Update app.py

app.py

@@ -1,3 +1,4 @@
+
 import os
 import gradio as gr
 import requests
@@ -12,6 +13,7 @@ load_dotenv()
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
+
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the LangGraph Agent on them, submits all answers,
@@ -19,6 +21,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID")  # Get the SPACE_ID for sending link to the code
+
     if profile:
         username = f"{profile.username}"
         print(f"User logged in: {username}")
@@ -38,8 +41,8 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     except Exception as e:
         print(f"Error instantiating agent graph: {e}")
         return f"Error initializing agent graph: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Agent code link unavailable (SPACE_ID not set)" 
+    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Agent code link unavailable (SPACE_ID not set)" # Added a check for SPACE_ID
     print(f"Agent code link: {agent_code}")
 
     # 2. Fetch Questions
@@ -68,33 +71,56 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
 
+    # Removed the problematic print statement from here
+
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
+
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
 
+        # Moved the print statement inside the loop, after task_id and question_text are assigned
         print(f"--- Starting processing Task ID: {task_id}, Question: {question_text[:100]}...")
+
         try:
+            # Invoke the LangGraph agent
             result_state = agent_graph.invoke({"messages": [HumanMessage(content=question_text)]})
-            submitted_answer = "Error: Agent did not provide a response."
+
+            # Extract the final answer from the last message
+            submitted_answer = "Error: Agent did not provide a response."  # Default in case extraction fails
+
             if result_state and "messages" in result_state and result_state["messages"]:
                 last_message = result_state["messages"][-1]
+                # The final content is typically in the content attribute of the last message
                 if hasattr(last_message, 'content') and last_message.content:
                     submitted_answer = last_message.content
+                # else: Handle cases where the last message might be a tool message etc.,
+                # for simplicity, we just use the default error message if content is missing.
+
+            # Ensure submitted_answer is a string
             if not isinstance(submitted_answer, str):
                 submitted_answer = str(submitted_answer)
+
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+
+            # Moved this print statement inside the loop as well
             print(f"--- Finished processing Task ID: {task_id}")
+            # Moved this print statement inside the loop as well
             print(f"--- Extracted answer for Task ID: {task_id}: {submitted_answer[:100]}...")
+
+
         except Exception as e:
             print(f"Error running agent graph on task {task_id}: {e}")
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            # Note: If an error occurs, the 'Finished' and 'Extracted answer' prints for this specific task won't happen,
+            # which is reasonable behavior.
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
+        # Even if no answers, show the log of errors
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
     # 4. Prepare Submission
@@ -118,6 +144,22 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         print("Submission successful.")
         results_df = pd.DataFrame(results_log)
         return final_status, results_df
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with status {e.response.status_code}."
+        try:
+            error_json = e.response.json()
+            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+        except requests.exceptions.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
     except requests.exceptions.RequestException as e:
         status_message = f"Submission Failed: Network error - {e}"
         print(status_message)
@@ -129,27 +171,58 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
+
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# LangGraph Agent Evaluation Runner")
+    gr.Markdown("# LangGraph Agent Evaluation Runner")  # Updated title
     gr.Markdown(
         """
         **Instructions:**
 
-        1. Clone this space, modify the code in `agent.py` and `app.py` to define your agent's logic.
-        2. Ensure `DEEPSEEK_API_KEY` is set as a Space Secret.
-        3. Log in to Hugging Face using the button below.
-        4. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, and submit answers.
+        1.  Please clone this space, then modify the code in `agent.py` and `app.py` to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  **Make sure you have your `DEEPSEEK_API_KEY` set as a Space Secret.**
+        3.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        4.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
 
-        **Disclaimer**: Submissions may take time to process.
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
         """
     )
+
     gr.LoginButton()
+
     run_button = gr.Button("Run Evaluation & Submit All Answers")
+
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
 
 if __name__ == "__main__":
-    print("Launching Gradio Interface for LangGraph Agent Evaluation...")
+    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup:  # Print repo URLs if SPACE_ID is found
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-" * (60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for LangGraph Agent Evaluation...")  # Updated message
     demo.launch(debug=True, share=False, auth=None)