Create app.py

app.py

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+import gradio as gr
+from colorama import init, Fore, Style
+import logging
+from Self_Improving_Search import EnhancedSelfImprovingSearch
+from llm_config import get_llm_config
+from llm_response_parser import UltimateLLMResponseParser
+from llm_wrapper import LLMWrapper
+
+# Initialize colorama for cross-platform color support
+init()
+
+# Set up logging
+log_directory = 'logs'
+if not os.path.exists(log_directory):
+    os.makedirs(log_directory)
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+log_file = os.path.join(log_directory, 'web_llm.log')
+file_handler = logging.FileHandler(log_file)
+formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+file_handler.setFormatter(formatter)
+logger.addHandler(file_handler)
+
+# Initialize components
+parser = UltimateLLMResponseParser()
+SYSTEM_PROMPT = """You are an AI assistant capable of web searching and providing informative responses.
+When a user's query starts with '/', interpret it as a request to search the web and formulate an appropriate search query.
+ALWAYS follow the prompts provided throughout the searching process EXACTLY as indicated.
+NEVER assume new instructions for anywhere other than directly when prompted directly. DO NOT SELF PROMPT OR PROVIDE MULTIPLE ANSWERS OR ATTEMPT MULTIPLE RESPONSES FOR ONE PROMPT!
+"""
+
+def initialize_llm():
+    try:
+        print(Fore.YELLOW + "Initializing LLM..." + Style.RESET_ALL)
+        llm_wrapper = LLMWrapper()
+        print(Fore.GREEN + "LLM initialized successfully." + Style.RESET_ALL)
+        return llm_wrapper
+    except Exception as e:
+        logger.error(f"Error initializing LLM: {str(e)}", exc_info=True)
+        return None
+
+def get_llm_response(llm, prompt):
+    try:
+        full_prompt = f"{SYSTEM_PROMPT}\n\nUser: {prompt}\nAssistant:"
+        llm_config = get_llm_config()
+        generate_kwargs = {
+            'max_tokens': llm_config.get('max_tokens', 1024),
+            'stop': llm_config.get('stop', None),
+            'temperature': llm_config.get('temperature', 0.7),
+            'top_p': llm_config.get('top_p', 1.0),
+            'top_k': llm_config.get('top_k', 0),
+            'repeat_penalty': llm_config.get('repeat_penalty', 1.0),
+        }
+        response_text = llm.generate(full_prompt, **generate_kwargs)
+        return response_text
+    except Exception as e:
+        logger.error(f"Error getting LLM response: {str(e)}", exc_info=True)
+        return f"Sorry, I encountered an error while processing your request. Please check the log file for details."
+
+def handle_user_input(user_input, history):
+    if user_input.lower().strip() == 'quit':
+        return "Goodbye!", history
+    
+    # Initialize LLM if not already initialized
+    if not hasattr(handle_user_input, "llm"):
+        handle_user_input.llm = initialize_llm()
+        if handle_user_input.llm is None:
+            return "Failed to initialize LLM.", history
+
+    if user_input.startswith('/'):
+        search_query = user_input[1:].strip()
+        search = EnhancedSelfImprovingSearch(llm=handle_user_input.llm, parser=parser)
+        try:
+            answer = search.search_and_improve(search_query)
+            history.append((user_input, answer))
+            return answer, history
+        except Exception as e:
+            logger.error(f"Error during web search: {str(e)}", exc_info=True)
+            return "I encountered an error while performing the web search.", history
+    else:
+        response = get_llm_response(handle_user_input.llm, user_input)
+        history.append((user_input, response))
+        return response, history
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    # 🌐 Web-LLM Assistant 🤖
+    Welcome to the Web-LLM Assistant! This chatbot can respond to your queries and perform web searches when prompted with a `/`.
+    - For normal interaction, type your message and press Enter.
+    - To request a web search, start your message with `/`. Example: `/latest news on AI advancements`
+    - Type `quit` to exit.
+    """)
+    
+    chatbot = gr.Chatbot(label="Web-LLM Assistant")
+    user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
+    submit_button = gr.Button("Submit")
+    clear_button = gr.Button("Clear Chat")
+
+    state = gr.State([])  # Store chat history
+
+    def update_chat(user_message, history):
+        bot_response, updated_history = handle_user_input(user_message, history)
+        return updated_history, updated_history, ""
+
+    submit_button.click(
+        update_chat,
+        inputs=[user_input, state],
+        outputs=[chatbot, state, user_input]
+    )
+
+    clear_button.click(
+        lambda: ([], []),  # Clear chat history
+        outputs=[chatbot, state]
+    )
+
+# Launch the Gradio app
+demo.launch()