Update app.py

app.py

@@ -313,8 +313,7 @@ def tavily_search(query: str) -> str:
     try:
         result = tavily.search(query=query, search_depth="advanced")
         return f"Search results for '{query}':\n" + "\n".join([f"- {r['content']}" for r in result['results']])
-    except Exception as e:
-        return f"Error during Tavily search: {e}"
+    except Exception as e: return f"Error during Tavily search: {e}"
 
 @tool
 def read_file(url: str) -> str:
@@ -325,7 +324,6 @@ def read_file(url: str) -> str:
         response = requests.get(url)
         response.raise_for_status()
         with open(filename, 'wb') as f: f.write(response.content)
-
         if url.lower().endswith('.pdf'):
             try:
                 pdf_reader = pypdf.PdfReader(filename)
@@ -349,7 +347,7 @@ def python_interpreter(code: str) -> str:
 
 #
 # ================================================================================================
-#  ✅ 2. CONFIGURE AND BUILD THE AGENT GRAPH (MANUAL LANGGRAPH IMPLEMENTATION)
+#  ✅ 2. CONFIGURE AND BUILD THE AGENT GRAPH
 # ================================================================================================
 #
 class AgentState(TypedDict):
@@ -361,43 +359,33 @@ def build_agent_graph():
     tool_map = {tool.name: tool for tool in tools}
 
     repo_id = "CohereForAI/c4ai-command-r-plus"
+    
+    # <<<--- CHANGE 1: Explicitly set `task="conversational"` --->>>
+    # This is the crucial fix. We are telling the endpoint to use the correct API pipeline.
     llm = HuggingFaceEndpoint(
-        repo_id=repo_id, max_new_tokens=1024, temperature=0,
+        repo_id=repo_id,
+        task="conversational", # This is the key fix!
+        max_new_tokens=1024,
+        temperature=0.1,
         huggingfacehub_api_token=os.getenv("HUGGINGFACEHUB_API_TOKEN")
     )
 
     def call_model(state: AgentState):
-        """Invokes the LLM and wraps the response in an AIMessage."""
-        messages = state['messages']
-        # We combine the system prompt with the rest of the conversation.
-        prompt_str = ""
-        for msg in messages:
-            if isinstance(msg, SystemMessage):
-                prompt_str += f"<|SYSTEM|>\n{msg.content}\n"
-            elif isinstance(msg, HumanMessage):
-                prompt_str += f"<|USER|>\n{msg.content}\n"
-            elif isinstance(msg, AIMessage):
-                prompt_str += f"<|ASSISTANT|>\n{msg.content}\n"
-            elif isinstance(msg, ToolMessage):
-                prompt_str += f"<|TOOL_RESULT|>\n{msg.content}\n"
-        
-        prompt_str += "<|ASSISTANT|>"
-
-        response_text = llm.invoke(prompt_str)
-        return {"messages": [AIMessage(content=response_text)]}
+        """Invokes the LLM using the conversational task."""
+        # <<<--- CHANGE 2: The conversational task takes a list of messages directly --->>>
+        # This is cleaner and the correct way to use this pipeline.
+        response = llm.invoke(state['messages'])
+        return {"messages": [response]}
 
     def should_continue(state: AgentState) -> str:
         """Determines whether to call a tool or end the loop."""
         last_message_content = state['messages'][-1].content.strip()
-        
-        # A simple check: if the response looks like a JSON object, it's a tool call.
         if last_message_content.startswith('{') and last_message_content.endswith('}'):
-             # More robust check for JSON tool call
             try:
                 json.loads(last_message_content)
                 return "action"
             except json.JSONDecodeError:
-                return "end" # It's not valid JSON, so it must be the final answer
+                return "end"
         else:
             return "end"
 
@@ -410,16 +398,14 @@ def build_agent_graph():
             parameters = tool_call_data.get("parameters", {})
 
             if tool_name not in tool_map:
-                error_message = f"Error: Tool '{tool_name}' not found."
-                return {"messages": [ToolMessage(content=error_message, tool_call_id="error")]}
+                return {"messages": [ToolMessage(content=f"Error: Tool '{tool_name}' not found.", tool_call_id="error")]}
 
             selected_tool = tool_map[tool_name]
             tool_output = selected_tool.invoke(parameters)
             return {"messages": [ToolMessage(content=str(tool_output), tool_call_id=tool_name)]}
 
         except Exception as e:
-            error_message = f"Error processing tool call: {e}. Content: '{last_message_content}'"
-            return {"messages": [ToolMessage(content=error_message, tool_call_id="error")]}
+            return {"messages": [ToolMessage(content=f"Error processing tool call: {e}. Content: '{last_message_content}'", tool_call_id="error")]}
 
     workflow = StateGraph(AgentState)
     workflow.add_node("agent", call_model)
@@ -436,23 +422,16 @@ def build_agent_graph():
 #
 class GaiaAgent:
     def __init__(self):
-        print("GaiaAgent initialized. Building Command R+ agent with manual LangGraph loop...")
+        print("GaiaAgent initialized. Building Command R+ agent with 'conversational' task...")
         self.agent_app = build_agent_graph()
 
     def __call__(self, question: str) -> str:
         print(f"\n{'='*60}\nAgent received question: {question[:100]}...\n{'='*60}")
-        initial_input = {
-            "messages": [
-                SystemMessage(content=AGENT_SYSTEM_PROMPT),
-                HumanMessage(content=question)
-            ]
-        }
+        initial_input = {"messages": [SystemMessage(content=AGENT_SYSTEM_PROMPT), HumanMessage(content=question)]}
         final_state = None
         for i, step in enumerate(self.agent_app.stream(initial_input, {"recursion_limit": 15})):
             if i == 0: print("--- Starting Agentic Loop ---")
-            print(f"--- Step {i+1} ---")
-            print(step)
-            final_state = list(step.values())[0] # Get the state from the graph step
+            final_state = list(step.values())[0]
 
         final_answer_message = final_state['messages'][-1]
         final_answer = str(final_answer_message.content).strip()
@@ -465,26 +444,21 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     if not profile: return "Please Login to Hugging Face with the button.", None
     username = f"{profile.username}"
     print(f"User logged in: {username}")
-
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
-
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(f"Fetching questions from: {questions_url}")
+
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
-        print(f"Fetched {len(questions_data)} questions.")
     except Exception as e:
         return f"An unexpected error occurred fetching questions: {e}", None
 
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-
-    agent_instance = GaiaAgent() # Instantiate the agent once
+    agent_instance = GaiaAgent()
 
     for item in questions_data:
         task_id = item.get("task_id")
@@ -502,7 +476,6 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
         response = requests.post(submit_url, json=submission_data, timeout=90)
         response.raise_for_status()
@@ -514,19 +487,16 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
             f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
             f"Message: {result_data.get('message', 'No message received.')}"
         )
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
+        return final_status, pd.DataFrame(results_log)
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
+        return f"An unexpected error occurred during submission: {e}", pd.DataFrame(results_log)
 
 with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Agent Final Assessment (Open Source: Command R+ - Final)")
+    gr.Markdown("# GAIA Agent Final Assessment (Open Source: Command R+ - Corrected Task)")
     gr.Markdown(
         """
-        **Instructor's Note:** This version uses a robust, manual LangGraph loop to handle tool calls for the `HuggingFaceEndpoint`.
-        It explicitly tells the model to generate JSON for tool calls and parses this JSON from the text output. This is the correct, fundamental way to build agents with models that don't support modern tool-binding abstractions.
+        **Instructor's Note:** This version corrects the `HuggingFaceEndpoint` invocation by specifying `task="conversational"`.
+        This is the final key required to make the Command R+ model work correctly with the Hugging Face Inference API for our agent.
         """
     )
     gr.LoginButton()