Update app.py

app.py

@@ -38,10 +38,8 @@ def get_weather(lat: float, lng: float) -> dict[str, str]:
     else: # Southern locations
         return {"temperature": "30°C", "description": "Very Sunny"}
 
-# --- Modified Agent Function ---
-# Change return type hint for clarity if desired, e.g., AsyncGenerator[str, None]
-# Or keep it simple, Gradio infers based on yields
-async def stream_from_agent(message: str, history: List[List[str]]) -> AsyncGenerator[str, None]:
+
+async def Answer_from_agent(message: str, history: List[List[str]]) -> AsyncGenerator[str, None]:
     """Processes message through LangChain agent, yielding intermediate steps as strings."""
 
     # Convert Gradio history to LangChain messages
@@ -59,7 +57,6 @@ async def stream_from_agent(message: str, history: List[List[str]]) -> AsyncGene
 
     lc_messages.append(HumanMessage(content=message))
 
-    # Initialize the agent (consider initializing outside the function if stateful across calls)
     llm = ChatOpenAI(temperature=0, model="gpt-4")
     memory = MemorySaver() # Be mindful of memory state if agent is re-initialized every time
     tools = [get_lat_lng, get_weather]
@@ -69,7 +66,7 @@ async def stream_from_agent(message: str, history: List[List[str]]) -> AsyncGene
     # Using a fixed one like "abc123" means all users share the same memory if server restarts aren't frequent
     thread_id = "user_session_" + str(os.urandom(4).hex()) # Example: generate unique ID
 
-    full_response = "" # Accumulate the response parts
+    full_response = ""
 
     async for chunk in agent_executor.astream_events(
         {"messages": lc_messages},
@@ -80,29 +77,21 @@ async def stream_from_agent(message: str, history: List[List[str]]) -> AsyncGene
         data = chunk["data"]
 
         if event == "on_chat_model_stream":
-            # Stream content from the LLM (final answer parts)
             content = data["chunk"].content
             if content:
                 full_response += content
-                yield full_response # Yield the accumulating final response
+                yield full_response 
 
         elif event == "on_tool_start":
-            # Show tool usage start
-            tool_input_str = str(data.get('input', '')) # Get tool input safely
+            tool_input_str = str(data.get('input', ''))
             yield f"🛠️ Using tool: **{data['name']}** with input: `{tool_input_str}`"
 
         elif event == "on_tool_end":
-             # Show tool result (optional, can make chat verbose)
-             tool_output_str = str(data.get('output', '')) # Get tool output safely
-             # Find the corresponding start message to potentially update, or just yield new message
-             # For simplicity, just yield the result as a new message line
+             tool_output_str = str(data.get('output', '')) 
              yield f"Tool **{data['name']}** finished.\nResult: `{tool_output_str}`"
-             # Yield the accumulated response again after tool use in case LLM continues
              if full_response:
                  yield full_response
 
-    # Ensure the final accumulated response is yielded if not already done by the last LLM chunk
-    # (stream might end on tool end sometimes)
     if full_response and (not chunk or chunk["event"] != "on_chat_model_stream"):
          yield full_response
 
@@ -110,16 +99,10 @@ async def stream_from_agent(message: str, history: List[List[str]]) -> AsyncGene
 # --- Gradio Interface (mostly unchanged) ---
 demo = gr.ChatInterface(
     fn=stream_from_agent,
-    # No type="messages" needed when yielding strings; ChatInterface handles it.
-    title="🌤️ Weather Assistant",
+    type="messages",
+    title="🤖 AGent template",
     description="Ask about the weather anywhere! Watch as I gather the information step by step.",
-    examples=[
-        ["What's the weather like in Tokyo?"],
-        ["Is it sunny in Paris right now?"],
-        ["Should I bring an umbrella in New York today?"]
-    ],
-    # Example icons removed for simplicity, ensure they are accessible if added back
-    cache_examples=False, # Turn off caching initially to ensure it's not the issue
+    cache_examples=False, 
     save_history=True,
     editable=True,
 )
@@ -127,22 +110,9 @@ demo = gr.ChatInterface(
 if __name__ == "__main__":
     # Load environment variables
     try:
-        from dotenv import load_dotenv
-        print("Attempting to load .env file...")
-        loaded = load_dotenv()
-        if loaded:
-            print(".env file loaded successfully.")
-        else:
-            print(".env file not found or empty.")
-        # Check if the key is loaded
         openai_api_key = os.getenv("OPENAI_API_KEY")
         if openai_api_key:
             print("OPENAI_API_KEY found.")
         else:
             print("Warning: OPENAI_API_KEY not found in environment variables.")
-    except ImportError:
-        print("dotenv not installed, skipping .env load.")
-        pass
-
-    # Add server_name="0.0.0.0" if running in Docker or need external access
     demo.launch(debug=True, server_name="0.0.0.0")