Fix this code

# Ensure you have the transformers library installed
# You can install it using pip:
# pip install transformers

import json
import logging
import asyncio
import sys
import os
import re
from PyQt5.QtCore import QObject, pyqtSignal
from PyQt5.QtWidgets import QMessageBox, QApplication, QDialog, QVBoxLayout, QListWidget, QHBoxLayout, QPushButton
from transformers import AutoTokenizer, AutoModelForCausalLM
import json
import logging
import asyncio
from PyQt5.QtCore import QObject, pyqtSignal
from PyQt5.QtWidgets import QDialog, QVBoxLayout, QListWidget, QHBoxLayout, QPushButton, QMessageBox, QApplication
import re
import os
import sys

def save_apis():
    pass  # Placeholder for the save_apis function implementation

def save_models(models):
    models_file_path = "path/to/models.json"  # Update this path as needed
    try:
        with open(models_file_path, 'w', encoding='utf-8') as f:
            json.dump(models, f, indent=4)
        logging.info("Models saved successfully.")
    except Exception as e:
        logging.error(f"Error saving models: {e}")

class Chatbot(QObject):
    status = pyqtSignal(str)
    error = pyqtSignal(str)
    finished = pyqtSignal(str)

    def __init__(self, context, user_input, apis, plugins, file_knowledge=None, max_new_tokens=50, temperature=0.7, top_p=0.9):
        super().__init__()
        self.context = context
        self.user_input = user_input
        self.apis = apis
        self.plugins = plugins
        self.file_knowledge = file_knowledge if file_knowledge is not None else []
        self.max_new_tokens = max_new_tokens
        self.temperature = temperature
        self.top_p = top_p
        self.reflection_history = {}
        self.virtual_env_path = "ai_virtual_env"

# Define the conversations directory
conversations_dir = "path/to/conversations"

class LLMManagerDialog(QDialog):
    def __init__(self, parent=None):
        super().__init__(parent)
        self.setWindowTitle("LLM Manager")
        self.layout = QVBoxLayout(self)
        
        self.model_list = QListWidget(self)
        self.layout.addWidget(self.model_list)
        
        self.button_layout = QHBoxLayout()
        self.save_button = QPushButton("Save", self)
        self.cancel_button = QPushButton("Cancel", self)
        self.button_layout.addWidget(self.save_button)
        self.button_layout.addWidget(self.cancel_button)
        self.layout.addLayout(self.button_layout)
        
        self.save_button.clicked.connect(self.accept)
        self.cancel_button.clicked.connect(self.reject)
        
        self.models = []  # Placeholder for the models list
        self.current_model = None  # Placeholder for the current model

    def get_updated_models(self):
        # Placeholder for getting updated models
        return self.models

    def get_selected_model(self):
        # Placeholder for getting the selected model
        return self.current_model

class ChatbotGUI(QObject):
    status = pyqtSignal(str)
    error = pyqtSignal(str)
    finished = pyqtSignal(str)

    def __init__(self):
        super().__init__()
        self.current_model_name = "default_model"

    def run(self):
        try:
            self.status.emit("Thinking...")
            asyncio.run(self.process())
        except Exception as e:
            logging.error(f"Error running chatbot: {e}")
            error_msg = f"Error during response generation: {e}"
            logging.error(error_msg)
            self.error.emit(error_msg)

    async def process(self):
        api_data = await self.fetch_api_data()
        prompt = self.build_prompt(api_data)
        initial_response = await self.generate_initial_response(prompt)
        final_response = await self.run_reflective_loop(initial_response)
        self.finished.emit(final_response)

    async def fetch_api_data(self):
        data = ""
        # Implementation for fetching API data
        return data

    def build_prompt(self, api_data):
        # Implementation for building the prompt
        return ""

    async def generate_initial_response(self, prompt):
        # Implementation for generating the initial response
        return ""

    async def run_reflective_loop(self, initial_response):
        # Implementation for running the reflective loop
        return ""

    def open_llm_manager(self):
        try:
            dialog = LLMManagerDialog(self)
            if dialog.exec_() == QDialog.Accepted:
                # Refresh the model list if changes were made
                global models
                models = dialog.get_updated_models()
                save_models(models)  # Save the updated models to the JSON file

                # Update the current model if it has changed
                new_model_name = dialog.get_selected_model()
                if new_model_name != self.current_model_name:
                    self.load_new_model(new_model_name)

                QMessageBox.information(self, "Models Updated", "The model list has been updated successfully.")
        except Exception as e:
            logging.error(f"Error opening LLM manager: {e}")
            QMessageBox.critical(self, "Error", f"An error occurred while opening the LLM manager: {str(e)}")

    def process_media_in_response(self, response):
        image_patterns = [
            r'(https?://\S+\.(?:png|jpg|jpeg|gif))',
            r'!\[.*?\]\((https?://\S+\.(?:png|jpg|jpeg|gif))\)'
        ]
        for pattern in image_patterns:
            matches = re.findall(pattern, response, re.IGNORECASE)
            for match in matches:
                img_url = match[1] if len(match) > 1 else match
                img_tag = f'<img src="{img_url}" width="300"/>'
                response = response.replace(match, img_tag)

        video_patterns = [
            r'(https?://\S+\.(?:mp4|webm|ogg))',
            r'!\[.*?\]\((https?://\S+\.(?:mp4|webm|ogg))\)'
        ]
        for pattern in video_patterns:
            matches = re.findall(pattern, response, re.IGNORECASE)
            for match in matches:
                video_url = match[1] if len(match) > 1 else match
                link = f'<a href="{video_url}">Video</a>'
                response = response.replace(match, link)
        return response

    def save_conversation(self, filename, context, reflection_history, file_knowledge):
        conversation_data = {
            "context": context,
            "reflection_history": reflection_history,
            "file_knowledge": file_knowledge
        }
        with open(os.path.join(conversations_dir, filename), 'w', encoding='utf-8') as f:
            json.dump(conversation_data, f, indent=4)

    def load_conversation(self, filename):
        with open(os.path.join(conversations_dir, filename), 'r', encoding='utf-8') as f:
            conversation_data = json.load(f)
        return (
            conversation_data.get("context", []),
            conversation_data.get("reflection_history", {}),
            conversation_data.get("file_knowledge", [])
        )

if __name__ == '__main__':
    # Initialize the Qt Application
    app = QApplication(sys.argv)
    
    # Create the Chatbot GUI instance
    gui = ChatbotGUI()
    
    # Show the chatbot window
    gui.show()
    
    # Execute the application event loop
    sys.exit(app.exec_())