Upload app.py

app.py

@@ -18,11 +18,11 @@ ENABLE_DETAILED_LOGGING = True
 if ENABLE_DETAILED_LOGGING:
     # Create formatter
     formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
-
+    
     # Setup console handler
     console_handler = logging.StreamHandler()
     console_handler.setFormatter(formatter)
-
+    
     # Setup rotating file handler (7 days, daily rotation)
     file_handler = logging.handlers.TimedRotatingFileHandler(
         'agent.log',
@@ -32,7 +32,7 @@ if ENABLE_DETAILED_LOGGING:
         encoding='utf-8'
     )
     file_handler.setFormatter(formatter)
-
+    
     # Configure root logger
     logging.basicConfig(
         level=logging.INFO,
@@ -51,8 +51,6 @@ llm_model = os.environ.get('model')
 
 # Tavily API configuration
 tavily_key = os.environ.get('tavily_key', '')
-if tavily_key:
-    os.environ['TAVILY_API_KEY'] = tavily_key
 
 # Tavily search tool integration
 
@@ -64,7 +62,7 @@ class ReactAgentChat:
         self.model = model
         self.agent = None
         self._setup_agent()
-
+    
     def _setup_agent(self):
         """Initialize the LangGraph ReAct agent"""
         try:
@@ -72,7 +70,7 @@ class ReactAgentChat:
                 logger.info(f"=== SETTING UP AGENT ===")
                 logger.info(f"LLM URL: http://{self.ip}:{self.port}/v1")
                 logger.info(f"Model: {self.model}")
-
+            
             # Create OpenAI-compatible model
             llm = ChatOpenAI(
                 base_url=f"http://{self.ip}:{self.port}/v1",
@@ -82,7 +80,7 @@ class ReactAgentChat:
             )
             if ENABLE_DETAILED_LOGGING:
                 logger.info("LLM created successfully")
-
+            
             # Define tools - use Tavily search API with graceful error handling
             if tavily_key:
                 if ENABLE_DETAILED_LOGGING:
@@ -94,6 +92,7 @@ class ReactAgentChat:
                         """Search the web for current information about any topic."""
                         try:
                             tavily_tool = TavilySearch(
+                                api_key=tavily_key,
                                 max_results=5,
                                 topic="general",
                                 include_answer=True,
@@ -107,7 +106,7 @@ class ReactAgentChat:
                             error_str = str(e).lower()
                             if ENABLE_DETAILED_LOGGING:
                                 logger.error(f"Tavily search failed for query '{query}': {e}")
-
+                            
                             # Check for rate limit or quota issues
                             if any(keyword in error_str for keyword in ['rate limit', 'quota', 'limit exceeded', 'usage limit', 'billing']):
                                 if ENABLE_DETAILED_LOGGING:
@@ -117,7 +116,7 @@ class ReactAgentChat:
                                 if ENABLE_DETAILED_LOGGING:
                                     logger.error(f"Tavily API error: {e}")
                                 return "I can't search the web right now."
-
+                    
                     search_tool = web_search
                     if ENABLE_DETAILED_LOGGING:
                         logger.info("Tavily search tool wrapper created successfully")
@@ -140,49 +139,49 @@ class ReactAgentChat:
                         logger.error("Search attempted but no Tavily API key configured")
                     return "I can't search the web right now."
                 search_tool = no_search
-
+            
             tools = [search_tool]
             if ENABLE_DETAILED_LOGGING:
                 logger.info(f"Tools defined: {[tool.name for tool in tools]}")
-
+            
             # Bind tools to the model
             model_with_tools = llm.bind_tools(tools)
             if ENABLE_DETAILED_LOGGING:
                 logger.info("Tools bound to model")
-
+            
             # Create the ReAct agent
             self.agent = create_react_agent(model_with_tools, tools)
             if ENABLE_DETAILED_LOGGING:
                 logger.info("ReAct agent created successfully")
-
+            
         except Exception as e:
             logger.error(f"=== AGENT SETUP ERROR ===")
             logger.error(f"Failed to setup agent: {e}")
             import traceback
             logger.error(f"Traceback: {traceback.format_exc()}")
             raise e
-
+    
     def update_config(self, ip: str, port: str, api_key: str, model: str):
         """Update LLM configuration"""
-        if (ip != self.ip or port != self.port or
+        if (ip != self.ip or port != self.port or 
             api_key != self.api_key or model != self.model):
             self.ip = ip
             self.port = port
             self.api_key = api_key
             self.model = model
             self._setup_agent()
-
+    
     def chat(self, message: str, history: List[List[str]]) -> str:
         """Generate chat response using ReAct agent"""
         try:
             if not self.agent:
                 return "Error: Agent not initialized"
-
+            
             if ENABLE_DETAILED_LOGGING:
                 logger.info(f"=== USER INPUT ===")
                 logger.info(f"Message: {message}")
                 logger.info(f"History length: {len(history)}")
-
+            
             # Convert history to messages for context handling
             messages = []
             for user_msg, assistant_msg in history:
@@ -190,33 +189,33 @@ class ReactAgentChat:
                 if assistant_msg:  # Only add if assistant responded
                     from langchain_core.messages import AIMessage
                     messages.append(AIMessage(content=assistant_msg))
-
+            
             # Add current message
             messages.append(HumanMessage(content=message))
-
+            
             # Invoke the agent
             if ENABLE_DETAILED_LOGGING:
                 logger.info(f"=== INVOKING AGENT ===")
                 logger.info(f"Total messages in history: {len(messages)}")
             response = self.agent.invoke({"messages": messages})
-
+            
             if ENABLE_DETAILED_LOGGING:
                 logger.info(f"=== AGENT RESPONSE ===")
                 logger.info(f"Full response: {response}")
                 logger.info(f"Number of messages: {len(response.get('messages', []))}")
-
+                
                 # Log each message in the response
                 for i, msg in enumerate(response.get("messages", [])):
                     logger.info(f"Message {i}: Type={type(msg).__name__}, Content={getattr(msg, 'content', 'No content')}")
-
+            
             # Extract the final response
             final_message = response["messages"][-1].content
             if ENABLE_DETAILED_LOGGING:
                 logger.info(f"=== FINAL MESSAGE ===")
                 logger.info(f"Final message: {final_message}")
-
+            
             return final_message
-
+                
         except Exception as e:
             error_msg = f"Agent error: {str(e)}"
             logger.error(f"=== AGENT ERROR ===")
@@ -229,25 +228,25 @@ class ReactAgentChat:
 # Global agent instance
 react_agent = ReactAgentChat(llm_ip, llm_port, llm_key, llm_model)
 
-def generate_response(message: str, history: List[List[str]], system_prompt: str,
+def generate_response(message: str, history: List[List[str]], system_prompt: str, 
                      max_tokens: int, ip: str, port: str, api_key: str, model: str):
     """Generate response using ReAct agent"""
     global react_agent
-
+    
     try:
         # Update agent configuration if changed
         react_agent.update_config(ip, port, api_key, model)
-
+        
         # Generate response
         response = react_agent.chat(message, history)
-
+        
         # Stream the response word by word for better UX
         words = response.split()
         current_response = ""
         for word in words:
             current_response += word + " "
             yield current_response.strip()
-
+            
     except Exception as e:
         error_msg = f"Error: {str(e)}"
         logger.error(error_msg)
@@ -261,31 +260,32 @@ chatbot = gr.ChatInterface(
             None,
             "https://cdn-avatars.huggingface.co/v1/production/uploads/64e6d37e02dee9bcb9d9fa18/o_HhUnXb_PgyYlqJ6gfEO.png"
         ],
-        height="64vh"
+        height="64vh",
+        type="messages"
     ),
     additional_inputs=[
         gr.Textbox(
-            "You are a helpful AI assistant with web search capabilities.",
+            "You are a helpful AI assistant with web search capabilities.", 
             label="System Prompt",
             lines=2
         ),
-        gr.Slider(50, 2048, label="Max Tokens", value=512,
+        gr.Slider(50, 2048, label="Max Tokens", value=512, 
                  info="Maximum number of tokens in the response"),
-        gr.Textbox(llm_ip, label="LLM IP Address",
+        gr.Textbox(llm_ip, label="LLM IP Address", 
                   info="IP address of the OpenAI-compatible LLM server"),
-        gr.Textbox(llm_port, label="LLM Port",
+        gr.Textbox(llm_port, label="LLM Port", 
                   info="Port of the LLM server"),
         gr.Textbox(llm_key, label="API Key", type="password",
                   info="API key for the LLM server"),
-        gr.Textbox(llm_model, label="Model Name",
+        gr.Textbox(llm_model, label="Model Name", 
                   info="Name of the model to use"),
     ],
     title="🤖 LangGraph ReAct Agent with DuckDuckGo Search",
     description="Chat with a LangGraph ReAct agent that can search the web using DuckDuckGo. Ask about current events, research topics, or any questions that require up-to-date information!",
     theme="finlaymacklon/smooth_slate",
     submit_btn="Send",
-    # retry_btn="🔄 Regenerate Response",
-    # undo_btn="↩ Delete Previous",
+    retry_btn="🔄 Regenerate Response",
+    undo_btn="↩ Delete Previous",
     clear_btn="🗑️ Clear Chat"
 )