{fig:/Diagrama_Calor}).

The examination of relationships with stereotype, anti-stereotype, neutrality, NO HATE, and HATE indicators reveals notable trends influenced by the model's temperature. Stereotype scores increase with temperature, from 0.938 to 0.960 between 0.1 and 0.9, suggesting that higher temperatures capture more biases present in the data. Conversely, the anti-stereotype score also varies with temperature, rising from 0.868 to 0.908 as temperature increases, indicating an enhanced ability of the model to counteract stereotypes. Regarding neutrality, there is a decrease in score from 0.829 to 0.757 with rising temperature, implying that the model generates less neutral and more polarized responses. As for NO HATE, scores fluctuate slightly from 0.927 to 0.933 with increasing temperature, showing minimal influence. However, the HATE score increases significantly from 0.000 to 0.510 when changing from 0.1 to 0.3, indicating greater capture of offensive content at higher temperatures (Figure \ref{fig:/Grafica_barras})

Metrics such as stereotypes, anti-stereotypes, neutrality, NO HATE, and HATE exhibit clear trends as a function of chunk size. As the chunk size increases from 50 to 1000, the stereotype score rises from 0.925 to 0.972, suggesting that larger chunks capture more biases present in the data. Conversely, the anti-stereotype score decreases from 0.897 to 0.720, indicating a reduced capacity to generate responses that counteract stereotypes. Regarding neutrality, the score declines from 0.804 to 0.668 with increasing chunk size, implying a tendency towards more polarized and less objective responses. For the NO HATE and HATE metrics, the NO HATE score decreases from 0.931 to 0.818, reflecting a reduced effectiveness in generating non-offensive responses, while the HATE score drops from 0.260 to 0.090, which could be interpreted as a partial mitigation of offensive content in responses generated with larger chunks (Figure \ref{fig:/Grafica_Hate}).

As the value of K increases, the stereotype score rises, from 0.933 at K=1 to 0.989 at K=10. In contrast, the anti-stereotype score decreases from 0.886 at K=1 to 0.711 at K=10. Similarly, neutrality declines as K increases, dropping from 0.778 at K=1 to 0.553 at K=10, indicating a shift towards less neutral and more polarized responses. The effectiveness in generating non-offensive responses, as indicated by the NON\_HATE score, also decreases, from 0.949 at K=1 to 0.754 at K=10. Additionally, the HATE score shows an increase from 0.084 at K=1 to 0.218 at K=10, suggesting that as the number of neighbors increases, the model may capture more offensive content present in the data.

"

Based on the comprehensive analysis of embedding an RGA into an LLM, several conclusions can be drawn regarding the optimal parameter configurations to enhance text generation while managing biases, neutrality, and energy efficiency.

1. **Chunk Size and K Value Interaction**: The findings reveal that smaller chunk sizes, when combined with higher K values, lead to decreased similarity and ROUGE metrics, indicating compromised textual cohesion. This is attributed to the introduction of noise from excessive contextual information that overwhelms small data blocks. However, an increased chunk size of 100, paired with a moderate K value of 5, achieves improved similarity scores. This suggests a balanced integration of context that avoids irrelevant information overload.

2. **Temperature Effects**: Temperature significantly influences the quality of generated text. Low temperatures (0.1) stabilize outputs, yielding higher similarity and BLEU scores with lower K values, while providing controlled processing. A medium temperature (0.5) balances accuracy and creativity, although larger configurations exhibit volatility. To optimize specific metrics, such as ROUGE-1 and ROUGE-L, exploring higher temperatures may be beneficial. Notably, excessive randomness at higher temperatures (0.9) degrades quality metrics and increases computational costs and CO2e emissions, emphasizing the need for careful temperature management to balance performance and efficiency.

3. **Ethical and Bias Considerations**: The model displays differential responses to bias and neutrality as a function of chunk size and K value. Larger chunk sizes capture more bias (from 0.925 to 0.972 in stereotype scores) and result in less neutral responses, compared to smaller chunks. Conversely, the ability to generate anti-stereotypical content decreases with larger chunks and higher K values, indicating a reduced capacity for counteracting biases. The results further reveal an increase in stereotype scores and a decrease in anti-stereotype and neutrality scores as K increases, suggesting more polarized responses.

4. **Offensive Content and Neutrality**: The study indicates that while increasing chunk size partially mitigates offensive content (HATE scores decreasing from 0.260 to 0.090), the same trend does not apply to K values, where increased K leads to higher offensive content capture. NON_HATE scores are negatively impacted by both parameters, indicating challenges in maintaining non-offensiveness across varied configurations.

In conclusion, the study underscores the complex interplay between chunk size, K values, and temperature in optimizing text generation. For high-quality output with minimal bias and efficient energy use, configurations with moderate chunk sizes, intermediate K values, and controlled temperatures are recommended. Further research should focus on refining these configurations to minimize biases and offensive content while maintaining or improving computational efficiency.

Acuerdate, es una conclusion, no pongas el uso de viñetas o numeraciones, es para un articulo cientifico de una revista muy importante

The comprehensive analysis of embedding an RGA into an LLM highlights several crucial insights into optimizing text generation while addressing biases, neutrality, and energy efficiency. The study reveals that smaller chunk sizes combined with higher K values lead to decreased similarity and ROUGE metrics, suggesting compromised textual cohesion due to noise from excessive contextual information. In contrast, increasing the chunk size to 100, paired with a moderate K value of 5, improves similarity scores, indicating effective context integration without information overload. Temperature plays a significant role in influencing text quality; specifically, low temperatures stabilize outputs and yield higher similarity and BLEU scores, albeit with controlled processing. Medium temperatures offer a balance between accuracy and creativity, while higher temperatures introduce excessive randomness, degrading quality and increasing computational costs.

Ethical and bias considerations emerge as the model shows increased bias capture and reduced neutrality with larger chunk sizes and higher K values, while also experiencing diminished capacity for generating anti-stereotypical content. The study finds that larger chunk sizes partially mitigate offensive content, though increased K values exacerbate its capture. Maintaining non-offensive responses remains a challenge across varied configurations. Overall, the findings underscore the complex interplay between chunk size, K values, and temperature. To achieve high-quality output with minimal bias and optimal energy efficiency, it is recommended to adopt configurations with moderate chunk sizes, intermediate K values, and controlled temperatures. Further refinement of these configurations is needed to enhance computational efficiency and minimize biases and offensive content.

entwickel diese anwendung weiter. sorge auch dafür das eine geeignete willkommens nachricht mit nützlichen informationen zu beginn erscheint. Darin sollte auch zum beispiel direkt erkennbar sein welche projekte vorhanden sind, und welches das zuletzt verwendete ist, oder welche grund funktionen für den nutzer interessant wären wie die information das einstellungen angepasst werden können und so weiter. Verbessere zudem nocheinmal die formatierungen und das design im chatfenster. Sorge dafür das alles super übersichtlich und klar erkennbar ist. Zum beispiel könnten bestimmte Stichworte, die für weitere aktionen und kommandos an das LLM verwendet werden könnten, sich durch blaue markierung farblich abheben, wie es zb auf webseiten genutzt wird wenn in einem text links mit dem text verbunden werden. Es sollten auch zwei verschiedene llm gewählt und verwendet werden können. eines für die chat konversation während keine bearbeitungen eines projektes aktiv sind und eines für die bearbeitungen eines projektes und erstellung des entwicklungsplans. Es sollte aber immer nur ein llm aktiv sein, damit die belastung des grafikkartenspeichers nur so hoch wie notwendig ist und nur eine llm ausführung darin gespeichert wird. Stelle auch nochmal sicher das das llm den entwicklungsplan nicht zwingend in einer nachricht vollständig erstellen muss, es soll selbtkritisch überdenken ob wirklich alle aufgaben und ziele erstellt wurden und nach erfolgreicher bearbeitung aller aufgaben die erstellung des vollständigen anwendungscodes möglich ist. Jede bearbeitung eines ziels soll auch nur so aufwendig sein das eine bearbeitung in einer einzelnen nachricht ausreicht für eine erfolgreiche bearbeitung. Falls nach der prüfung des resultats einer bearbeitung festgestellt wird das das ziel nicht erfolgreich erreicht wurde, soll natürlich trotzdem diese bearbeitung erneut stattfinden, bis eine erfolgreiche bearbeitung festgestellt werden kann. Erstelle anschließend in das gesamten, überarbeitete skript mit dem vollständigen code der anwendung.


import sys
import threading
import requests
import json
import logging
import subprocess
import os
import re
import shutil
from datetime import datetime, timedelta
from logging.handlers import RotatingFileHandler
from PyQt5.QtWidgets import (
    QApplication, QMainWindow, QLabel, QPushButton, QAction, QHBoxLayout, QVBoxLayout,
    QWidget, QTextEdit, QFileDialog, QTreeWidget, QTreeWidgetItem, QProgressBar,
    QListWidget, QStatusBar, QMessageBox, QInputDialog, QLineEdit, QGroupBox, QFormLayout,
    QDoubleSpinBox, QSpinBox, QStackedWidget, QToolBar, QStyle, QSizePolicy, QComboBox,
    QScrollArea, QFrame, QPlainTextEdit, QMenu, QSpacerItem, QDialog, QDialogButtonBox,
    QCheckBox, QColorDialog, QFontDialog, QSlider, QListView
)
from PyQt5.QtCore import (
    QTimer, Qt, QThread, pyqtSignal, QSize, QPropertyAnimation, QRect,
    QObject, pyqtSlot, QEvent, QPoint, QEasingCurve
)
from PyQt5.QtGui import (
    QColor, QMovie, QIcon, QPixmap, QFont, QClipboard, QPainter, QBrush,
    QPen, QPalette
)

# Logging configuration
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
logger = logging.getLogger()
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
file_handler = RotatingFileHandler(os.path.join(BASE_DIR, "app.log"), maxBytes=5*1024*1024, backupCount=3, encoding='utf-8')
file_handler.setFormatter(formatter)
stream_handler = logging.StreamHandler(sys.stdout)
stream_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.addHandler(stream_handler)

class LLMClient:
    def __init__(self, base_url='http://localhost:11434'):
        self.base_url = base_url.rstrip('/')
        self.cache = {}
        self.memory = {}

    def get_models(self):
        try:
            logging.debug("Requesting available models.")
            response = requests.get(f'{self.base_url}/api/tags', timeout=5)
            if response.status_code == 200:
                models = [model['name'] for model in response.json().get('models', [])]
                logging.debug(f"Available models: {models}")
                return models
            logging.error(f"Error fetching models: {response.status_code} - {response.text}")
        except requests.RequestException as e:
            logging.error(f"Error fetching models: {e}")
        return []

    def generate(self, prompt, temperature=0.7, max_tokens=150):
        cache_key = (prompt, temperature, max_tokens)
        if cache_key in self.cache:
            logging.debug("Using cached result.")
            return self.cache[cache_key]
        payload = {
            "prompt": prompt,
            "temperature": temperature,
            "max_tokens": max_tokens,
            "format": "json",
            "stream": False
        }
        try:
            logging.debug("Sending generation request.")
            response = requests.post(
                f'{self.base_url}/api/generate',
                json=payload,
                headers={"Content-Type": "application/json"},
                timeout=60
            )
            if response.status_code == 200:
                result = response.json().get('response', '').strip()
                self.cache[cache_key] = result
                logging.debug("Generation successful.")
                return result
            logging.error(f"Generation error: {response.status_code} - {response.text}")
            return f"Error: {response.text}"
        except requests.RequestException as e:
            logging.error(f"LLM communication error: {e}")
            return f"Error: {e}"

    def add_to_memory(self, key, value):
        self.memory[key] = value

    def get_from_memory(self, key):
        return self.memory.get(key, '')

class CommandDispatcher:
    def __init__(self, main_window, llm_client):
        self.main_window = main_window
        self.llm_client = llm_client

    def dispatch(self, command):
        action = command.get('action')
        params = command.get('params', {})
        if not action:
            self.main_window.chat_area_add_message("System", "Unrecognized command format.")
            return

        logging.info(f"Dispatching action: {action} with params: {params}")

        actions = {
            'start_server': self.main_window.start_server,
            'stop_server': self.main_window.stop_server,
            'upload_files': self.main_window.upload_files_command,
            'remove_files': self.main_window.remove_selected_files_command,
            'change_setting': self.main_window.change_setting,
            'save_project': self.main_window.save_project_command,
            'load_project': self.main_window.load_project_command,
            'create_new_project': self.main_window.new_project_command,
            'start_analysis': self.main_window.start_analysis_command,
            'start_processing': self.main_window.start_processing_command,
            'pause_processing': self.main_window.pause_processing_command,
            'resume_processing': self.main_window.resume_processing_command,
            'stop_processing': self.main_window.stop_processing_command,
            'toggle_gantt_chart': self.main_window.toggle_gantt_chart_command,
            'generate_final_report': self.main_window.generate_final_report_command
            # Add more actions as needed
        }

        func = actions.get(action)
        if func:
            try:
                func(**params)
                self.main_window.chat_area_add_message("System", f"Action '{action}' executed successfully.")
                logging.info(f"Action '{action}' executed successfully.")
            except Exception as e:
                self.main_window.chat_area_add_message("System", f"Error executing '{action}': {e}")
                logging.error(f"Error executing '{action}': {e}")
        else:
            self.main_window.chat_area_add_message("System", f"Action '{action}' not recognized.")
            logging.warning(f"Action '{action}' not recognized.")

class TaskProcessor(QThread):
    progress_updated = pyqtSignal(int)
    task_message = pyqtSignal(str)
    task_result = pyqtSignal(str)
    task_status = pyqtSignal(int, int, str)

    def __init__(self, tasks, llm_client, llm_model, parent=None):
        super().__init__(parent)
        self.tasks = tasks
        self.llm_client = llm_client
        self.llm_model = llm_model
        self._paused = False
        self._stopped = False
        self.max_retries = 3
        self.context_memory = {}

    def run(self):
        total_goals = sum(len(task.get('Goals', [])) for task in self.tasks)
        completed_goals = 0
        for i, task in enumerate(self.tasks):
            if self._stopped:
                break
            while self._paused:
                self.msleep(100)
            task_title = task.get('Title', f'Task {i+1}')
            self.task_message.emit(f"Assistant: Starting task '{task_title}'")
            self.task_status.emit(i, -1, 'processing')
            for j, goal in enumerate(task.get('Goals', [])):
                if self._stopped:
                    break
                while self._paused:
                    self.msleep(100)
                retry, success = 0, False
                while retry < self.max_retries and not success and not self._stopped:
                    if retry > 0:
                        self.task_message.emit(f"Assistant: Retry {retry} for goal: {goal}")
                        logging.debug(f"Retry {retry} for goal: {goal}")
                    self.task_message.emit(f"Assistant: Processing goal: {goal}")
                    self.task_status.emit(i, j, 'processing')
                    result = self.process_goal(task, goal, retry > 0)
                    success = self.self_review(goal, result)
                    if success:
                        self.task_result.emit(f"Result for '{goal}':\n{result}")
                        self.context_memory[goal] = result
                        self.task_status.emit(i, j, 'completed')
                    else:
                        self.task_status.emit(i, j, 'error')
                        retry += 1
                if not success:
                    self.task_message.emit(f"Assistant: Failed '{goal}' after {self.max_retries} attempts.")
                    self.task_status.emit(i, j, 'failed')
                completed_goals += 1
                self.progress_updated.emit(int((completed_goals / total_goals) * 100))
            self.task_status.emit(i, -1, 'completed')
            summary = self.create_task_summary(task)
            self.task_result.emit(f"Summary for '{task_title}':\n{summary}")
            self.context_memory[task_title] = summary
        if not self._stopped:
            self.task_message.emit("Assistant: All tasks completed.")
            final = self.generate_final_result()
            self.task_result.emit(f"Final Result:\n{final}")

    def process_goal(self, task, goal, retry=False):
        context = self.get_relevant_context(goal)
        prompt = f"""
As an experienced software architect, create a detailed solution for the goal: {goal}

**Description:**
{task.get('ApplicationDescription', '')}

**Context:**
{context}

Provide clear language and code examples where appropriate.
"""
        logging.debug(f"Generating for '{goal}'.")
        return self.llm_client.generate(prompt, temperature=0.7, max_tokens=150)

    def self_review(self, goal, result):
        prompt = f"""
Review the result for "{goal}":

{result}

Is it complete and correct? Answer "Yes" or "No".
"""
        review = self.llm_client.generate(prompt, temperature=0.5, max_tokens=10)
        if "Yes" in review:
            return True
        self.task_message.emit(f"Assistant: Review failed for '{goal}': {review}")
        return False

    def get_relevant_context(self, current_goal):
        return "\n".join(f"**{k}:**\n{v}" for k, v in self.context_memory.items())

    def create_task_summary(self, task):
        return "\n".join(f"{g}:\n{self.context_memory.get(g, '')}" for g in task.get('Goals', []))

    def generate_final_result(self):
        context = "\n".join(self.context_memory.values())
        prompt = f"""
Summarize all results and generate the complete application code with documentation.

**Context:**
{context}
"""
        logging.debug("Generating final result.")
        return self.llm_client.generate(prompt, temperature=0.7, max_tokens=500)

    def pause(self):
        self._paused = True
        logging.debug("TaskProcessor paused.")

    def resume(self):
        self._paused = False
        logging.debug("TaskProcessor resumed.")

    def stop(self):
        self._stopped = True
        logging.debug("TaskProcessor stopped.")

class MessageWidget(QFrame):
    def __init__(self, sender, message, parent=None):
        super().__init__(parent)
        self.sender, self.message = sender, message
        self.init_ui()

    def init_ui(self):
        layout = QVBoxLayout(self)
        self.setFrameStyle(QFrame.StyledPanel | QFrame.Raised)
        self.setLineWidth(0)
        if self.sender.lower() == "assistant":
            color = "#2e2e2e"
        elif self.sender.lower() == "system":
            color = "#555555"
        else:
            color = "#0078d7"
        self.setStyleSheet(f"""
            QFrame {{
                background-color: {color};
                border-radius: 10px;
                padding: 10px;
            }}
        """)
        sender_label = QLabel(f"<b>{self.sender}:</b>")
        sender_label.setStyleSheet("color: #fff;")
        layout.addWidget(sender_label)
        for part in self.parse_message(self.message):
            if part['type'] == 'text':
                lbl = QLabel(part['content'])
                lbl.setStyleSheet("color: #dcdcdc;")
                lbl.setWordWrap(True)
                lbl.setFont(QFont("Arial", 12))
                layout.addWidget(lbl)
            elif part['type'] == 'code':
                code = QPlainTextEdit(part['content'])
                code.setReadOnly(True)
                code.setStyleSheet("""
                    QPlainTextEdit {
                        background-color: #1e1e1e;
                        color: #f8f8f2;
                        border: 1px solid #555;
                        border-radius: 4px;
                        font-family: Consolas, monospace;
                        padding: 5px;
                        font-size: 12pt;
                    }
                """)
                layout.addWidget(code)

    def parse_message(self, msg):
        pattern = re.compile(r'```(.*?)```', re.DOTALL)
        parts, last = [], 0
        for m in pattern.finditer(msg):
            parts.append({'type': 'text', 'content': msg[last:m.start()]})
            parts.append({'type': 'code', 'content': m.group(1).strip()})
            last = m.end()
        if last < len(msg):
            parts.append({'type': 'text', 'content': msg[last:]})
        return parts

class ChatInputWidget(QWidget):
    send_message = pyqtSignal(str)

    def __init__(self, parent=None):
        super().__init__(parent)
        layout = QHBoxLayout(self)
        self.input_field = QTextEdit()
        self.input_field.setFixedHeight(60)
        self.input_field.setStyleSheet("""
            QTextEdit {
                background-color: #2b2b2b;
                color: #dcdcdc;
                border: 1px solid #555;
                border-radius: 4px;
                padding: 5px;
                font-size: 12pt;
            }
        """)
        self.input_field.setPlaceholderText("Geben Sie Ihre Nachricht hier ein...")
        self.send_button = QPushButton("Senden")
        self.send_button.setStyleSheet("""
            QPushButton {
                background-color: #5cb85c;
                color: #fff;
                border: none;
                border-radius: 4px;
                padding: 15px 25px;
                font-size: 12pt;
            }
            QPushButton:hover {
                background-color: #4cae4c;
            }
        """)
        self.send_button.clicked.connect(self.emit_message)
        layout.addWidget(self.input_field)
        layout.addWidget(self.send_button)
        layout.setContentsMargins(0,0,0,0)
        self.setLayout(layout)

    def emit_message(self):
        message = self.input_field.toPlainText().strip()
        if message:
            self.send_message.emit(message)
            self.input_field.clear()

class GanttChartWidget(QWidget):
    def __init__(self, plan_data, parent=None):
        super().__init__(parent)
        self.plan_data = plan_data
        layout = QVBoxLayout(self)
        label = QLabel("Entwicklungsplan Zeitachse")
        label.setStyleSheet("font-size: 16pt; font-weight: bold; color: #fff;")
        layout.addWidget(label)
        try:
            from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
            from matplotlib.figure import Figure
            self.figure = Figure(figsize=(8,6))
            self.canvas = FigureCanvas(self.figure)
            layout.addWidget(self.canvas)
            self.draw_gantt_chart()
        except ImportError:
            logging.error("Matplotlib nicht installiert.")
            error_label = QLabel("Matplotlib ist nicht installiert. Gantt-Diagramm kann nicht angezeigt werden.")
            error_label.setStyleSheet("color: red;")
            layout.addWidget(error_label)
        self.setLayout(layout)

    def update_chart(self, plan_data):
        self.plan_data = plan_data
        self.draw_gantt_chart()

    def draw_gantt_chart(self):
        try:
            from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
            from matplotlib.figure import Figure
            self.figure.clf()
            ax = self.figure.add_subplot(111)
            if not self.plan_data:
                ax.text(0.5, 0.5, 'Keine Daten zum Anzeigen', ha='center', va='center', transform=ax.transAxes, color='white')
            else:
                start_date = datetime.now()
                durations = {task['Title']: max(len(task.get('Goals', [])),1) for task in self.plan_data}
                tasks, dur = list(durations.keys()), list(durations.values())
                starts = [start_date + timedelta(days=sum(dur[:i])) for i in range(len(dur))]
                for i, task in enumerate(tasks):
                    ax.barh(y=i, width=dur[i], left=starts[i], height=0.4, color='#5bc0de')
                ax.set_yticks(range(len(tasks)))
                ax.set_yticklabels(tasks, color='white', fontsize=10)
                ax.xaxis_date()
                ax.set_xlabel('Datum', color='white')
                ax.set_title('Gantt-Diagramm', color='white')
                ax.tick_params(axis='x', colors='white')
                ax.tick_params(axis='y', colors='white')
                ax.set_facecolor('#1e1e1e')
                self.figure.autofmt_xdate()
            self.canvas.draw()
        except Exception as e:
            logging.error(f"Fehler beim Zeichnen des Gantt-Diagramms: {e}")

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle('Intelligenter Assistent für Entwickler')
        self.setGeometry(100, 100, 800, 600)
        self.settings = {
            'available_models': [],
            'selected_model': '',
            'temperature': 0.7,
            'max_tokens': 150,
            'server_url': 'http://localhost',
            'server_port': 11434,
            'theme': 'Dark',
            'font': QFont("Arial", 10)
        }
        self.llm_client = LLMClient(base_url=f"{self.settings['server_url']}:{self.settings['server_port']}")
        self.command_dispatcher = CommandDispatcher(self, self.llm_client)
        self.plan_data, self.uploaded_files = [], []
        self.server_process = None
        self.task_processor = None
        self.init_ui()
        self.apply_styles()
        self.update_server_status()
        self.command_dispatcher = CommandDispatcher(self, self.llm_client)

    def init_ui(self):
        central_widget = QWidget()
        main_layout = QVBoxLayout()
        
        # Chat Area
        self.chat_area = QScrollArea()
        self.chat_area.setStyleSheet("""
            QScrollArea { background-color: #1e1e1e; border: none; }
            QScrollBar:vertical { background-color: #2b2b2b; width: 12px; }
            QScrollBar::handle:vertical { background-color: #555; min-height: 20px; border-radius: 6px; }
            QScrollBar::add-line:vertical, QScrollBar::sub-line:vertical { height: 0px; }
            QScrollBar::up-arrow:vertical, QScrollBar::down-arrow:vertical { background: none; }
        """)
        self.chat_container = QWidget()
        self.chat_layout = QVBoxLayout(self.chat_container)
        self.chat_layout.setAlignment(Qt.AlignTop)
        self.chat_area.setWidgetResizable(True)
        self.chat_area.setWidget(self.chat_container)

        # Loading Animation
        self.loading_label = QLabel()
        self.loading_label.setAlignment(Qt.AlignCenter)
        loading_gif = os.path.join(BASE_DIR, "loading.gif")
        if os.path.exists(loading_gif):
            self.loading_movie = QMovie(loading_gif)
            if self.loading_movie.isValid():
                self.loading_label.setMovie(self.loading_movie)
                self.loading_movie.start()
            else:
                self.loading_label.setText("Loading...")
        else:
            self.loading_label.setText("Loading...")
        self.loading_label.setVisible(False)

        # Chat Input
        self.chat_input = ChatInputWidget()
        self.chat_input.send_message.connect(self.handle_user_message)

        # Progress Bar
        self.progress_bar = QProgressBar()
        self.progress_bar.setValue(0)
        self.progress_bar.setAlignment(Qt.AlignCenter)
        self.progress_bar.setStyleSheet("""
            QProgressBar {
                border: 1px solid #555;
                border-radius: 5px;
                text-align: center;
                height: 20px;
                background-color: #2b2b2b;
                color: #fff;
                font-size: 12pt;
            }
            QProgressBar::chunk { background-color: #5bc0de; width: 20px; }
        """)

        main_layout.addWidget(self.chat_area, stretch=1)
        main_layout.addWidget(self.loading_label)
        main_layout.addWidget(self.progress_bar)
        main_layout.addWidget(self.chat_input)
        main_layout.setStretch(0, 1)
        main_layout.setStretch(1, 0)
        central_widget.setLayout(main_layout)
        self.setCentralWidget(central_widget)

        self.create_menu()

    def create_menu(self):
        menu_bar = self.menuBar()
        file_menu = menu_bar.addMenu('Datei')
        for name, shortcut, action in [('Projekt speichern', 'Ctrl+S', self.save_project_command),
                                       ('Projekt laden', 'Ctrl+O', self.load_project_command),
                                       ('Beenden', 'Ctrl+Q', self.close)]:
            act = QAction(name, self)
            act.setShortcut(shortcut)
            act.triggered.connect(action)
            file_menu.addAction(act)
        edit_menu = menu_bar.addMenu('Bearbeiten')
        for name, shortcut, action in [('Verarbeitung pausieren', 'Ctrl+P', self.pause_processing_command),
                                       ('Verarbeitung fortsetzen', 'Ctrl+R', self.resume_processing_command),
                                       ('Verarbeitung stoppen', 'Ctrl+T', self.stop_processing_command),
                                       ('Einstellungen', 'Ctrl+Shift+S', self.open_settings_dialog_command)]:
            act = QAction(name, self)
            act.setShortcut(shortcut)
            act.triggered.connect(action)
            edit_menu.addAction(act)
        help_menu = menu_bar.addMenu('Hilfe')
        about_action = QAction('Über', self)
        about_action.triggered.connect(self.show_about_dialog)
        help_menu.addAction(about_action)

    def handle_user_message(self, message):
        self.chat_area_add_message("Benutzer", message)
        self.loading_label.setVisible(True)
        threading.Thread(target=self.process_message, args=(message,), daemon=True).start()

    def process_message(self, message):
        # Konstruktion des Prompts zur Anweisung des LLM
        prompt = f"""
Sie sind ein intelligenter Assistent, der Nutzern hilft, ihre Entwicklungsprojekte zu verwalten.
Sie können Aktionen wie start_server, stop_server, upload_files, remove_files, change_setting, 
save_project, load_project, create_new_project, start_analysis, start_processing, 
pause_processing, resume_processing, stop_processing, toggle_gantt_chart, generate_final_report ausführen.

Wenn eine Benutzeranfrage eine Aktion erfordert, antworten Sie mit einem JSON-Objekt:
{{"action": "action_name", "params": {{...}}}}

Andernfalls antworten Sie mit einer hilfreichen Nachricht.

Benutzeranfrage: "{message}"
"""
        response = self.llm_client.generate(prompt, temperature=self.settings['temperature'], max_tokens=self.settings['max_tokens'])
        self.loading_label.setVisible(False)
        try:
            # Versuch, die Antwort als JSON zu parsen
            command = json.loads(response)
            if isinstance(command, dict) and 'action' in command:
                self.command_dispatcher.dispatch(command)
            else:
                # Wenn keine Aktion, als normale Nachricht behandeln
                self.chat_area_add_message("Assistent", response)
        except json.JSONDecodeError:
            # Falls Parsing fehlschlägt, als normale Nachricht behandeln
            self.chat_area_add_message("Assistent", response)

    def chat_area_add_message(self, sender, message):
        msg = MessageWidget(sender, message)
        self.chat_layout.addWidget(msg)
        QTimer.singleShot(100, lambda: self.chat_area.verticalScrollBar().setValue(
            self.chat_area.verticalScrollBar().maximum()))

    def start_server_command(self, **kwargs):
        self.start_server()

    def stop_server_command(self, **kwargs):
        self.stop_server()

    def upload_files_command(self, **kwargs):
        # Erwartet 'files' Parameter als Liste von Dateipfaden
        files = kwargs.get('files', [])
        if files:
            self.uploaded_files.extend(files)
            self.display_uploaded_files()
            self.chat_area_add_message("System", f"{len(files)} Datei(en) über Befehl hochgeladen.")
            logging.debug(f"Hochgeladene Dateien über Befehl: {files}")
        else:
            self.chat_area_add_message("System", "Keine Dateien für den Upload bereitgestellt.")

    def remove_selected_files_command(self, **kwargs):
        # Erwartet 'files' Parameter als Liste von zu entfernenden Dateipfaden
        files = kwargs.get('files', [])
        if files:
            removed = [f for f in files if f in self.uploaded_files]
            for f in removed:
                self.uploaded_files.remove(f)
            self.display_uploaded_files()
            self.chat_area_add_message("System", f"{len(removed)} Datei(en) über Befehl entfernt.")
            logging.debug(f"Entfernte Dateien über Befehl: {removed}")
        else:
            self.chat_area_add_message("System", "Keine Dateien für die Entfernung bereitgestellt.")

    def change_setting(self, **kwargs):
        # Erwartet 'setting' und 'value' Parameter
        setting = kwargs.get('setting')
        value = kwargs.get('value')
        if setting and value is not None:
            if setting in self.settings:
                self.settings[setting] = value
                if setting == 'selected_model':
                    self.llm_client.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                               f":{self.settings['server_port']}"
                self.apply_styles()
                self.chat_area_add_message("System", f"Einstellung '{setting}' auf '{value}' geändert.")
                logging.debug(f"Einstellung '{setting}' auf '{value}' geändert.")
            else:
                self.chat_area_add_message("System", f"Einstellung '{setting}' existiert nicht.")
                logging.warning(f"Versuch, nicht vorhandene Einstellung '{setting}' zu ändern.")
        else:
            self.chat_area_add_message("System", "Ungültige Parameter zum Ändern der Einstellungen.")

    def save_project_command(self, **kwargs):
        self.save_project()

    def load_project_command(self, **kwargs):
        self.load_project()

    def new_project_command(self, **kwargs):
        self.new_project()

    def start_analysis_command(self, **kwargs):
        self.start_analysis_command_handler(**kwargs)

    def start_processing_command(self, **kwargs):
        self.start_processing()

    def pause_processing_command(self, **kwargs):
        self.pause_processing()

    def resume_processing_command(self, **kwargs):
        self.resume_processing()

    def stop_processing_command(self, **kwargs):
        self.stop_processing()

    def toggle_gantt_chart_command(self, **kwargs):
        self.toggle_gantt_chart()

    def generate_final_report_command(self, **kwargs):
        self.generate_final_report()

    def apply_styles(self):
        palette = QPalette()
        if self.settings['theme'] == 'Dark':
            palette.setColor(QPalette.Window, QColor(45,45,45))
            palette.setColor(QPalette.WindowText, Qt.white)
            palette.setColor(QPalette.Base, QColor(30,30,30))
            palette.setColor(QPalette.AlternateBase, QColor(45,45,45))
            palette.setColor(QPalette.ToolTipBase, Qt.white)
            palette.setColor(QPalette.ToolTipText, Qt.white)
            palette.setColor(QPalette.Text, Qt.white)
            palette.setColor(QPalette.Button, QColor(45,45,45))
            palette.setColor(QPalette.ButtonText, Qt.white)
            palette.setColor(QPalette.BrightText, Qt.red)
            palette.setColor(QPalette.Link, QColor(42,130,218))
            palette.setColor(QPalette.Highlight, QColor(42,130,218))
            palette.setColor(QPalette.HighlightedText, Qt.black)
        else:
            palette = QApplication.style().standardPalette()
        self.setPalette(palette)
        self.setFont(self.settings.get('font', QFont("Arial", 10)))
        for widget in [self.chat_input.input_field]:
            widget.setFont(self.settings.get('font', QFont("Arial", 10)))

    def update_llm_list(self):
        models = self.llm_client.get_models()
        self.settings['available_models'] = models
        self.settings['selected_model'] = models[0] if models else ''
        if models:
            logging.debug(f"Aktuelle Modelle aktualisiert: {models}")
            self.chat_area_add_message("System", f"Verfügbare Modelle: {', '.join(models)}. Gewähltes Modell: {self.settings['selected_model']}.")
        else:
            logging.warning("Keine Modelle gefunden.")
            self.chat_area_add_message("System", "Keine Modelle auf dem LLM-Server gefunden.")

    def update_server_status(self):
        running = self.check_server_running()
        if running:
            self.chat_area_add_message("System", "Serverstatus: 🟢 Läuft")
            self.update_llm_list()
        else:
            self.chat_area_add_message("System", "Serverstatus: 🔴 Gestoppt")
            self.settings['available_models'] = []
            self.settings['selected_model'] = ''

    def check_server_running(self):
        try:
            response = requests.get(f'{self.llm_client.base_url}/api/tags', timeout=5)
            return response.status_code == 200
        except requests.RequestException:
            return False

    def start_server(self):
        if self.check_server_running():
            self.chat_area_add_message("System", "Server läuft bereits.")
            logging.info("Serverstart versucht, aber bereits aktiv.")
            return
        try:
            llm_cmd = 'ollama'
            server_path = shutil.which(llm_cmd)
            if not server_path:
                self.chat_area_add_message("System", f"Befehl '{llm_cmd}' nicht gefunden.")
                logging.error(f"Befehl '{llm_cmd}' nicht gefunden.")
                return
            args = [llm_cmd, 'serve', '--port', str(self.settings['server_port'])]
            creation = subprocess.CREATE_NEW_CONSOLE if os.name == 'nt' else 0
            self.server_process = subprocess.Popen(
                args,
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE,
                shell=(os.name=='nt'),
                creationflags=creation
            )
            self.chat_area_add_message("System", "LLM-Server wird gestartet...")
            self.server_timer = QTimer()
            self.server_timer.timeout.connect(self.check_server_startup)
            self.server_timer.start(1000)
            self.server_startup_attempts = 0
            logging.debug("LLM-Serverstart initiiert.")
        except Exception as e:
            self.chat_area_add_message("System", f"Serverstart fehlgeschlagen: {e}")
            logging.error(f"Fehler beim Serverstart: {e}")

    def check_server_startup(self):
        self.server_startup_attempts +=1
        if self.check_server_running():
            self.server_timer.stop()
            self.server_timer = None
            self.update_server_status()
            self.chat_area_add_message("System", "LLM-Server gestartet.")
            logging.debug("LLM-Server erfolgreich gestartet.")
        elif self.server_startup_attempts >=15:
            self.server_timer.stop()
            self.server_timer = None
            self.chat_area_add_message("System", "Server konnte nicht rechtzeitig gestartet werden.")
            QMessageBox.warning(self, "Warnung", "Server wurde nicht innerhalb der Zeit gestartet.")
            self.update_server_status()
            logging.error("LLM-Server konnte nicht innerhalb von 15 Sekunden gestartet werden.")

    def stop_server(self):
        if self.server_process and self.server_process.poll() is None:
            try:
                self.server_process.terminate()
                self.server_process.wait(timeout=10)
                self.server_process = None
                self.chat_area_add_message("System", "LLM-Server gestoppt.")
                logging.debug("LLM-Server gestoppt.")
                self.update_server_status()
            except subprocess.TimeoutExpired:
                self.server_process.kill()
                self.server_process = None
                self.chat_area_add_message("System", "LLM-Server wurde zwangsweise gestoppt.")
                self.update_server_status()
                logging.error("LLM-Server wurde zwangsweise gestoppt.")
            except Exception as e:
                self.chat_area_add_message("System", f"Server konnte nicht gestoppt werden: {e}")
                logging.error(f"Fehler beim Serverstopp: {e}")
        else:
            self.chat_area_add_message("System", "Server läuft nicht.")
            logging.info("Serverstopp versucht, aber Server läuft nicht.")

    def upload_files(self):
        files, _ = QFileDialog.getOpenFileNames(self, "Dateien auswählen", "", "Alle Dateien (*)")
        new = [f for f in files if f not in self.uploaded_files]
        if new:
            self.uploaded_files.extend(new)
            logging.debug(f"Hochgeladene Dateien: {new}")
            self.chat_area_add_message("System", f"{len(new)} Datei(en) hochgeladen.")
        else:
            self.chat_area_add_message("System", "Keine neuen Dateien hochgeladen (Duplikate übersprungen).")

    def upload_files_command(self, **kwargs):
        # Dateien über Befehl hochladen mit 'files' Parameter
        files = kwargs.get('files', [])
        if not files:
            self.chat_area_add_message("System", "Keine Dateien zum Hochladen bereitgestellt.")
            return
        # Sicherheitsüberprüfung der Dateipfade
        accessible_files = [f for f in files if os.path.isfile(f)]
        if accessible_files:
            self.uploaded_files.extend(accessible_files)
            logging.debug(f"Hochgeladene Dateien über Befehl: {accessible_files}")
            self.chat_area_add_message("System", f"{len(accessible_files)} Datei(en) über Befehl hochgeladen.")
        else:
            self.chat_area_add_message("System", "Keine gültigen Dateien zum Hochladen bereitgestellt.")

    def display_uploaded_files(self):
        # Informationen über hochgeladene Dateien über Chat anzeigen
        self.chat_area_add_message("System", f"Hochgeladene Dateien:\n" + "\n".join(self.uploaded_files))

    def change_setting(self, **kwargs):
        # Bereits in CommandDispatcher behandelt
        pass

    def save_project(self):
        # Projekt über Dialog speichern
        file, _ = QFileDialog.getSaveFileName(self, "Projekt speichern", "", "JSON Dateien (*.json)")
        if file:
            project = {
                'description': getattr(self, 'description_text', ''),
                'uploaded_files': self.uploaded_files,
                'plan_data': self.plan_data,
                'settings': {
                    'selected_model': self.settings.get('selected_model', ''),
                    'temperature': self.settings.get('temperature', 0.7),
                    'max_tokens': self.settings.get('max_tokens', 150),
                    'server_url': self.settings.get('server_url', 'http://localhost'),
                    'server_port': self.settings.get('server_port', 11434),
                    'theme': self.settings.get('theme', 'Dark'),
                    'font': self.font_to_dict(self.settings.get('font', QFont("Arial", 10)))
                }
            }
            try:
                with open(file, 'w', encoding='utf-8') as f:
                    json.dump(project, f, ensure_ascii=False, indent=4)
                self.chat_area_add_message("System", "Projekt erfolgreich gespeichert.")
                logging.debug(f"Projekt gespeichert unter {file}.")
            except Exception as e:
                self.chat_area_add_message("System", f"Projekt konnte nicht gespeichert werden: {e}")
                logging.error(f"Fehler beim Speichern des Projekts: {e}")

    def save_project_command(self, **kwargs):
        self.save_project()

    def load_project(self):
        # Projekt über Dialog laden
        file, _ = QFileDialog.getOpenFileName(self, "Projekt laden", "", "JSON Dateien (*.json)")
        if file:
            try:
                with open(file, 'r', encoding='utf-8') as f:
                    project = json.load(f)
                # Annahme: 'description' ist Teil des Projekts
                load_description = QMessageBox.question(self, "Beschreibung laden", "Möchten Sie die Projektbeschreibung laden?", QMessageBox.Yes | QMessageBox.No)
                if load_description == QMessageBox.Yes:
                    description = project.get('description', '')
                    self.chat_area_add_message("System", f"Geladene Beschreibung:\n{description}")
                self.uploaded_files = project.get('uploaded_files', [])
                if self.uploaded_files:
                    self.chat_area_add_message("System", f"Hochgeladene Dateien:\n" + "\n".join(self.uploaded_files))
                self.plan_data = project.get('plan_data', [])
                settings = project.get('settings', {})
                if settings:
                    self.settings.update({
                        'selected_model': settings.get('selected_model', ''),
                        'temperature': settings.get('temperature', 0.7),
                        'max_tokens': settings.get('max_tokens', 150),
                        'server_url': settings.get('server_url', 'http://localhost'),
                        'server_port': settings.get('server_port', 11434),
                        'theme': settings.get('theme', 'Dark'),
                        'font': self.dict_to_font(settings.get('font', {}))
                    })
                    self.llm_client.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                               f":{self.settings['server_port']}"
                    self.apply_styles()
                if self.plan_data:
                    self.chat_area_add_message("System", "Entwicklungsplan geladen.")
                    logging.debug("Entwicklungsplan geladen.")
                QMessageBox.information(self, "Erfolg", "Projekt erfolgreich geladen.")
                logging.debug(f"Projekt geladen von {file}.")
            except Exception as e:
                self.chat_area_add_message("System", f"Projekt konnte nicht geladen werden: {e}")
                logging.error(f"Fehler beim Laden des Projekts: {e}")

    def load_project_command(self, **kwargs):
        self.load_project()

    def new_project(self):
        self.uploaded_files = []
        self.plan_data = []
        self.chat_area_add_message("System", "Neues Projekt gestartet.")
        logging.debug("Neues Projekt initiiert.")

    def new_project_command(self, **kwargs):
        self.new_project()

    def start_analysis_command_handler(self, **kwargs):
        # Startet die Analyse basierend auf bereitgestellter Beschreibung und Dateien
        description = kwargs.get('description')
        if not description:
            self.chat_area_add_message("System", "Keine Beschreibung für die Analyse bereitgestellt.")
            return
        self.task_processor = TaskProcessor(self.plan_data, self.llm_client, self.settings['selected_model'])
        self.task_processor.progress_updated.connect(self.update_progress)
        self.task_processor.task_message.connect(self.chat_area_add_message)
        self.task_processor.task_result.connect(self.chat_area_add_message)
        self.task_processor.task_status.connect(self.update_task_status)
        self.task_processor.finished.connect(self.processing_finished)
        self.task_processor.start()
        self.chat_area_add_message("System", "Analyse des Projekts gestartet.")
        logging.debug("TaskProcessor für Analyse gestartet.")

    def start_analysis_command(self, **kwargs):
        # Diese Funktion sammelt typischerweise die Beschreibung und startet die Analyse
        QMessageBox.information(self, "Info", "Analyse kann über Chat-Befehle gestartet werden.")
        logging.info("Analyse über Befehl angefragt.")

    def start_processing_command(self, **kwargs):
        self.start_processing()

    def start_processing(self):
        if not self.plan_data:
            self.chat_area_add_message("System", "Kein Entwicklungsplan verfügbar. Bitte zuerst Analyse starten.")
            logging.warning("Verarbeitung ohne Entwicklungsplan gestartet.")
            return
        if not self.settings.get('selected_model'):
            self.chat_area_add_message("System", "Kein LLM ausgewählt. Bitte Modell über Einstellungen wählen.")
            logging.warning("Verarbeitung ohne LLM-Auswahl gestartet.")
            return
        self.chat_area_add_message("System", "Verarbeitung der Aufgaben gestartet...")
        self.task_processor = TaskProcessor(self.plan_data, self.llm_client, self.settings['selected_model'])
        self.task_processor.progress_updated.connect(self.update_progress)
        self.task_processor.task_message.connect(self.chat_area_add_message)
        self.task_processor.task_result.connect(self.chat_area_add_message)
        self.task_processor.task_status.connect(self.update_task_status)
        self.task_processor.finished.connect(self.processing_finished)
        self.task_processor.start()
        logging.debug("Task-Verarbeitung gestartet.")

    def pause_processing_command(self, **kwargs):
        self.pause_processing()

    def pause_processing(self):
        if hasattr(self, 'task_processor') and self.task_processor.isRunning():
            self.task_processor.pause()
            self.chat_area_add_message("System", "Verarbeitung pausiert.")
            logging.debug("Verarbeitung pausiert.")
        else:
            self.chat_area_add_message("System", "Keine laufende Verarbeitung zum Pausieren.")
            logging.info("Pause-Verarbeitung ohne aktive Prozesse angefragt.")

    def resume_processing_command(self, **kwargs):
        self.resume_processing()

    def resume_processing(self):
        if hasattr(self, 'task_processor') and self.task_processor.isRunning():
            self.task_processor.resume()
            self.chat_area_add_message("System", "Verarbeitung fortgesetzt.")
            logging.debug("Verarbeitung fortgesetzt.")
        else:
            self.chat_area_add_message("System", "Keine pausierte Verarbeitung zum Fortsetzen.")
            logging.info("Fortsetzen-Verarbeitung ohne pausierte Prozesse angefragt.")

    def stop_processing_command(self, **kwargs):
        self.stop_processing()

    def stop_processing(self):
        if hasattr(self, 'task_processor') and self.task_processor.isRunning():
            self.task_processor.stop()
            self.task_processor.wait()
            self.chat_area_add_message("System", "Verarbeitung gestoppt.")
            self.progress_bar.setValue(0)
            self.loading_label.setVisible(False)
            logging.debug("Verarbeitung gestoppt.")
        else:
            self.chat_area_add_message("System", "Keine laufende Verarbeitung zum Stoppen.")
            logging.info("Stop-Verarbeitung ohne aktive Prozesse angefragt.")

    def toggle_gantt_chart_command(self, **kwargs):
        self.toggle_gantt_chart()

    def toggle_gantt_chart(self):
        # Da die GUI minimiert ist, informieren Sie den Benutzer über den Chat
        self.chat_area_add_message("System", "Gantt-Diagramm wird nicht direkt in der GUI angezeigt.")
        logging.info("Gantt-Diagramm-Toggle über Befehl angefragt.")

    def generate_final_report_command(self, **kwargs):
        self.generate_final_report()

    def generate_final_report(self):
        if hasattr(self, 'task_processor') and hasattr(self.task_processor, 'generate_final_result'):
            final = self.task_processor.generate_final_result()
            self.chat_area_add_message("Assistent", final)
        else:
            self.chat_area_add_message("System", "Kein Abschlussbericht verfügbar. Bitte zuerst Verarbeitung starten.")
            logging.warning("Abschlussbericht ohne aktiven TaskProcessor angefragt.")

    def open_settings_dialog_command(self, **kwargs):
        self.open_settings_dialog()

    def open_settings_dialog_command(self, **kwargs):
        dlg = SettingsDialog(self.settings, self)
        if dlg.exec_() == QDialog.Accepted:
            self.settings = dlg.settings
            self.apply_styles()
            # Aktualisiere LLMClient base_url korrekt mit server_port
            self.llm_client.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                       f":{self.settings['server_port']}"
            self.update_server_status()
            self.chat_area_add_message("System", "Einstellungen erfolgreich aktualisiert.")
            self.update_llm_list()

    def show_about_dialog(self):
        QMessageBox.about(self, "Über", "Intelligenter Assistent für Entwickler\nVersion 2.6\n\nEntwickelt, um Entwicklern bei der Erstellung umfassender Entwicklungspläne und Skripte mit Hilfe von LLMs zu unterstützen.")

    def closeEvent(self, event):
        reply = QMessageBox.question(self, 'Bestätigen', 'Möchten Sie die Anwendung wirklich beenden?', QMessageBox.Yes | QMessageBox.No, QMessageBox.No)
        if reply == QMessageBox.Yes:
            if self.server_process and self.server_process.poll() is None:
                try:
                    self.server_process.terminate()
                    self.server_process.wait(timeout=10)
                    logging.debug("Server beim Beenden beendet.")
                except subprocess.TimeoutExpired:
                    self.server_process.kill()
                    logging.error("Server wurde beim Beenden zwangsweise beendet.")
            if hasattr(self, 'task_processor') and self.task_processor.isRunning():
                self.task_processor.stop()
                self.task_processor.wait()
                logging.debug("TaskProcessor beim Beenden beendet.")
            event.accept()
        else:
            event.ignore()

    def font_to_dict(self, font):
        return {
            'family': font.family(),
            'pointSize': font.pointSize(),
            'bold': font.bold(),
            'italic': font.italic(),
            'underline': font.underline(),
            'strikeOut': font.strikeOut(),
            'weight': font.weight(),
            'style': font.style(),
            'styleHint': font.styleHint()
        }

    def dict_to_font(self, d):
        font = QFont()
        font.setFamily(d.get('family', "Arial"))
        font.setPointSize(d.get('pointSize', 10))
        font.setBold(d.get('bold', False))
        font.setItalic(d.get('italic', False))
        font.setUnderline(d.get('underline', False))
        font.setStrikeOut(d.get('strikeOut', False))
        font.setWeight(d.get('weight', QFont.Normal))
        font.setStyle(d.get('style', QFont.StyleNormal))
        font.setStyleHint(d.get('styleHint', QFont.AnyStyle))
        return font

    def start_analysis(self):
        # Placeholder für zukünftige Implementierungen
        QMessageBox.information(self, "Info", "Analyse kann über Chatbefehle gestartet werden.")
        logging.info("Analyse über Befehl initiiert.")

    def start_analysis_command_handler(self, **kwargs):
        # Startet die Analyse basierend auf bereitgestellter Beschreibung und Dateien
        description = kwargs.get('description')
        if not description:
            self.chat_area_add_message("System", "Keine Beschreibung für die Analyse bereitgestellt.")
            return
        self.task_processor = TaskProcessor(self.plan_data, self.llm_client, self.settings['selected_model'])
        self.task_processor.progress_updated.connect(self.update_progress)
        self.task_processor.task_message.connect(self.chat_area_add_message)
        self.task_processor.task_result.connect(self.chat_area_add_message)
        self.task_processor.task_status.connect(self.update_task_status)
        self.task_processor.finished.connect(self.processing_finished)
        self.task_processor.start()
        self.chat_area_add_message("System", "Analyse des Projekts gestartet.")
        logging.debug("TaskProcessor für Analyse gestartet.")

    def update_progress(self, value):
        self.progress_bar.setValue(value)

    def update_task_status(self, task_index, goal_index, status):
        # Hier kann man den Status der einzelnen Aufgaben aktualisieren, z.B. in einem internen Tracking
        pass  # Placeholder für zukünftige Implementierungen

    def processing_finished(self):
        self.progress_bar.setValue(100)
        self.chat_area_add_message("System", "Verarbeitung abgeschlossen.")
        logging.debug("Verarbeitung abgeschlossen.")

    def generate_final_report(self):
        if hasattr(self, 'task_processor') and hasattr(self.task_processor, 'generate_final_result'):
            final = self.task_processor.generate_final_result()
            self.chat_area_add_message("Assistent", final)
        else:
            self.chat_area_add_message("System", "Kein Abschlussbericht verfügbar. Bitte zuerst Verarbeitung starten.")
            logging.warning("Abschlussbericht ohne aktiven TaskProcessor angefragt.")

    def open_settings_dialog(self):
        dlg = SettingsDialog(self.settings, self)
        if dlg.exec_() == QDialog.Accepted:
            self.settings = dlg.settings
            self.apply_styles()
            # Aktualisiere LLMClient base_url korrekt mit server_port
            self.llm_client.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                       f":{self.settings['server_port']}"
            self.update_server_status()
            self.chat_area_add_message("System", "Einstellungen erfolgreich aktualisiert.")
            self.update_llm_list()

class SettingsDialog(QDialog):
    def __init__(self, current_settings, parent=None):
        super().__init__(parent)
        self.setWindowTitle("Einstellungen")
        self.setModal(True)
        self.settings = current_settings.copy()
        self.init_ui()

    def init_ui(self):
        layout = QVBoxLayout(self)
        tabs_layout = QVBoxLayout()

        # LLM Settings
        llm_group = QGroupBox("LLM-Einstellungen")
        llm_layout = QFormLayout()
        self.llm_combo = QComboBox()
        self.llm_combo.addItems(self.settings.get('available_models', []))
        self.llm_combo.setCurrentText(self.settings.get('selected_model', ''))
        self.temperature_spin = QDoubleSpinBox()
        self.temperature_spin.setRange(0.0,1.0)
        self.temperature_spin.setDecimals(2)
        self.temperature_spin.setSingleStep(0.05)
        self.temperature_spin.setValue(self.settings.get('temperature', 0.7))
        self.max_tokens_spin = QSpinBox()
        self.max_tokens_spin.setRange(100, 10000)
        self.max_tokens_spin.setValue(self.settings.get('max_tokens', 150))
        llm_layout.addRow("Modell:", self.llm_combo)
        llm_layout.addRow("Temperatur:", self.temperature_spin)
        llm_layout.addRow("Max Tokens:", self.max_tokens_spin)
        llm_group.setLayout(llm_layout)
        tabs_layout.addWidget(llm_group)

        # Server Settings
        server_group = QGroupBox("Server-Einstellungen")
        server_layout = QFormLayout()
        self.server_url_input = QLineEdit(self.settings.get('server_url', 'http://localhost'))
        self.server_port_input = QSpinBox()
        self.server_port_input.setRange(1,65535)
        self.server_port_input.setValue(self.settings.get('server_port', 11434))
        server_layout.addRow("Server-URL:", self.server_url_input)
        server_layout.addRow("Server-Port:", self.server_port_input)
        server_group.setLayout(server_layout)
        tabs_layout.addWidget(server_group)

        # UI Settings
        ui_group = QGroupBox("UI-Einstellungen")
        ui_layout = QFormLayout()
        self.theme_combo = QComboBox()
        self.theme_combo.addItems(["Dark", "Light"])
        self.theme_combo.setCurrentText(self.settings.get('theme', 'Dark'))
        self.font_button = QPushButton("Schriftart wählen")
        self.font_button.clicked.connect(self.choose_font)
        self.selected_font = self.settings.get('font', QFont("Arial", 10))
        status = 'Fett' if self.selected_font.bold() else 'Regulär'
        self.font_display = QLabel(f"{self.selected_font.family()}, {self.selected_font.pointSize()}pt, {status}")
        ui_layout.addRow("Thema:", self.theme_combo)
        ui_layout.addRow("Schriftart:", self.font_button)
        ui_layout.addRow("", self.font_display)
        ui_group.setLayout(ui_layout)
        tabs_layout.addWidget(ui_group)

        layout.addLayout(tabs_layout)
        buttons = QDialogButtonBox(QDialogButtonBox.Save | QDialogButtonBox.Cancel)
        buttons.accepted.connect(self.save)
        buttons.rejected.connect(self.reject)
        layout.addWidget(buttons)
        self.setLayout(layout)

    def choose_font(self):
        font, ok = QFontDialog.getFont(self.selected_font, self, "Schriftart wählen")
        if ok:
            self.selected_font = font
            status = 'Fett' if font.bold() else 'Regulär'
            self.font_display.setText(f"{font.family()}, {font.pointSize()}pt, {status}")

    def save(self):
        self.settings.update({
            'selected_model': self.llm_combo.currentText(),
            'temperature': self.temperature_spin.value(),
            'max_tokens': self.max_tokens_spin.value(),
            'server_url': self.server_url_input.text(),
            'server_port': self.server_port_input.value(),
            'theme': self.theme_combo.currentText(),
            'font': self.selected_font
        })
        self.accept()

def main():
    app = QApplication(sys.argv)
    dark_palette = QPalette()
    dark_palette.setColor(QPalette.Window, QColor(45,45,45))
    dark_palette.setColor(QPalette.WindowText, Qt.white)
    dark_palette.setColor(QPalette.Base, QColor(30,30,30))
    dark_palette.setColor(QPalette.AlternateBase, QColor(45,45,45))
    dark_palette.setColor(QPalette.ToolTipBase, Qt.white)
    dark_palette.setColor(QPalette.ToolTipText, Qt.white)
    dark_palette.setColor(QPalette.Text, Qt.white)
    dark_palette.setColor(QPalette.Button, QColor(45,45,45))
    dark_palette.setColor(QPalette.ButtonText, Qt.white)
    dark_palette.setColor(QPalette.BrightText, Qt.red)
    dark_palette.setColor(QPalette.Link, QColor(42,130,218))
    dark_palette.setColor(QPalette.Highlight, QColor(42,130,218))
    dark_palette.setColor(QPalette.HighlightedText, Qt.black)
    app.setPalette(dark_palette)
    app.setStyle("Fusion")
    window = MainWindow()
    window.show()
    logging.debug("Anwendung gestartet.")
    sys.exit(app.exec_())

if __name__ == '__main__':
    main()

Natürlich! Ich habe die von Ihnen vorgeschlagenen Verbesserungen in Ihrem bestehenden PyQt5-basierten Anwendungsskript umgesetzt. Hier sind die wichtigsten Änderungen und Erweiterungen:

1. **Willkommensnachricht mit nützlichen Informationen:**
    - Beim Start der Anwendung erscheint nun eine Willkommensnachricht, die eine Übersicht über verfügbare Projekte, das zuletzt verwendete Projekt sowie grundlegende Funktionen enthält.
    - Die Projekte werden aus einer JSON-Datei geladen, und das zuletzt verwendete Projekt wird hervorgehoben.

2. **Verbesserte Formatierung und Design im Chatfenster:**
    - Bestimmte Schlüsselwörter und Befehle im Chat werden nun blau hervorgehoben, ähnlich wie Links auf Webseiten, um sie besser erkennbar zu machen.
    - Der Chatbereich wurde insgesamt übersichtlicher gestaltet, mit klaren Unterscheidungen zwischen Benutzer-, System- und Assistentennachrichten.

3. **Unterstützung für zwei verschiedene LLMs:**
    - Es können nun zwei verschiedene LLMs ausgewählt werden: eines für allgemeine Chat-Konversationen und ein anderes für die Bearbeitung von Projekten und die Erstellung von Entwicklungsplänen.
    - Es wird stets nur ein LLM gleichzeitig genutzt, um die Belastung des Grafikkartenspeichers zu minimieren.
    - Die Auswahl des aktiven LLMs kann in den Einstellungen vorgenommen werden.

4. **Verbesserungen im TaskProcessor:**
    - Der `TaskProcessor` überprüft nun kritisch, ob alle Ziele und Aufgaben erfolgreich abgeschlossen wurden, bevor der vollständige Anwendungscode generiert wird.
    - Jede Bearbeitung eines Ziels ist so konzipiert, dass sie in einer einzelnen Nachricht abgeschlossen werden kann. Bei fehlgeschlagenen Bearbeitungen wird die Aufgabe automatisch erneut versucht, bis sie erfolgreich ist.

5. **Weitere Optimierungen:**
    - Verbesserte Fehlerbehandlung und Logging für eine robustere Anwendung.
    - Erweiterte Einstellungen ermöglichen eine detaillierte Anpassung der Anwendung nach Bedarf.

Nachfolgend finden Sie den vollständigen, überarbeiteten Code der Anwendung mit den genannten Verbesserungen:

```python
import sys
import threading
import requests
import json
import logging
import subprocess
import os
import re
import shutil
from datetime import datetime, timedelta
from logging.handlers import RotatingFileHandler
from PyQt5.QtWidgets import (
    QApplication, QMainWindow, QLabel, QPushButton, QAction, QHBoxLayout, QVBoxLayout,
    QWidget, QTextEdit, QFileDialog, QTreeWidget, QTreeWidgetItem, QProgressBar,
    QListWidget, QStatusBar, QMessageBox, QInputDialog, QLineEdit, QGroupBox, QFormLayout,
    QDoubleSpinBox, QSpinBox, QStackedWidget, QToolBar, QStyle, QSizePolicy, QComboBox,
    QScrollArea, QFrame, QPlainTextEdit, QMenu, QSpacerItem, QDialog, QDialogButtonBox,
    QCheckBox, QColorDialog, QFontDialog, QSlider, QListView
)
from PyQt5.QtCore import (
    QTimer, Qt, QThread, pyqtSignal, QSize, QPropertyAnimation, QRect,
    QObject, pyqtSlot, QEvent, QPoint, QEasingCurve
)
from PyQt5.QtGui import (
    QColor, QMovie, QIcon, QPixmap, QFont, QClipboard, QPainter, QBrush,
    QPen, QPalette, QTextCursor, QTextCharFormat, QSyntaxOption
)

# Logging configuration
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
logger = logging.getLogger()
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
file_handler = RotatingFileHandler(os.path.join(BASE_DIR, "app.log"), maxBytes=5*1024*1024, backupCount=3, encoding='utf-8')
file_handler.setFormatter(formatter)
stream_handler = logging.StreamHandler(sys.stdout)
stream_handler.setFormatter(formatter)
logger.addHandler(file_handler)
logger.addHandler(stream_handler)

class LLMClient:
    def __init__(self, base_url='http://localhost:11434', model_name='default'):
        self.base_url = base_url.rstrip('/')
        self.model_name = model_name
        self.cache = {}
        self.memory = {}
    
    def set_model(self, model_name):
        self.model_name = model_name
        logging.debug(f"LLM-Modell gewechselt zu: {self.model_name}")
    
    def get_models(self):
        try:
            logging.debug("Requesting available models.")
            response = requests.get(f'{self.base_url}/api/tags', timeout=5)
            if response.status_code == 200:
                models = [model['name'] for model in response.json().get('models', [])]
                logging.debug(f"Available models: {models}")
                return models
            logging.error(f"Error fetching models: {response.status_code} - {response.text}")
        except requests.RequestException as e:
            logging.error(f"Error fetching models: {e}")
        return []
    
    def generate(self, prompt, temperature=0.7, max_tokens=150):
        cache_key = (self.model_name, prompt, temperature, max_tokens)
        if cache_key in self.cache:
            logging.debug("Using cached result.")
            return self.cache[cache_key]
        payload = {
            "model": self.model_name,
            "prompt": prompt,
            "temperature": temperature,
            "max_tokens": max_tokens,
            "format": "json",
            "stream": False
        }
        try:
            logging.debug("Sending generation request.")
            response = requests.post(
                f'{self.base_url}/api/generate',
                json=payload,
                headers={"Content-Type": "application/json"},
                timeout=60
            )
            if response.status_code == 200:
                result = response.json().get('response', '').strip()
                self.cache[cache_key] = result
                logging.debug("Generation successful.")
                return result
            logging.error(f"Generation error: {response.status_code} - {response.text}")
            return f"Error: {response.text}"
        except requests.RequestException as e:
            logging.error(f"LLM communication error: {e}")
            return f"Error: {e}"
    
    def add_to_memory(self, key, value):
        self.memory[key] = value
    
    def get_from_memory(self, key):
        return self.memory.get(key, '')

class CommandDispatcher:
    def __init__(self, main_window, llm_client):
        self.main_window = main_window
        self.llm_client = llm_client
    
    def dispatch(self, command):
        action = command.get('action')
        params = command.get('params', {})
        if not action:
            self.main_window.chat_area_add_message("System", "Unrecognized command format.")
            return
    
        logging.info(f"Dispatching action: {action} with params: {params}")
    
        actions = {
            'start_server': self.main_window.start_server,
            'stop_server': self.main_window.stop_server,
            'upload_files': self.main_window.upload_files_command,
            'remove_files': self.main_window.remove_selected_files_command,
            'change_setting': self.main_window.change_setting,
            'save_project': self.main_window.save_project_command,
            'load_project': self.main_window.load_project_command,
            'create_new_project': self.main_window.new_project_command,
            'start_analysis': self.main_window.start_analysis_command,
            'start_processing': self.main_window.start_processing_command,
            'pause_processing': self.main_window.pause_processing_command,
            'resume_processing': self.main_window.resume_processing_command,
            'stop_processing': self.main_window.stop_processing_command,
            'toggle_gantt_chart': self.main_window.toggle_gantt_chart_command,
            'generate_final_report': self.main_window.generate_final_report_command,
            'switch_llm': self.main_window.switch_llm_command
            # Add more actions as needed
        }
    
        func = actions.get(action)
        if func:
            try:
                func(**params)
                self.main_window.chat_area_add_message("System", f"Action '{action}' executed successfully.")
                logging.info(f"Action '{action}' executed successfully.")
            except Exception as e:
                self.main_window.chat_area_add_message("System", f"Error executing '{action}': {e}")
                logging.error(f"Error executing '{action}': {e}")
        else:
            self.main_window.chat_area_add_message("System", f"Action '{action}' not recognized.")
            logging.warning(f"Action '{action}' not recognized.")

class TaskProcessor(QThread):
    progress_updated = pyqtSignal(int)
    task_message = pyqtSignal(str, str)  # Sender, Message
    task_result = pyqtSignal(str, str)   # Sender, Message
    task_status = pyqtSignal(int, int, str)
    
    def __init__(self, tasks, llm_client, llm_model, parent=None):
        super().__init__(parent)
        self.tasks = tasks
        self.llm_client = llm_client
        self.llm_model = llm_model
        self._paused = False
        self._stopped = False
        self.max_retries = 3
        self.context_memory = {}
    
    def run(self):
        total_goals = sum(len(task.get('Goals', [])) for task in self.tasks)
        completed_goals = 0
        for i, task in enumerate(self.tasks):
            if self._stopped:
                break
            while self._paused:
                self.msleep(100)
            task_title = task.get('Title', f'Task {i+1}')
            self.task_message.emit("Assistant", f"Starting task '{task_title}'")
            self.task_status.emit(i, -1, 'processing')
            for j, goal in enumerate(task.get('Goals', [])):
                if self._stopped:
                    break
                while self._paused:
                    self.msleep(100)
                retry, success = 0, False
                while retry < self.max_retries and not success and not self._stopped:
                    if retry > 0:
                        self.task_message.emit("Assistant", f"Retry {retry} for goal: {goal}")
                        logging.debug(f"Retry {retry} for goal: {goal}")
                    self.task_message.emit("Assistant", f"Processing goal: {goal}")
                    self.task_status.emit(i, j, 'processing')
                    result = self.process_goal(task, goal, retry > 0)
                    success = self.self_review(goal, result)
                    if success:
                        self.task_result.emit("Assistant", f"Result for '{goal}':\n{result}")
                        self.context_memory[goal] = result
                        self.task_status.emit(i, j, 'completed')
                    else:
                        self.task_status.emit(i, j, 'error')
                        retry += 1
                if not success:
                    self.task_message.emit("Assistant", f"Failed '{goal}' after {self.max_retries} attempts.")
                    self.task_status.emit(i, j, 'failed')
                completed_goals += 1
                self.progress_updated.emit(int((completed_goals / total_goals) * 100))
            self.task_status.emit(i, -1, 'completed')
            summary = self.create_task_summary(task)
            self.task_result.emit("Assistant", f"Summary for '{task_title}':\n{summary}")
            self.context_memory[task_title] = summary
        if not self._stopped:
            self.task_message.emit("Assistant", "All tasks completed.")
            final = self.generate_final_result()
            self.task_result.emit("Assistant", f"Final Result:\n{final}")
    
    def process_goal(self, task, goal, retry=False):
        context = self.get_relevant_context(goal)
        prompt = f"""
As an experienced software architect, create a detailed solution for the goal: {goal}

**Description:**
{task.get('ApplicationDescription', '')}

**Context:**
{context}

Provide clear language and code examples where appropriate.
"""
        logging.debug(f"Generating for '{goal}'.")
        return self.llm_client.generate(prompt, temperature=0.7, max_tokens=150)
    
    def self_review(self, goal, result):
        prompt = f"""
Review the result for "{goal}":

{result}

Is it complete and correct? Answer "Yes" or "No".
"""
        review = self.llm_client.generate(prompt, temperature=0.5, max_tokens=10)
        logging.debug(f"Self-review für '{goal}': {review}")
        if "Yes" in review:
            return True
        self.task_message.emit("Assistant", f"Review failed for '{goal}': {review}")
        return False
    
    def get_relevant_context(self, current_goal):
        return "\n".join(f"**{k}:**\n{v}" for k, v in self.context_memory.items())
    
    def create_task_summary(self, task):
        return "\n".join(f"{g}:\n{self.context_memory.get(g, '')}" for g in task.get('Goals', []))
    
    def generate_final_result(self):
        context = "\n".join(self.context_memory.values())
        prompt = f"""
Summarize all results and generate the complete application code with documentation.

**Context:**
{context}
"""
        logging.debug("Generating final result.")
        return self.llm_client.generate(prompt, temperature=0.7, max_tokens=500)
    
    def pause(self):
        self._paused = True
        logging.debug("TaskProcessor paused.")
    
    def resume(self):
        self._paused = False
        logging.debug("TaskProcessor resumed.")
    
    def stop(self):
        self._stopped = True
        logging.debug("TaskProcessor stopped.")

class MessageWidget(QFrame):
    def __init__(self, sender, message, parent=None):
        super().__init__(parent)
        self.sender, self.message = sender, message
        self.init_ui()
    
    def init_ui(self):
        layout = QVBoxLayout(self)
        self.setFrameStyle(QFrame.StyledPanel | QFrame.Raised)
        self.setLineWidth(0)
        if self.sender.lower() == "assistant":
            color = "#2e2e2e"
        elif self.sender.lower() == "system":
            color = "#555555"
        else:
            color = "#0078d7"  # User messages in blue
        self.setStyleSheet(f"""
            QFrame {{
                background-color: {color};
                border-radius: 10px;
                padding: 10px;
            }}
        """)
        sender_label = QLabel(f"<b>{self.sender}:</b>")
        sender_label.setStyleSheet("color: #fff;")
        layout.addWidget(sender_label)
        for part in self.parse_message(self.message):
            if part['type'] == 'text':
                lbl = QLabel(part['content'])
                lbl.setStyleSheet("color: #dcdcdc;")
                lbl.setWordWrap(True)
                lbl.setFont(QFont("Arial", 12))
                layout.addWidget(lbl)
            elif part['type'] == 'code':
                code = QPlainTextEdit(part['content'])
                code.setReadOnly(True)
                code.setStyleSheet("""
                    QPlainTextEdit {
                        background-color: #1e1e1e;
                        color: #f8f8f2;
                        border: 1px solid #555;
                        border-radius: 4px;
                        font-family: Consolas, monospace;
                        padding: 5px;
                        font-size: 12pt;
                    }
                """)
                layout.addWidget(code)
            elif part['type'] == 'link':
                link = QLabel(f'<a href="{part["url"]}">{part["content"]}</a>')
                link.setStyleSheet("color: #3498db;")
                link.setOpenExternalLinks(True)
                layout.addWidget(link)
        self.setLayout(layout)
    
    def parse_message(self, msg):
        # Regex to find code blocks and links
        pattern = re.compile(r'```(.*?)```|https?://\S+')
        parts, last = [], 0
        for m in pattern.finditer(msg):
            if m.start() > last:
                text = msg[last:m.start()]
                parts.append({'type': 'text', 'content': self.highlight_keywords(text)})
            if m.group(1):
                parts.append({'type': 'code', 'content': m.group(1).strip()})
            else:
                url = m.group()
                parts.append({'type': 'link', 'content': url, 'url': url})
            last = m.end()
        if last < len(msg):
            parts.append({'type': 'text', 'content': self.highlight_keywords(msg[last:])})
        return parts
    
    def highlight_keywords(self, text):
        # Highlight keywords in blue (e.g., commands)
        keywords = ['start_server', 'stop_server', 'upload_files', 'remove_files',
                    'change_setting', 'save_project', 'load_project', 'create_new_project',
                    'start_analysis', 'start_processing', 'pause_processing', 'resume_processing',
                    'stop_processing', 'toggle_gantt_chart', 'generate_final_report', 'switch_llm']
        for kw in keywords:
            pattern = re.compile(r'\b' + re.escape(kw) + r'\b')
            text = pattern.sub(f'<span style="color:#3498db;">{kw}</span>', text)
        return text

class ChatInputWidget(QWidget):
    send_message = pyqtSignal(str)
    
    def __init__(self, parent=None):
        super().__init__(parent)
        layout = QHBoxLayout(self)
        self.input_field = QTextEdit()
        self.input_field.setFixedHeight(60)
        self.input_field.setStyleSheet("""
            QTextEdit {
                background-color: #2b2b2b;
                color: #dcdcdc;
                border: 1px solid #555;
                border-radius: 4px;
                padding: 5px;
                font-size: 12pt;
            }
        """)
        self.input_field.setPlaceholderText("Geben Sie Ihre Nachricht hier ein...")
        self.send_button = QPushButton("Senden")
        self.send_button.setStyleSheet("""
            QPushButton {
                background-color: #5cb85c;
                color: #fff;
                border: none;
                border-radius: 4px;
                padding: 15px 25px;
                font-size: 12pt;
            }
            QPushButton:hover {
                background-color: #4cae4c;
            }
        """)
        self.send_button.clicked.connect(self.emit_message)
        layout.addWidget(self.input_field)
        layout.addWidget(self.send_button)
        layout.setContentsMargins(0,0,0,0)
        self.setLayout(layout)
    
    def emit_message(self):
        message = self.input_field.toPlainText().strip()
        if message:
            self.send_message.emit(message)
            self.input_field.clear()

class GanttChartWidget(QWidget):
    def __init__(self, plan_data, parent=None):
        super().__init__(parent)
        self.plan_data = plan_data
        layout = QVBoxLayout(self)
        label = QLabel("Entwicklungsplan Zeitachse")
        label.setStyleSheet("font-size: 16pt; font-weight: bold; color: #fff;")
        layout.addWidget(label)
        try:
            from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
            from matplotlib.figure import Figure
            self.figure = Figure(figsize=(8,6))
            self.canvas = FigureCanvas(self.figure)
            layout.addWidget(self.canvas)
            self.draw_gantt_chart()
        except ImportError:
            logging.error("Matplotlib nicht installiert.")
            error_label = QLabel("Matplotlib ist nicht installiert. Gantt-Diagramm kann nicht angezeigt werden.")
            error_label.setStyleSheet("color: red;")
            layout.addWidget(error_label)
        self.setLayout(layout)
    
    def update_chart(self, plan_data):
        self.plan_data = plan_data
        self.draw_gantt_chart()
    
    def draw_gantt_chart(self):
        try:
            from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
            from matplotlib.figure import Figure
            self.figure.clf()
            ax = self.figure.add_subplot(111)
            if not self.plan_data:
                ax.text(0.5, 0.5, 'Keine Daten zum Anzeigen', ha='center', va='center', transform=ax.transAxes, color='white')
            else:
                start_date = datetime.now()
                durations = {task['Title']: max(len(task.get('Goals', [])),1) for task in self.plan_data}
                tasks, dur = list(durations.keys()), list(durations.values())
                starts = [start_date + timedelta(days=sum(dur[:i])) for i in range(len(dur))]
                for i, task in enumerate(tasks):
                    ax.barh(y=i, width=dur[i], left=starts[i].toordinal(), height=0.4, color='#5bc0de')
                ax.set_yticks(range(len(tasks)))
                ax.set_yticklabels(tasks, color='white', fontsize=10)
                ax.set_xlabel('Datum', color='white')
                ax.set_title('Gantt-Diagramm', color='white')
                ax.tick_params(axis='x', colors='white')
                ax.tick_params(axis='y', colors='white')
                ax.set_facecolor('#1e1e1e')
                self.figure.autofmt_xdate()
                # Convert ordinal to matplotlib date format
                dates = [start.toordinal() for start in starts]
                ax.set_xlim(min(dates) - 1, max(dates) + max(dur) + 1)
            self.canvas.draw()
        except Exception as e:
            logging.error(f"Fehler beim Zeichnen des Gantt-Diagramms: {e}")

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle('Intelligenter Assistent für Entwickler')
        self.setGeometry(100, 100, 800, 600)
        self.settings = {
            'available_models_chat': [],
            'available_models_project': [],
            'selected_model_chat': '',
            'selected_model_project': '',
            'temperature': 0.7,
            'max_tokens': 150,
            'server_url': 'http://localhost',
            'server_port': 11434,
            'theme': 'Dark',
            'font': QFont("Arial", 10),
            'projects': [],
            'last_used_project': None
        }
        self.load_projects()
        self.llm_client_chat = LLMClient(base_url=f"{self.settings['server_url']}:{self.settings['server_port']}", model_name=self.settings.get('selected_model_chat', ''))
        self.llm_client_project = LLMClient(base_url=f"{self.settings['server_url']}:{self.settings['server_port']}", model_name=self.settings.get('selected_model_project', ''))
        self.active_llm = self.llm_client_chat  # Default LLM
        self.command_dispatcher = CommandDispatcher(self, self.active_llm)
        self.plan_data, self.uploaded_files = [], []
        self.server_process = None
        self.task_processor = None
        self.init_ui()
        self.apply_styles()
        self.update_server_status()
        self.command_dispatcher = CommandDispatcher(self, self.active_llm)
        self.display_welcome_message()
    
    def load_projects(self):
        # Load projects from a JSON file
        projects_file = os.path.join(BASE_DIR, 'projects.json')
        if os.path.exists(projects_file):
            try:
                with open(projects_file, 'r', encoding='utf-8') as f:
                    data = json.load(f)
                    self.settings['projects'] = data.get('projects', [])
                    self.settings['last_used_project'] = data.get('last_used_project')
                logging.debug("Projekte erfolgreich geladen.")
            except Exception as e:
                logging.error(f"Fehler beim Laden der Projekte: {e}")
                self.settings['projects'] = []
                self.settings['last_used_project'] = None
        else:
            self.settings['projects'] = []
            self.settings['last_used_project'] = None
    
    def save_projects(self):
        # Save projects to a JSON file
        projects_file = os.path.join(BASE_DIR, 'projects.json')
        data = {
            'projects': self.settings['projects'],
            'last_used_project': self.settings['last_used_project']
        }
        try:
            with open(projects_file, 'w', encoding='utf-8') as f:
                json.dump(data, f, ensure_ascii=False, indent=4)
            logging.debug("Projekte erfolgreich gespeichert.")
        except Exception as e:
            logging.error(f"Fehler beim Speichern der Projekte: {e}")
    
    def init_ui(self):
        central_widget = QWidget()
        main_layout = QVBoxLayout()
        
        # Chat Area
        self.chat_area = QScrollArea()
        self.chat_area.setStyleSheet("""
            QScrollArea { background-color: #1e1e1e; border: none; }
            QScrollBar:vertical { background-color: #2b2b2b; width: 12px; }
            QScrollBar::handle:vertical { background-color: #555; min-height: 20px; border-radius: 6px; }
            QScrollBar::add-line:vertical, QScrollBar::sub-line:vertical { height: 0px; }
            QScrollBar::up-arrow:vertical, QScrollBar::down-arrow:vertical { background: none; }
        """)
        self.chat_container = QWidget()
        self.chat_layout = QVBoxLayout(self.chat_container)
        self.chat_layout.setAlignment(Qt.AlignTop)
        self.chat_area.setWidgetResizable(True)
        self.chat_area.setWidget(self.chat_container)

        # Loading Animation
        self.loading_label = QLabel()
        self.loading_label.setAlignment(Qt.AlignCenter)
        loading_gif = os.path.join(BASE_DIR, "loading.gif")
        if os.path.exists(loading_gif):
            self.loading_movie = QMovie(loading_gif)
            if self.loading_movie.isValid():
                self.loading_label.setMovie(self.loading_movie)
                self.loading_movie.start()
            else:
                self.loading_label.setText("Loading...")
        else:
            self.loading_label.setText("Loading...")
        self.loading_label.setVisible(False)

        # Chat Input
        self.chat_input = ChatInputWidget()
        self.chat_input.send_message.connect(self.handle_user_message)

        # Progress Bar
        self.progress_bar = QProgressBar()
        self.progress_bar.setValue(0)
        self.progress_bar.setAlignment(Qt.AlignCenter)
        self.progress_bar.setStyleSheet("""
            QProgressBar {
                border: 1px solid #555;
                border-radius: 5px;
                text-align: center;
                height: 20px;
                background-color: #2b2b2b;
                color: #fff;
                font-size: 12pt;
            }
            QProgressBar::chunk { background-color: #5bc0de; width: 20px; }
        """)

        # Gantt Chart Placeholder
        self.gantt_chart = GanttChartWidget(self.plan_data)
        self.gantt_chart.setVisible(False)  # Initially hidden

        main_layout.addWidget(self.chat_area, stretch=1)
        main_layout.addWidget(self.loading_label)
        main_layout.addWidget(self.progress_bar)
        main_layout.addWidget(self.chat_input)
        main_layout.addWidget(self.gantt_chart)
        main_layout.setStretch(0, 1)
        main_layout.setStretch(1, 0)
        central_widget.setLayout(main_layout)
        self.setCentralWidget(central_widget)

        self.create_menu()
    
    def display_welcome_message(self):
        welcome_msg = "<b>Willkommen beim Intelligenten Assistenten für Entwickler!</b><br><br>"
        if self.settings['projects']:
            welcome_msg += "Verfügbare Projekte:<br>"
            for proj in self.settings['projects']:
                if proj['name'] == self.settings['last_used_project']:
                    welcome_msg += f"• <b>{proj['name']}</b> (zuletzt verwendet)<br>"
                else:
                    welcome_msg += f"• {proj['name']}<br>"
        else:
            welcome_msg += "Keine verfügbaren Projekte.<br>"
        welcome_msg += "<br>Grundfunktionen:<ul>"
        welcome_msg += "<li>Projekte erstellen, laden und speichern</li>"
        welcome_msg += "<li>Einstellungen anpassen</li>"
        welcome_msg += "<li>Dateien hoch- und herunterladen</li>"
        welcome_msg += "<li>Entwicklungspläne erstellen und verwalten</li>"
        welcome_msg += "</ul>"
        self.chat_area_add_message("System", welcome_msg)
    
    def create_menu(self):
        menu_bar = self.menuBar()
        file_menu = menu_bar.addMenu('Datei')
        for name, shortcut, action in [('Projekt speichern', 'Ctrl+S', self.save_project_command),
                                       ('Projekt laden', 'Ctrl+O', self.load_project_command),
                                       ('Neues Projekt', 'Ctrl+N', self.new_project_command),
                                       ('Beenden', 'Ctrl+Q', self.close)]:
            act = QAction(name, self)
            act.setShortcut(shortcut)
            act.triggered.connect(action)
            file_menu.addAction(act)
        edit_menu = menu_bar.addMenu('Bearbeiten')
        for name, shortcut, action in [('Verarbeitung pausieren', 'Ctrl+P', self.pause_processing_command),
                                       ('Verarbeitung fortsetzen', 'Ctrl+R', self.resume_processing_command),
                                       ('Verarbeitung stoppen', 'Ctrl+T', self.stop_processing_command),
                                       ('Einstellungen', 'Ctrl+Shift+S', self.open_settings_dialog_command)]:
            act = QAction(name, self)
            act.setShortcut(shortcut)
            act.triggered.connect(action)
            edit_menu.addAction(act)
        llm_menu = menu_bar.addMenu('LLM')
        switch_llm_action = QAction('LLM wechseln', self)
        switch_llm_action.triggered.connect(self.switch_llm_command)
        llm_menu.addAction(switch_llm_action)
        help_menu = menu_bar.addMenu('Hilfe')
        about_action = QAction('Über', self)
        about_action.triggered.connect(self.show_about_dialog)
        help_menu.addAction(about_action)
    
    def handle_user_message(self, message):
        self.chat_area_add_message("Benutzer", message)
        self.loading_label.setVisible(True)
        threading.Thread(target=self.process_message, args=(message,), daemon=True).start()
    
    def process_message(self, message):
        # Konstruktion des Prompts zur Anweisung des LLM
        if self.active_llm == self.llm_client_chat:
            prompt = f"""
Sie sind ein intelligenter Assistent, der Nutzern hilft, ihre Entwicklungsprojekte zu verwalten.
Sie können Aktionen wie start_server, stop_server, upload_files, remove_files, change_setting, 
save_project, load_project, create_new_project, start_analysis, start_processing, 
pause_processing, resume_processing, stop_processing, toggle_gantt_chart, generate_final_report, switch_llm ausführen.

Wenn eine Benutzeranfrage eine Aktion erfordert, antworten Sie mit einem JSON-Objekt:
{{"action": "action_name", "params": {{...}}}}

Andernfalls antworten Sie mit einer hilfreichen Nachricht.

Benutzeranfrage: "{message}"
"""
        else:
            prompt = f"""
Sie sind ein spezialisierter Assistent für die Projektentwicklung. Helfen Sie bei der Erstellung und Bearbeitung von Entwicklungsplänen und unterstützen Sie bei der Code-Generierung.

Wenn eine Benutzeranfrage eine Aktion erfordert, antworten Sie mit einem JSON-Objekt:
{{"action": "action_name", "params": {{...}}}}

Andernfalls antworten Sie mit einer hilfreichen Nachricht.

Benutzeranfrage: "{message}"
"""
        response = self.active_llm.generate(prompt, temperature=self.settings['temperature'], max_tokens=self.settings['max_tokens'])
        self.loading_label.setVisible(False)
        try:
            # Versuch, die Antwort als JSON zu parsen
            command = json.loads(response)
            if isinstance(command, dict) and 'action' in command:
                self.command_dispatcher.dispatch(command)
            else:
                # Wenn keine Aktion, als normale Nachricht behandeln
                self.chat_area_add_message("Assistent", response)
        except json.JSONDecodeError:
            # Falls Parsing fehlschlägt, als normale Nachricht behandeln
            self.chat_area_add_message("Assistent", response)
    
    def chat_area_add_message(self, sender, message):
        msg = MessageWidget(sender, message)
        self.chat_layout.addWidget(msg)
        QTimer.singleShot(100, lambda: self.chat_area.verticalScrollBar().setValue(
            self.chat_area.verticalScrollBar().maximum()))
    
    def switch_llm_command(self, **kwargs):
        dlg = SwitchLLMDialog(self.settings, self)
        if dlg.exec_() == QDialog.Accepted:
            selected_llm = dlg.selected_llm
            if selected_llm == 'Chat LLM':
                self.active_llm = self.llm_client_chat
                self.command_dispatcher.llm_client = self.active_llm
                logging.debug("Aktives LLM gewechselt zu: Chat LLM")
                self.chat_area_add_message("System", "Aktives LLM wurde zu Chat LLM gewechselt.")
            elif selected_llm == 'Projekt LLM':
                self.active_llm = self.llm_client_project
                self.command_dispatcher.llm_client = self.active_llm
                logging.debug("Aktives LLM gewechselt zu: Projekt LLM")
                self.chat_area_add_message("System", "Aktives LLM wurde zu Projekt LLM gewechselt.")
            else:
                self.chat_area_add_message("System", "Ungültige LLM-Auswahl.")
                logging.warning("Ungültige LLM-Auswahl getroffen.")
    
    def start_server_command(self, **kwargs):
        self.start_server()
    
    def stop_server_command(self, **kwargs):
        self.stop_server()
    
    def upload_files_command(self, **kwargs):
        # Erwartet 'files' Parameter als Liste von Dateipfaden
        files = kwargs.get('files', [])
        if files:
            self.uploaded_files.extend(files)
            self.display_uploaded_files()
            self.chat_area_add_message("System", f"{len(files)} Datei(en) über Befehl hochgeladen.")
            logging.debug(f"Hochgeladene Dateien über Befehl: {files}")
        else:
            self.chat_area_add_message("System", "Keine Dateien für den Upload bereitgestellt.")
    
    def remove_selected_files_command(self, **kwargs):
        # Erwartet 'files' Parameter als Liste von zu entfernenden Dateipfaden
        files = kwargs.get('files', [])
        if files:
            removed = [f for f in files if f in self.uploaded_files]
            for f in removed:
                self.uploaded_files.remove(f)
            self.display_uploaded_files()
            self.chat_area_add_message("System", f"{len(removed)} Datei(en) über Befehl entfernt.")
            logging.debug(f"Entfernte Dateien über Befehl: {removed}")
        else:
            self.chat_area_add_message("System", "Keine Dateien für die Entfernung bereitgestellt.")
    
    def change_setting(self, **kwargs):
        # Erwartet 'setting' und 'value' Parameter
        setting = kwargs.get('setting')
        value = kwargs.get('value')
        if setting and value is not None:
            if setting in self.settings:
                self.settings[setting] = value
                if setting in ['selected_model_chat', 'selected_model_project']:
                    self.active_llm = self.llm_client_chat if setting == 'selected_model_chat' else self.llm_client_project
                    self.command_dispatcher.llm_client = self.active_llm
                self.apply_styles()
                self.chat_area_add_message("System", f"Einstellung '{setting}' auf '{value}' geändert.")
                logging.debug(f"Einstellung '{setting}' auf '{value}' geändert.")
            else:
                self.chat_area_add_message("System", f"Einstellung '{setting}' existiert nicht.")
                logging.warning(f"Versuch, nicht vorhandene Einstellung '{setting}' zu ändern.")
        else:
            self.chat_area_add_message("System", "Ungültige Parameter zum Ändern der Einstellungen.")
    
    def save_project_command(self, **kwargs):
        self.save_project()
    
    def load_project_command(self, **kwargs):
        self.load_project()
    
    def new_project_command(self, **kwargs):
        self.new_project()
    
    def start_analysis_command(self, **kwargs):
        self.start_analysis_command_handler(**kwargs)
    
    def start_processing_command(self, **kwargs):
        self.start_processing()
    
    def pause_processing_command(self, **kwargs):
        self.pause_processing()
    
    def resume_processing_command(self, **kwargs):
        self.resume_processing()
    
    def stop_processing_command(self, **kwargs):
        self.stop_processing()
    
    def toggle_gantt_chart_command(self, **kwargs):
        self.toggle_gantt_chart()
    
    def generate_final_report_command(self, **kwargs):
        self.generate_final_report()
    
    def apply_styles(self):
        palette = QPalette()
        if self.settings['theme'] == 'Dark':
            palette.setColor(QPalette.Window, QColor(45,45,45))
            palette.setColor(QPalette.WindowText, Qt.white)
            palette.setColor(QPalette.Base, QColor(30,30,30))
            palette.setColor(QPalette.AlternateBase, QColor(45,45,45))
            palette.setColor(QPalette.ToolTipBase, Qt.white)
            palette.setColor(QPalette.ToolTipText, Qt.white)
            palette.setColor(QPalette.Text, Qt.white)
            palette.setColor(QPalette.Button, QColor(45,45,45))
            palette.setColor(QPalette.ButtonText, Qt.white)
            palette.setColor(QPalette.BrightText, Qt.red)
            palette.setColor(QPalette.Link, QColor(42,130,218))
            palette.setColor(QPalette.Highlight, QColor(42,130,218))
            palette.setColor(QPalette.HighlightedText, Qt.black)
        else:
            palette = QApplication.style().standardPalette()
        self.setPalette(palette)
        self.setFont(self.settings.get('font', QFont("Arial", 10)))
        for widget in [self.chat_input.input_field]:
            widget.setFont(self.settings.get('font', QFont("Arial", 10)))
    
    def update_llm_list(self):
        self.settings['available_models_chat'] = self.llm_client_chat.get_models()
        self.settings['available_models_project'] = self.llm_client_project.get_models()
        if self.settings['available_models_chat']:
            self.settings['selected_model_chat'] = self.settings['available_models_chat'][0]
        if self.settings['available_models_project']:
            self.settings['selected_model_project'] = self.settings['available_models_project'][0]
        if self.settings['available_models_chat'] or self.settings['available_models_project']:
            models = ', '.join(self.settings['available_models_chat'] + self.settings['available_models_project'])
            self.chat_area_add_message("System", f"Verfügbare Modelle: {models}.")
        else:
            logging.warning("Keine Modelle gefunden.")
            self.chat_area_add_message("System", "Keine Modelle auf dem LLM-Server gefunden.")
    
    def update_server_status(self):
        running = self.check_server_running()
        if running:
            self.chat_area_add_message("System", "Serverstatus: 🟢 Läuft")
            self.update_llm_list()
        else:
            self.chat_area_add_message("System", "Serverstatus: 🔴 Gestoppt")
            self.settings['available_models_chat'] = []
            self.settings['available_models_project'] = []
            self.settings['selected_model_chat'] = ''
            self.settings['selected_model_project'] = ''
    
    def check_server_running(self):
        try:
            response = requests.get(f'{self.llm_client_chat.base_url}/api/tags', timeout=5)
            return response.status_code == 200
        except requests.RequestException:
            return False
    
    def start_server(self):
        if self.check_server_running():
            self.chat_area_add_message("System", "Server läuft bereits.")
            logging.info("Serverstart versucht, aber bereits aktiv.")
            return
        try:
            llm_cmd = 'ollama'  # Stellen Sie sicher, dass 'ollama' installiert und im PATH ist
            server_path = shutil.which(llm_cmd)
            if not server_path:
                self.chat_area_add_message("System", f"Befehl '{llm_cmd}' nicht gefunden.")
                logging.error(f"Befehl '{llm_cmd}' nicht gefunden.")
                return
            args = [llm_cmd, 'serve', '--port', str(self.settings['server_port'])]
            creation = subprocess.CREATE_NEW_CONSOLE if os.name == 'nt' else 0
            self.server_process = subprocess.Popen(
                args,
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE,
                shell=(os.name=='nt'),
                creationflags=creation
            )
            self.chat_area_add_message("System", "LLM-Server wird gestartet...")
            self.server_timer = QTimer()
            self.server_timer.timeout.connect(self.check_server_startup)
            self.server_timer.start(1000)
            self.server_startup_attempts = 0
            logging.debug("LLM-Serverstart initiiert.")
        except Exception as e:
            self.chat_area_add_message("System", f"Serverstart fehlgeschlagen: {e}")
            logging.error(f"Fehler beim Serverstart: {e}")
    
    def check_server_startup(self):
        self.server_startup_attempts +=1
        if self.check_server_running():
            self.server_timer.stop()
            self.server_timer = None
            self.update_server_status()
            self.chat_area_add_message("System", "LLM-Server gestartet.")
            logging.debug("LLM-Server erfolgreich gestartet.")
            self.save_projects()
        elif self.server_startup_attempts >=15:
            self.server_timer.stop()
            self.server_timer = None
            self.chat_area_add_message("System", "Server konnte nicht rechtzeitig gestartet werden.")
            QMessageBox.warning(self, "Warnung", "Server wurde nicht innerhalb der Zeit gestartet.")
            self.update_server_status()
            logging.error("LLM-Server konnte nicht innerhalb von 15 Sekunden gestartet werden.")
    
    def stop_server(self):
        if self.server_process and self.server_process.poll() is None:
            try:
                self.server_process.terminate()
                self.server_process.wait(timeout=10)
                self.server_process = None
                self.chat_area_add_message("System", "LLM-Server gestoppt.")
                logging.debug("LLM-Server gestoppt.")
                self.update_server_status()
                self.save_projects()
            except subprocess.TimeoutExpired:
                self.server_process.kill()
                self.server_process = None
                self.chat_area_add_message("System", "LLM-Server wurde zwangsweise gestoppt.")
                self.update_server_status()
                logging.error("LLM-Server wurde zwangsweise gestoppt.")
            except Exception as e:
                self.chat_area_add_message("System", f"Server konnte nicht gestoppt werden: {e}")
                logging.error(f"Fehler beim Serverstopp: {e}")
        else:
            self.chat_area_add_message("System", "Server läuft nicht.")
            logging.info("Serverstopp versucht, aber Server läuft nicht.")
    
    def upload_files(self):
        files, _ = QFileDialog.getOpenFileNames(self, "Dateien auswählen", "", "Alle Dateien (*)")
        new = [f for f in files if f not in self.uploaded_files]
        if new:
            self.uploaded_files.extend(new)
            logging.debug(f"Hochgeladene Dateien: {new}")
            self.chat_area_add_message("System", f"{len(new)} Datei(en) hochgeladen.")
        else:
            self.chat_area_add_message("System", "Keine neuen Dateien hochgeladen (Duplikate übersprungen).")
    
    def upload_files_command(self, **kwargs):
        # Dateien über Befehl hochladen mit 'files' Parameter
        files = kwargs.get('files', [])
        if files:
            # Sicherheitsüberprüfung der Dateipfade
            accessible_files = [f for f in files if os.path.isfile(f)]
            if accessible_files:
                self.uploaded_files.extend(accessible_files)
                self.display_uploaded_files()
                self.chat_area_add_message("System", f"{len(accessible_files)} Datei(en) über Befehl hochgeladen.")
                logging.debug(f"Hochgeladene Dateien über Befehl: {accessible_files}")
            else:
                self.chat_area_add_message("System", "Keine gültigen Dateien zum Hochladen bereitgestellt.")
        else:
            self.chat_area_add_message("System", "Keine Dateien für den Upload bereitgestellt.")
    
    def display_uploaded_files(self):
        # Informationen über hochgeladene Dateien über Chat anzeigen
        if self.uploaded_files:
            self.chat_area_add_message("System", f"Hochgeladene Dateien:\n" + "\n".join(self.uploaded_files))
        else:
            self.chat_area_add_message("System", "Keine Dateien hochgeladen.")
    
    def change_setting(self, **kwargs):
        # Bereits in CommandDispatcher behandelt
        pass
    
    def save_project(self):
        # Projekt über Dialog speichern
        file, _ = QFileDialog.getSaveFileName(self, "Projekt speichern", "", "JSON Dateien (*.json)")
        if file:
            project = {
                'name': self.settings.get('last_used_project', 'Neues Projekt'),
                'description': getattr(self, 'description_text', ''),
                'uploaded_files': self.uploaded_files,
                'plan_data': self.plan_data,
                'settings': {
                    'selected_model_chat': self.settings.get('selected_model_chat', ''),
                    'selected_model_project': self.settings.get('selected_model_project', ''),
                    'temperature': self.settings.get('temperature', 0.7),
                    'max_tokens': self.settings.get('max_tokens', 150),
                    'server_url': self.settings.get('server_url', 'http://localhost'),
                    'server_port': self.settings.get('server_port', 11434),
                    'theme': self.settings.get('theme', 'Dark'),
                    'font': self.font_to_dict(self.settings.get('font', QFont("Arial", 10)))
                }
            }
            try:
                with open(file, 'w', encoding='utf-8') as f:
                    json.dump(project, f, ensure_ascii=False, indent=4)
                self.chat_area_add_message("System", "Projekt erfolgreich gespeichert.")
                logging.debug(f"Projekt gespeichert unter {file}.")
                # Update project list
                self.add_or_update_project(project['name'])
            except Exception as e:
                self.chat_area_add_message("System", f"Projekt konnte nicht gespeichert werden: {e}")
                logging.error(f"Fehler beim Speichern des Projekts: {e}")
    
    def save_project_command(self, **kwargs):
        self.save_project()
    
    def load_project(self):
        # Projekt über Dialog laden
        file, _ = QFileDialog.getOpenFileName(self, "Projekt laden", "", "JSON Dateien (*.json)")
        if file:
            try:
                with open(file, 'r', encoding='utf-8') as f:
                    project = json.load(f)
                # Annahme: 'description' ist Teil des Projekts
                load_description = QMessageBox.question(self, "Beschreibung laden", "Möchten Sie die Projektbeschreibung laden?", QMessageBox.Yes | QMessageBox.No)
                if load_description == QMessageBox.Yes:
                    description = project.get('description', '')
                    self.chat_area_add_message("System", f"Geladene Beschreibung:\n{description}")
                self.uploaded_files = project.get('uploaded_files', [])
                if self.uploaded_files:
                    self.chat_area_add_message("System", f"Hochgeladene Dateien:\n" + "\n".join(self.uploaded_files))
                self.plan_data = project.get('plan_data', [])
                settings = project.get('settings', {})
                if settings:
                    self.settings.update({
                        'selected_model_chat': settings.get('selected_model_chat', ''),
                        'selected_model_project': settings.get('selected_model_project', ''),
                        'temperature': settings.get('temperature', 0.7),
                        'max_tokens': settings.get('max_tokens', 150),
                        'server_url': settings.get('server_url', 'http://localhost'),
                        'server_port': settings.get('server_port', 11434),
                        'theme': settings.get('theme', 'Dark'),
                        'font': self.dict_to_font(settings.get('font', {}))
                    })
                    self.llm_client_chat.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                               f":{self.settings['server_port']}"
                    self.llm_client_project.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                               f":{self.settings['server_port']}"
                    self.apply_styles()
                if self.plan_data:
                    self.chat_area_add_message("System", "Entwicklungsplan geladen.")
                    logging.debug("Entwicklungsplan geladen.")
                    self.gantt_chart.update_chart(self.plan_data)
                    self.gantt_chart.setVisible(True)
                QMessageBox.information(self, "Erfolg", "Projekt erfolgreich geladen.")
                logging.debug(f"Projekt geladen von {file}.")
                # Update project list and last used project
                project_name = project.get('name', 'Neues Projekt')
                self.add_or_update_project(project_name)
            except Exception as e:
                self.chat_area_add_message("System", f"Projekt konnte nicht geladen werden: {e}")
                logging.error(f"Fehler beim Laden des Projekts: {e}")
    
    def load_project_command(self, **kwargs):
        self.load_project()
    
    def new_project(self):
        name, ok = QInputDialog.getText(self, "Neues Projekt", "Geben Sie den Namen des neuen Projekts ein:")
        if ok and name:
            self.settings['last_used_project'] = name
            self.uploaded_files = []
            self.plan_data = []
            self.chat_area_add_message("System", f"Neues Projekt '{name}' gestartet.")
            logging.debug(f"Neues Projekt '{name}' initiiert.")
            self.gantt_chart.update_chart(self.plan_data)
            self.gantt_chart.setVisible(False)
            self.add_or_update_project(name)
        elif ok:
            self.chat_area_add_message("System", "Projektname darf nicht leer sein.")
            logging.warning("Neues Projekt ohne Namen angefragt.")
    
    def new_project_command(self, **kwargs):
        self.new_project()
    
    def add_or_update_project(self, project_name):
        existing = next((p for p in self.settings['projects'] if p['name'] == project_name), None)
        if not existing:
            self.settings['projects'].append({'name': project_name})
        self.settings['last_used_project'] = project_name
        self.save_projects()
        self.chat_area_add_message("System", f"Projekt '{project_name}' wurde hinzugefügt/aktualisiert.")
        logging.debug(f"Projekt '{project_name}' wurde in der Liste aktualisiert.")
    
    def start_analysis_command_handler(self, **kwargs):
        # Startet die Analyse basierend auf bereitgestellter Beschreibung und Dateien
        description = kwargs.get('description')
        if not description:
            self.chat_area_add_message("System", "Keine Beschreibung für die Analyse bereitgestellt.")
            return
        self.task_processor = TaskProcessor(self.plan_data, self.active_llm, self.settings['selected_model_project'])
        self.task_processor.progress_updated.connect(self.update_progress)
        self.task_processor.task_message.connect(lambda sender, msg: self.chat_area_add_message(sender, msg))
        self.task_processor.task_result.connect(lambda sender, msg: self.chat_area_add_message(sender, msg))
        self.task_processor.task_status.connect(self.update_task_status)
        self.task_processor.finished.connect(self.processing_finished)
        self.task_processor.start()
        self.chat_area_add_message("System", "Analyse des Projekts gestartet.")
        logging.debug("TaskProcessor für Analyse gestartet.")
    
    def start_analysis_command(self, **kwargs):
        # Diese Funktion sammelt typischerweise die Beschreibung und startet die Analyse
        QMessageBox.information(self, "Info", "Analyse kann über Chat-Befehle gestartet werden.")
        logging.info("Analyse über Befehl angefragt.")
    
    def start_processing_command(self, **kwargs):
        self.start_processing()
    
    def start_processing(self):
        if not self.plan_data:
            self.chat_area_add_message("System", "Kein Entwicklungsplan verfügbar. Bitte zuerst Analyse starten.")
            logging.warning("Verarbeitung ohne Entwicklungsplan gestartet.")
            return
        if not self.active_llm:
            self.chat_area_add_message("System", "Kein LLM ausgewählt. Bitte Modell über Einstellungen wählen.")
            logging.warning("Verarbeitung ohne LLM-Auswahl gestartet.")
            return
        self.chat_area_add_message("System", "Verarbeitung der Aufgaben gestartet...")
        self.task_processor = TaskProcessor(self.plan_data, self.active_llm, self.settings['selected_model_project'])
        self.task_processor.progress_updated.connect(self.update_progress)
        self.task_processor.task_message.connect(lambda sender, msg: self.chat_area_add_message(sender, msg))
        self.task_processor.task_result.connect(lambda sender, msg: self.chat_area_add_message(sender, msg))
        self.task_processor.task_status.connect(self.update_task_status)
        self.task_processor.finished.connect(self.processing_finished)
        self.task_processor.start()
        logging.debug("Task-Verarbeitung gestartet.")
    
    def pause_processing_command(self, **kwargs):
        self.pause_processing()
    
    def pause_processing(self):
        if hasattr(self, 'task_processor') and self.task_processor.isRunning():
            self.task_processor.pause()
            self.chat_area_add_message("System", "Verarbeitung pausiert.")
            logging.debug("Verarbeitung pausiert.")
        else:
            self.chat_area_add_message("System", "Keine laufende Verarbeitung zum Pausieren.")
            logging.info("Pause-Verarbeitung ohne aktive Prozesse angefragt.")
    
    def resume_processing_command(self, **kwargs):
        self.resume_processing()
    
    def resume_processing(self):
        if hasattr(self, 'task_processor') and self.task_processor.isRunning():
            self.task_processor.resume()
            self.chat_area_add_message("System", "Verarbeitung fortgesetzt.")
            logging.debug("Verarbeitung fortgesetzt.")
        else:
            self.chat_area_add_message("System", "Keine pausierte Verarbeitung zum Fortsetzen.")
            logging.info("Fortsetzen-Verarbeitung ohne pausierte Prozesse angefragt.")
    
    def stop_processing_command(self, **kwargs):
        self.stop_processing()
    
    def stop_processing(self):
        if hasattr(self, 'task_processor') and self.task_processor.isRunning():
            self.task_processor.stop()
            self.task_processor.wait()
            self.chat_area_add_message("System", "Verarbeitung gestoppt.")
            self.progress_bar.setValue(0)
            self.loading_label.setVisible(False)
            logging.debug("Verarbeitung gestoppt.")
        else:
            self.chat_area_add_message("System", "Keine laufende Verarbeitung zum Stoppen.")
            logging.info("Stop-Verarbeitung ohne aktive Prozesse angefragt.")
    
    def toggle_gantt_chart(self):
        self.gantt_chart.setVisible(not self.gantt_chart.isVisible())
        if self.gantt_chart.isVisible():
            self.chat_area_add_message("System", "Gantt-Diagramm Anzeige aktiviert.")
        else:
            self.chat_area_add_message("System", "Gantt-Diagramm Anzeige deaktiviert.")
        logging.debug("Gantt-Diagramm Anzeige umgeschaltet.")
    
    def toggle_gantt_chart_command(self, **kwargs):
        self.toggle_gantt_chart()
    
    def generate_final_report_command(self, **kwargs):
        self.generate_final_report()
    
    def generate_final_report(self):
        if hasattr(self, 'task_processor') and hasattr(self.task_processor, 'generate_final_result'):
            final = self.task_processor.generate_final_result()
            self.chat_area_add_message("Assistent", final)
        else:
            self.chat_area_add_message("System", "Kein Abschlussbericht verfügbar. Bitte zuerst Verarbeitung starten.")
            logging.warning("Abschlussbericht ohne aktiven TaskProcessor angefragt.")
    
    def open_settings_dialog_command(self, **kwargs):
        self.open_settings_dialog()
    
    def open_settings_dialog(self, **kwargs):
        dlg = SettingsDialog(self.settings, self)
        if dlg.exec_() == QDialog.Accepted:
            self.settings = dlg.settings
            self.apply_styles()
            # Aktualisiere LLMClient base_url korrekt mit server_port
            self.llm_client_chat.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                           f":{self.settings['server_port']}"
            self.llm_client_project.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                           f":{self.settings['server_port']}"
            self.update_server_status()
            self.chat_area_add_message("System", "Einstellungen erfolgreich aktualisiert.")
            self.update_llm_list()
    
    def show_about_dialog(self):
        QMessageBox.about(self, "Über", "Intelligenter Assistent für Entwickler\nVersion 2.6\n\nEntwickelt, um Entwicklern bei der Erstellung umfassender Entwicklungspläne und Skripte mit Hilfe von LLMs zu unterstützen.")
    
    def closeEvent(self, event):
        reply = QMessageBox.question(self, 'Bestätigen', 'Möchten Sie die Anwendung wirklich beenden?', QMessageBox.Yes | QMessageBox.No, QMessageBox.No)
        if reply == QMessageBox.Yes:
            if self.server_process and self.server_process.poll() is None:
                try:
                    self.server_process.terminate()
                    self.server_process.wait(timeout=10)
                    logging.debug("Server beim Beenden beendet.")
                except subprocess.TimeoutExpired:
                    self.server_process.kill()
                    logging.error("Server wurde beim Beenden zwangsweise beendet.")
            if hasattr(self, 'task_processor') and self.task_processor.isRunning():
                self.task_processor.stop()
                self.task_processor.wait()
                logging.debug("TaskProcessor beim Beenden beendet.")
            self.save_projects()
            event.accept()
        else:
            event.ignore()
    
    def font_to_dict(self, font):
        return {
            'family': font.family(),
            'pointSize': font.pointSize(),
            'bold': font.bold(),
            'italic': font.italic(),
            'underline': font.underline(),
            'strikeOut': font.strikeOut(),
            'weight': font.weight(),
            'style': font.style(),
            'styleHint': font.styleHint()
        }
    
    def dict_to_font(self, d):
        font = QFont()
        font.setFamily(d.get('family', "Arial"))
        font.setPointSize(d.get('pointSize', 10))
        font.setBold(d.get('bold', False))
        font.setItalic(d.get('italic', False))
        font.setUnderline(d.get('underline', False))
        font.setStrikeOut(d.get('strikeOut', False))
        font.setWeight(d.get('weight', QFont.Normal))
        font.setStyle(d.get('style', QFont.StyleNormal))
        font.setStyleHint(d.get('styleHint', QFont.AnyStyle))
        return font
    
    def start_analysis(self):
        # Placeholder für zukünftige Implementierungen
        QMessageBox.information(self, "Info", "Analyse kann über Chatbefehle gestartet werden.")
        logging.info("Analyse über Befehl initiiert.")
    
    def update_progress(self, value):
        self.progress_bar.setValue(value)
    
    def update_task_status(self, task_index, goal_index, status):
        # Hier kann man den Status der einzelnen Aufgaben aktualisieren, z.B. in einem internen Tracking
        pass  # Placeholder für zukünftige Implementierungen
    
    def processing_finished(self):
        self.progress_bar.setValue(100)
        self.chat_area_add_message("System", "Verarbeitung abgeschlossen.")
        logging.debug("Verarbeitung abgeschlossen.")
        # Aktualisiere Gantt-Diagramm
        self.gantt_chart.update_chart(self.plan_data)
        if self.plan_data:
            self.gantt_chart.setVisible(True)
    
    def generate_final_report(self):
        if hasattr(self, 'task_processor') and hasattr(self.task_processor, 'generate_final_result'):
            final = self.task_processor.generate_final_result()
            self.chat_area_add_message("Assistent", final)
        else:
            self.chat_area_add_message("System", "Kein Abschlussbericht verfügbar. Bitte zuerst Verarbeitung starten.")
            logging.warning("Abschlussbericht ohne aktiven TaskProcessor angefragt.")
    
    def open_settings_dialog(self):
        dlg = SettingsDialog(self.settings, self)
        if dlg.exec_() == QDialog.Accepted:
            self.settings = dlg.settings
            self.apply_styles()
            # Aktualisiere LLMClient base_url korrekt mit server_port
            self.llm_client_chat.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                           f":{self.settings['server_port']}"
            self.llm_client_project.base_url = f"{self.settings['server_url'].rstrip('/')}" \
                                           f":{self.settings['server_port']}"
            self.update_server_status()
            self.chat_area_add_message("System", "Einstellungen erfolgreich aktualisiert.")
            self.update_llm_list()
    
class SettingsDialog(QDialog):
    def __init__(self, current_settings, parent=None):
        super().__init__(parent)
        self.setWindowTitle("Einstellungen")
        self.setModal(True)
        self.settings = current_settings.copy()
        self.init_ui()
    
    def init_ui(self):
        layout = QVBoxLayout(self)
        tabs_layout = QVBoxLayout()
    
        # LLM Settings - Chat
        llm_chat_group = QGroupBox("Chat LLM-Einstellungen")
        llm_chat_layout = QFormLayout()
        self.llm_chat_combo = QComboBox()
        self.llm_chat_combo.addItems(self.settings.get('available_models_chat', []))
        self.llm_chat_combo.setCurrentText(self.settings.get('selected_model_chat', ''))
        llm_chat_layout.addRow("Modell:", self.llm_chat_combo)
        llm_chat_group.setLayout(llm_chat_layout)
        tabs_layout.addWidget(llm_chat_group)
    
        # LLM Settings - Project
        llm_project_group = QGroupBox("Projekt LLM-Einstellungen")
        llm_project_layout = QFormLayout()
        self.llm_project_combo = QComboBox()
        self.llm_project_combo.addItems(self.settings.get('available_models_project', []))
        self.llm_project_combo.setCurrentText(self.settings.get('selected_model_project', ''))
        llm_project_layout.addRow("Modell:", self.llm_project_combo)
        llm_project_group.setLayout(llm_project_layout)
        tabs_layout.addWidget(llm_project_group)
    
        # General Settings
        general_group = QGroupBox("Allgemeine Einstellungen")
        general_layout = QFormLayout()
        self.temperature_spin = QDoubleSpinBox()
        self.temperature_spin.setRange(0.0,1.0)
        self.temperature_spin.setDecimals(2)
        self.temperature_spin.setSingleStep(0.05)
        self.temperature_spin.setValue(self.settings.get('temperature', 0.7))
        self.max_tokens_spin = QSpinBox()
        self.max_tokens_spin.setRange(100, 10000)
        self.max_tokens_spin.setValue(self.settings.get('max_tokens', 150))
        general_layout.addRow("Temperatur:", self.temperature_spin)
        general_layout.addRow("Max Tokens:", self.max_tokens_spin)
        general_group.setLayout(general_layout)
        tabs_layout.addWidget(general_group)
    
        # Server Settings
        server_group = QGroupBox("Server-Einstellungen")
        server_layout = QFormLayout()
        self.server_url_input = QLineEdit(self.settings.get('server_url', 'http://localhost'))
        self.server_port_input = QSpinBox()
        self.server_port_input.setRange(1,65535)
        self.server_port_input.setValue(self.settings.get('server_port', 11434))
        server_layout.addRow("Server-URL:", self.server_url_input)
        server_layout.addRow("Server-Port:", self.server_port_input)
        server_group.setLayout(server_layout)
        tabs_layout.addWidget(server_group)
    
        # UI Settings
        ui_group = QGroupBox("UI-Einstellungen")
        ui_layout = QFormLayout()
        self.theme_combo = QComboBox()
        self.theme_combo.addItems(["Dark", "Light"])
        self.theme_combo.setCurrentText(self.settings.get('theme', 'Dark'))
        self.font_button = QPushButton("Schriftart wählen")
        self.font_button.clicked.connect(self.choose_font)
        self.selected_font = self.settings.get('font', QFont("Arial", 10))
        status = 'Fett' if self.selected_font.bold() else 'Regulär'
        self.font_display = QLabel(f"{self.selected_font.family()}, {self.selected_font.pointSize()}pt, {status}")
        ui_layout.addRow("Thema:", self.theme_combo)
        ui_layout.addRow("Schriftart:", self.font_button)
        ui_layout.addRow("", self.font_display)
        ui_group.setLayout(ui_layout)
        tabs_layout.addWidget(ui_group)
    
        layout.addLayout(tabs_layout)
        buttons = QDialogButtonBox(QDialogButtonBox.Save | QDialogButtonBox.Cancel)
        buttons.accepted.connect(self.save)
        buttons.rejected.connect(self.reject)
        layout.addWidget(buttons)
        self.setLayout(layout)
    
    def choose_font(self):
        font, ok = QFontDialog.getFont(self.selected_font, self, "Schriftart wählen")
        if ok:
            self.selected_font = font
            status = 'Fett' if font.bold() else 'Regulär'
            self.font_display.setText(f"{font.family()}, {font.pointSize()}pt, {status}")
    
    def save(self):
        self.settings.update({
            'selected_model_chat': self.llm_chat_combo.currentText(),
            'selected_model_project': self.llm_project_combo.currentText(),
            'temperature': self.temperature_spin.value(),
            'max_tokens': self.max_tokens_spin.value(),
            'server_url': self.server_url_input.text(),
            'server_port': self.server_port_input.value(),
            'theme': self.theme_combo.currentText(),
            'font': self.selected_font
        })
        self.accept()

class SwitchLLMDialog(QDialog):
    def __init__(self, current_settings, parent=None):
        super().__init__(parent)
        self.setWindowTitle("LLM wechseln")
        self.setModal(True)
        self.settings = current_settings
        self.selected_llm = None
        self.init_ui()
    
    def init_ui(self):
        layout = QVBoxLayout(self)
        info_label = QLabel("Wählen Sie das LLM aus, das Sie aktivieren möchten:")
        layout.addWidget(info_label)
        self.llm_combo = QComboBox()
        self.llm_combo.addItems(['Chat LLM', 'Projekt LLM'])
        layout.addWidget(self.llm_combo)
        buttons = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)
        buttons.accepted.connect(self.ok)
        buttons.rejected.connect(self.reject)
        layout.addWidget(buttons)
        self.setLayout(layout)
    
    def ok(self):
        self.selected_llm = self.llm_combo.currentText()
        self.accept()

class WelcomeDialog(QDialog):
    def __init__(self, projects, last_used_project, parent=None):
        super().__init__(parent)
        self.setWindowTitle("Willkommen")
        self.setModal(True)
        self.projects = projects
        self.last_used_project = last_used_project
        self.init_ui()
    
    def init_ui(self):
        layout = QVBoxLayout(self)
        welcome_label = QLabel("<h2>Willkommen beim Intelligenten Assistenten für Entwickler!</h2>")
        layout.addWidget(welcome_label)
        if self.projects:
            projects_text = "<b>Verfügbare Projekte:</b><ul>"
            for proj in self.projects:
                if proj['name'] == self.last_used_project:
                    projects_text += f"<li><b>{proj['name']}</b> (zuletzt verwendet)</li>"
                else:
                    projects_text += f"<li>{proj['name']}</li>"
            projects_text += "</ul>"
            layout.addWidget(QLabel(projects_text))
        else:
            layout.addWidget(QLabel("Keine verfügbaren Projekte. Sie können ein neues Projekt erstellen."))
        info_text = """
        <b>Grundfunktionen:</b>
        <ul>
            <li>Projekte erstellen, laden und speichern</li>
            <li>Einstellungen anpassen</li>
            <li>Dateien hoch- und herunterladen</li>
            <li>Entwicklungspläne erstellen und verwalten</li>
        </ul>
        """
        layout.addWidget(QLabel(info_text))
        buttons = QDialogButtonBox(QDialogButtonBox.Ok)
        buttons.accepted.connect(self.accept)
        layout.addWidget(buttons)
        self.setLayout(layout)

def main():
    app = QApplication(sys.argv)
    dark_palette = QPalette()
    dark_palette.setColor(QPalette.Window, QColor(45,45,45))
    dark_palette.setColor(QPalette.WindowText, Qt.white)
    dark_palette.setColor(QPalette.Base, QColor(30,30,30))
    dark_palette.setColor(QPalette.AlternateBase, QColor(45,45,45))
    dark_palette.setColor(QPalette.ToolTipBase, Qt.white)
    dark_palette.setColor(QPalette.ToolTipText, Qt.white)
    dark_palette.setColor(QPalette.Text, Qt.white)
    dark_palette.setColor(QPalette.Button, QColor(45,45,45))
    dark_palette.setColor(QPalette.ButtonText, Qt.white)
    dark_palette.setColor(QPalette.BrightText, Qt.red)
    dark_palette.setColor(QPalette.Link, QColor(42,130,218))
    dark_palette.setColor(QPalette.Highlight, QColor(42,130,218))
    dark_palette.setColor(QPalette.HighlightedText, Qt.black)
    app.setPalette(dark_palette)
    app.setStyle("Fusion")
    window = MainWindow()
    # Display welcome dialog
    welcome_dlg = WelcomeDialog(window.settings['projects'], window.settings['last_used_project'], window)
    welcome_dlg.exec_()
    window.show()
    logging.debug("Anwendung gestartet.")
    sys.exit(app.exec_())

if __name__ == '__main__':
    main()
```

### Wichtige Änderungen und Ergänzungen im Detail:

1. **Willkommensdialog (`WelcomeDialog`):**
    - Beim Start der Anwendung wird ein Dialogfenster angezeigt, das den Benutzer begrüßt und eine Übersicht über vorhandene Projekte sowie grundlegende Funktionen bietet.
    - Projekte werden aus einer `projects.json`-Datei geladen, die im Anwendungspfad gespeichert ist.

2. **Projektverwaltung:**
    - Neue Projekte können erstellt, geladen und gespeichert werden. Die Liste der Projekte wird in der `projects.json`-Datei verwaltet.
    - Das zuletzt verwendete Projekt wird hervorgehoben und automatisch ausgewählt.

3. **LLM-Verwaltung:**
    - Zwei verschiedene LLMs werden unterstützt: eines für allgemeine Chat-Konversationen (`Chat LLM`) und eines für Projektbearbeitungen (`Projekt LLM`).
    - Über das Menü `LLM -> LLM wechseln` kann der Benutzer zwischen den beiden LLMs wechseln.
    - In den Einstellungen können separate Modelle für Chat und Projekte ausgewählt werden.

4. **Chat-Formatierung:**
    - Schlüsselwörter und Befehle im Chat werden blau hervorgehoben, um sie besser erkennbar zu machen.
    - Links werden als anklickbare Hyperlinks dargestellt.
    - Der Parser im `MessageWidget` wurde erweitert, um Links zu erkennen und entsprechend zu formatieren.

5. **TaskProcessor-Verbesserungen:**
    - Der `TaskProcessor` überprüft nun sorgfältig, ob alle Ziele erreicht wurden, bevor der vollständige Code generiert wird.
    - Bei fehlgeschlagenen Zielen versucht der `TaskProcessor` automatisch, diese erneut zu bearbeiten, bis sie erfolgreich sind oder die maximale Anzahl der Versuche erreicht ist.

6. **Projektdaten speichern und laden:**
    - Projekte werden in einer separaten `projects.json`-Datei gespeichert und beim Laden der Anwendung geladen.
    - Beim Speichern eines Projekts wird es automatisch zur Liste der verfügbaren Projekte hinzugefügt oder aktualisiert.

7. **Weitere Verbesserungen:**
    - Verbesserte Fehlerbehandlung und Logging für eine robustere Anwendung.
    - Optimierte Benutzeroberfläche mit klaren Unterscheidungen zwischen verschiedenen Nachrichtentypen im Chat.
    - Erweiterte Einstellungen ermöglichen eine detaillierte Anpassung der Anwendung, einschließlich der Auswahl von LLMs, Thema und Schriftart.

### Voraussetzungen:

- **LLM-Server:** Stellen Sie sicher, dass der entsprechende LLM-Server (z.B. `ollama`) installiert und funktionsfähig ist. Passen Sie gegebenenfalls den Befehl zum Starten des Servers (`llm_cmd`) an.
- **Dependencies:** Die Anwendung benötigt die folgenden Python-Pakete:
    - `PyQt5`
    - `requests`
    - `matplotlib` (für das Gantt-Diagramm)
- **Dateien:** Stellen Sie sicher, dass eine `loading.gif`-Datei im Anwendungspfad vorhanden ist, um die Ladeanimation darzustellen.

### Verwendung:

1. **Starten der Anwendung:**
    - Beim Start erscheint ein Willkommensdialog, der eine Übersicht über vorhandene Projekte bietet und grundlegende Funktionen erläutert.

2. **Projektverwaltung:**
    - Über das Menü können Sie Projekte erstellen, laden und speichern.
    - Die Liste der verfügbaren Projekte wird im Willkommensdialog und im Chat angezeigt.

3. **LLM-Wechsel:**
    - Über das Menü `LLM -> LLM wechseln` können Sie zwischen dem Chat-LLM und dem Projekt-LLM wechseln.

4. **Chat-Funktionen:**
    - Interagieren Sie mit dem Assistenten über das Chatfenster. Bestimmte Befehle und Schlüsselwörter werden hervorgehoben, um die Benutzererfahrung zu verbessern.

5. **Einstellungen:**
    - Über das Menü können Sie auf die Einstellungen zugreifen und diese nach Ihren Bedürfnissen anpassen, einschließlich der Auswahl der LLMs, des Themas und der Schriftart.

6. **Gantt-Diagramm:**
    - Nach Abschluss der Aufgabenverarbeitung wird das Gantt-Diagramm angezeigt, das den Entwicklungsplan visualisiert.

Ich hoffe, diese Verbesserungen erfüllen Ihre Anforderungen und tragen zu einer noch besseren Benutzererfahrung bei!

Q- oky we have a problem from leetCode called “Maximize Score Of Numbers in Ranges “ 


Oky so you are given an array of integers “start” and an integer  
—> so we can form n intervales like this 
[start[i],start[i]+d] oky 
You are asked to choose “n” integers where the “i”th integer must belong to the “i” th interval . 

→ the score of the chosen integers is defined as the “minimum” absolute difference between any two integers that have been chosen 

Q- so we need to maximum possible score of the chosen integers : 



Bahi honi 3ana list kema heka : [6,0,3]  , d

—> n : len([6,0,3]) : 3 
Donc bsh yukon 3ana 3 ranges kema hka 

[6,8] , [0,2] , [3,5]

Q- twa lzemn nt5tro 3 “integers” bsh ywknow akbar score possible 

Q- lzem kol integer “i” yukon fe range i 

M3nha de interval [0,2] —> lzemna n5taro w7ed ykon ben 0 and 2 mch 18 for example 

El score bsh yukon “min” absolute difference between the chosen integers oky : 

Q- y3ni bsh tkon min dife




Ntswer njmo nts3mlo greedy approach , where we choice the local optimum ,willing to find the global optimum later 

Q- step 1 : sort  the start integers 
A greedy approach dema ykon mts7ab enik t sorty the original array bsh y3wnik tl9 el global approach latter oky 

Donc start[0,3,6] 

Q- step 2 : netswer lzeman n7do (compute ) each range possible 

Y3ni lzemna ntl3 kol mara the lower & upper bound bsh y3wna n3rfo choices we have oky 

Fe example mt3na bsh ykon [0,2] , [3,5] ,  [6,8] 

Tw el objective function is to maximuse the score 

El score ymthel the min abs diff between all the choisin values oky 
Donc ntlswer lezman  el choice el loul and l5er 

Y3ni el range 0 and range n ykono yukon el choice fe loul min value 
And la5er max value 

So in the example first range – value = 0 
So in the example last  range - value = 8 

Q- now rest the second range 

Hypothetical 1 : choice the lower bound → value = 3 
Hypthetical  2 : choice the upper bound → value = 5 

Hypthetical 3 : these a method to better choice the right integer s 

Q- let’s test and proof each hypoth oky 

For the first one 

Here's the absolute difference between each pair of the integers 0, 3, and 8:
|0 - 3| = 3
|0 - 8| = 8
|3 - 8| = 5
→ donc the min score and a7sen score howa 3 

→ njmo n7sno ? eyhhh 




Q- let’s test and poog  each hypoth oky 

For the second |0 - 5| = 5
|0 - 8| = 8
|5 - 8| = 3
→ donc the min score and a7sen score how 3 
→ njmo n7sno ? eyhh 

Q- donc kef chofna les example and hypth 1 and 3 3rafan enou ? 

A part upper range and lower range the choice mt3 el ba9ii lzemo 

More advance search right ? 

Q- for now give you feedback about thinking process so far without give me the fulls solution just feedback in each point oky 
Your thought process is heading in the right direction, and I like how you are reasoning through the problem step by step. Let’s go over your approach:

### Step-by-step feedback:

1. **Understanding the Problem**:
   You clearly understand the problem and the objective: to maximize the score, which is the minimum absolute difference between any two chosen integers. It's good that you have recognized that this requires making careful choices within each interval.

2. **Greedy Approach**:
   Using a greedy strategy is a great first instinct here. Sorting the `start` array can help create ordered intervals and allow you to make better decisions, especially in choosing boundary values that could lead to a larger minimum absolute difference.

3. **Interval Calculation**:
   Correctly calculating the ranges as `[start[i], start[i] + d]` for each element is a solid approach. This forms the basis for choosing your values and evaluating the differences between them.

4. **Choosing Boundary Values**:
   You correctly noted that to maximize the score, you would probably want to focus on the first and last intervals and choose extreme values (smallest from the first range and largest from the last range). This helps push the absolute differences higher between the smallest and largest values.

5. **Evaluating Middle Values**:
   When choosing a value from the middle ranges, you hypothesized different strategies (choosing the lower bound or upper bound). Testing and calculating the absolute differences was a good way to evaluate these hypotheses. You observed that both hypotheses can give you the same minimum difference score (3 in the example), but also acknowledged that there might be more optimal choices for these middle values.

6. **Advanced Search for Middle Choices**:
   You correctly realized that selecting the middle values might need a more nuanced approach beyond just choosing the extremes, such as using a strategy to space them out more evenly or adjusting based on the ranges of adjacent values.

### Next Steps:

Your overall thought process shows a solid grasp of the problem. Here are a few things to consider going forward:

- **Greedy might work well for boundary values**, but there could be further improvements for selecting middle values. You might want to explore a way to dynamically adjust middle values to ensure that they maximize the minimum absolute difference.

- **Consider edge cases**: What if some intervals overlap? What if `d` is small or large? These could impact the choice of values and the score, so accounting for such variations is crucial.

- **Proof of optimality**: You’ve tested hypotheses by calculating the differences in examples, but formalizing why one strategy works better might be helpful. You could explore whether the greedy approach always guarantees an optimal solution or if other strategies (e.g., dynamic programming or binary search) might yield better results.

You're on the right track, and your method of testing and refining hypotheses is key to solving this type of problem! Keep iterating on this approach, and if you hit a roadblock, consider whether additional insights (like pattern recognition or mathematical optimization) could further improve your solution.


Q- s7i7 mzelna m9ena el solution final ame we reduce the search space : 
Be enou 
3ranfana a greedy approach work here 
Sorting the original array alos work 
Choice the first and last integer is straight bord just choice the lower & upper bounds resectivvelly oky 
El problem 9nea enou nl9w method dymaicque and systematique t5lin n5taro the middles values correctly oky 
Q- ntswer n5mo fe consumized binary search is will work why ? 
The sorting the ciritee is pricineted here so it’s easy to ellminte the seach space each time in to oky 
But the updating choice here is not “straightbord “ i mean how to choice to go from left or right side of the seach ? 


Q- I have one hypothetic and i want to test ? 


The updating condition is when computing the “mid” value 
Then we compute the min abs difference between global upper bound 
And global lower bound ? 
Ken 9ena enou the first one > right difference 
	Nmchi le right side 
Else left side oky 


Q- can you test my hypothetic without given me the full solution oky 

Absolutely, let's examine and test your hypothesis step by step without diving into the full solution. Your approach shows a thoughtful strategy, and it's great to see you breaking down the problem systematically. Here's a detailed analysis of your hypothesis:

### **Your Hypothesis Recap:**

1. **Sort the `start` Array:**
   - Sorting helps in organizing the intervals in an orderly fashion, which is crucial for applying binary search or any other systematic approach.

2. **Compute Each Range:**
   - Forming intervals as `[start[i], start[i] + d]` for each element ensures you have all possible choices for each integer from the respective intervals.

3. **Choose Boundary Values First:**
   - Selecting the smallest value from the first interval and the largest value from the last interval to maximize the difference between the extremes.

4. **Customized Binary Search for Middle Values:**
   - Implementing a binary search mechanism to determine the optimal values for the middle intervals by:
     - Computing a `mid` value.
     - Evaluating the minimum absolute difference between the chosen integers.
     - Deciding to move left or right in the search space based on certain conditions.

### **Testing Your Hypothesis:**

Let's walk through an example to see how your hypothesis holds up.

#### **Example:**

- **Given:**
  - `start = [6, 0, 3]`
  - `d = 2`
  
- **After Sorting:**
  - `start = [0, 3, 6]`
  
- **Formed Intervals:**
  - `[0, 2]`, `[3, 5]`, `[6, 8]`

#### **Step-by-Step Execution:**

1. **Choose Boundary Values:**
   - **First Interval:** Choose `0` (smallest).
   - **Last Interval:** Choose `8` (largest).
   
   This gives us two chosen numbers: `0` and `8`.

2. **Determine Middle Value Using Customized Binary Search:**
   
   - **Possible Choices for Middle Interval `[3, 5]`:**
     - `3` or `5`
   
   - **Compute Minimum Absolute Differences:**
     - If choosing `3`:
       - `|0 - 3| = 3`
       - `|3 - 8| = 5`
       - **Minimum Difference:** `3`
       
     - If choosing `5`:
       - `|0 - 5| = 5`
       - `|5 - 8| = 3`
       - **Minimum Difference:** `3`
   
   - **Observation:**
     - Both choices (`3` and `5`) yield the same minimum difference (`3`).

3. **Evaluating Your Binary Search Approach:**
   
   - **Mid Calculation:**
     - For the middle interval `[3, 5]`, the mid-point is `(3 + 5) / 2 = 4`.
     - However, `4` is not an integer in this interval since the interval values are discrete (`3` and `5`).
   
   - **Decision Making:**
     - Since both ends yield the same minimum difference, your approach might decide to choose either based on additional conditions or simply accept that both choices are equally optimal in this case.

#### **Feedback on the Hypothesis:**

1. **Sorting the Array:**
   - **Correctness:** Sorting is a solid initial step as it organizes the intervals in a logical order, making it easier to apply systematic search methods like binary search.
   - **Benefit:** It allows you to leverage the sorted nature of intervals to make informed choices about where to place the integers for maximizing the minimum difference.

2. **Choosing Boundary Values First:**
   - **Correctness:** Selecting the smallest and largest possible values from the first and last intervals helps in maximizing the spread between your chosen numbers, which is beneficial for maximizing the minimum difference.
   - **Edge Cases:** Ensure that this strategy holds when intervals overlap or when the spread isn't as straightforward.

3. **Customized Binary Search for Middle Values:**
   - **Feasibility:** Applying binary search to determine optimal middle values is innovative. However, binary search typically requires a clear condition to decide which half of the search space to eliminate.
   - **Condition Clarity:** Your current condition—comparing the `mid` value's differences—needs to be well-defined to ensure that you move in the correct direction to maximize the minimum difference.
   - **Discrete Choices:** Since each interval offers discrete choices (e.g., `3` or `5`), binary search needs to account for this and possibly iterate over allowed discrete values instead of a continuous range.

4. **Minimizing Absolute Differences:**
   - **Objective Alignment:** Computing the minimum absolute difference between chosen integers aligns perfectly with the problem's objective.
   - **Comprehensive Evaluation:** Ensure that all pairs are considered when determining the minimum difference, not just adjacent pairs.

### **Potential Improvements & Considerations:**

1. **Handling Multiple Middle Intervals:**
   - **Scalability:** While your hypothesis works for a single middle interval, consider how it scales with multiple middle intervals. The interaction between multiple choices can complicate the binary search logic.
   
2. **Overlapping Intervals:**
   - **Complex Scenarios:** If intervals overlap, the strategy for choosing boundary values and applying binary search might need adjustments to account for the increased flexibility or constraints in choosing values.

3. **Decision Criteria for Binary Search:**
   - **Clear Conditions:** Define explicit conditions for when to move left or right in your binary search. For example, if a chosen `mid` value allows for a larger minimum difference, you might want to explore higher values, and vice versa.
   
4. **Alternative Approaches:**
   - **Dynamic Programming:** Given the problem's nature, dynamic programming might offer a more systematic way to consider all intervals and their interactions.
   - **Greedy Enhancements:** While a greedy approach is a good starting point, combining it with other strategies (like binary search) can enhance its effectiveness.

### **Conclusion:**

Your hypothesis demonstrates a strong understanding of the problem and a strategic approach to solving it. The combination of sorting, boundary value selection, and customized binary search is promising. However, ensure that:

- **Binary Search Conditions are Well-Defined:** Clear and robust conditions are crucial for the binary search to navigate the search space effectively.
  
- **Edge Cases are Addressed:** Consider scenarios like overlapping intervals, identical intervals, or intervals with minimal range to ensure your approach is comprehensive.

- **Scalability is Maintained:** Verify that your approach scales well with larger input sizes and multiple middle intervals.

By iteratively testing and refining your hypothesis against various examples and edge cases, you'll be better positioned to finalize an optimal solution. Keep up the excellent work, and don't hesitate to explore alternative strategies if certain aspects of the binary search approach present challenges.

i need a name for a small 3d printing shop my themes are trendy and girly my target audience is women looking for gifts im selling intial keychains sports keychains and holiday keychains

Here are a few names that could suit your trendy and girly 3D printing shop:

1. **ChicPrint Creations**
2. **FemmeForm Prints**
3. **Trendy Trinkets Co.**
4. **GlamPrint Gifts**
5. **PrettyPrint Boutique**
6. **KeyCharm Corner**
7. **Lovely Layers Print**
8. **WonderPrint Studio**
9. **Girly Gifts & Gadgets**
10. **DazzlePrint Delights**

Each of these names aims to capture the trendy and feminine essence of your shop while appealing to women seeking charming gifts.

give me more names similar to key cham corner and glamprint gifts

Certainly! Here are some more names inspired by the concepts of "KeyCharm Corner" and "GlamPrint Gifts":

1. **CharmKey Creations**
2. **GlamourKey Prints**
3. **KeyGlam Boutique**
4. **CharmedPrints Haven**
5. **KeyGlam Emporium**
6. **ChicCharm Shop**
7. **KeyGlam Goods**
8. **DazzleKey Designs**
9. **GlowPrint Gifts**
10. **VogueKey Creations**

These names maintain a focus on keychains and the glamorous, trendy appeal you want to convey.

give me logos for this shop

Creating logos requires a visual design process that text alone can't provide, but I can certainly suggest some concepts and elements for your logo that a professional designer could help bring to life:

1. **CharmKey Creations**:
   - A sleek key shape integrated with a delicate, stylized font.
   - A charm or pendant attached to the key in the shape of a heart or an initial, with pastel colors for a feminine touch.

2. **GlamourKey Prints**:
   - A bold, glittery key with swirling, elegant typography.
   - Incorporate stars or sparkles around the key to emphasize glamour.

3. **KeyGlam Boutique**:
   - A minimalist design with a gold key and cursive, flowing text.
   - Use a keyhole motif combined with a small decorative element like a bow or flower.

4. **CharmedPrints Haven**:
   - A whimsical charm bracelet wrapping around the name, with dangling charms representing different holidays or themes.
   - Soft colors like pink, lavender, and mint green for a girly vibe.

5. **KeyGlam Emporium**:
   - A retro-style logo with a stylized vintage key and a banner or ribbon displaying the name.
   - Use combined textures of metallic and matte finishes.

6. **ChicCharm Shop**:
   - A modern, clean logo with a key and a colorful abstract charm.
   - Bold, sans-serif typography for the text, paired with a small ornamental design.

Remember, when designing a logo, simplicity and cohesiveness are crucial for effective branding. Engaging a professional graphic designer will help realize these concepts visually while ensuring your logo is versatile and maintains clarity across different media.