Update langgraph_agent.py

langgraph_agent.py

@@ -159,23 +159,6 @@ def describe_image(image_path: str) -> Dict[str, str]:
     except Exception as e:
         return {"error": f"Error describing image {image_path}: {str(e)}"}
 
-@tool
-def transcribe_audio(audio_path: str) -> Dict[str, str]:
-    """
-    Transcribes an audio file (e.g., MP3) to text using an automatic speech recognition model
-    from the Hugging Face Inference API. Requires HF_API_TOKEN environment variable to be set.
-    """
-    if not HF_INFERENCE_CLIENT:
-        return {"error": "Hugging Face API token not configured for audio transcription. Cannot use this tool."}
-    try:
-        with open(audio_path, "rb") as f:
-            audio_bytes = f.read()
-        transcription = HF_INFERENCE_CLIENT.automatic_speech_recognition(audio_bytes)
-        return {"audio_transcription": transcription, "audio_path": audio_path}
-    except FileNotFoundError:
-        return {"error": f"Audio file not found: {audio_path}. Please ensure the file exists."}
-    except Exception as e:
-        return {"error": f"Error transcribing audio {audio_path}: {str(e)}"}
 
 
 API_KEY = os.getenv("GEMINI_API_KEY")
@@ -189,7 +172,6 @@ tools = [
     read_file_content,
     python_interpreter,
     describe_image,
-    transcribe_audio,
 ]
 
 
@@ -222,8 +204,10 @@ def build_graph(provider: str = "gemini"):
     llm_with_tools = llm.bind_tools(tools)
 
     def assistant(state: MessagesState):
-        messages_to_send = [sys_msg] + state["messages"]
-        return {"messages": [llm_with_tools.invoke(messages_to_send)]}
+    messages_to_send = [sys_msg] + state["messages"]
+    llm_response = llm_with_tools.invoke(messages_to_send)
+    print(f"LLM Raw Response: {llm_response}") # Add this line
+    return {"messages": [llm_response]}
 
     builder = StateGraph(MessagesState)
     builder.add_node("assistant", assistant)