Create app.py

app.py

@@ -466,6 +466,152 @@ Answer:"""
 
 # --- Gradio Interface Setup ---
 # Define the Gradio interface
+
+# MOVIDO PARA CIMA: A função run_and_submit_all precisa ser definida antes de ser usada no Gradio.
+def run_and_submit_all( profile: gr.OAuthProfile | None, other_arg=None): # Modified to accept 2 arguments
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    print("run_and_submit_all function started.") # Debugging print at function start
+    # --- 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}")
+    else:
+        print("User not logged in.")
+        # --- SIMULAÇÃO PARA COLAB ---
+        username = "colab_test_user" # <--- Adicione esta linha para simular um usuário no Colab
+        print(f"User not logged in, simulating for Colab: {username}")
+        # return "Please Login to Hugging Face with the button.", None # Comente esta linha para testar no Colab
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent ( modify this part to create your agent)
+    print("Attempting to instantiate BasicAgent...") # Debugging print before instantiation
+    try:
+        agent = BasicAgent()
+        print("BasicAgent instantiated successfully.") # Debugging print after instantiation
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    # 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 "No SPACE_ID found, cannot generate agent_code URL."
+    print(agent_code)
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    questions_data = None # Initialize to None
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        # Add check for empty or non-list questions_data immediately after fetching
+        if not isinstance(questions_data, list) or not questions_data:
+             print(f"Fetched questions_data is empty or not a list. Type: {type(questions_data)}")
+             return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
+    except requests.exceptions.JSONDecodeError as e:
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         # Print the response text for debugging if JSON decoding fails
+         print(f"Response text: {response.text[:500] if 'response' in locals() else 'No response object'}")
+         return f"Error decoding server response for questions: {e}", None
+    except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
+
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    # The check that questions_data is a list is now done immediately after fetching
+    for item in questions_data:
+        # Add check for None or non-dict item before accessing keys
+        if item is None or not isinstance(item, dict):
+            print(f"Skipping invalid item in questions_data: {item}")
+            continue
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or not isinstance(task_id, (str, int)) or not question_text or not isinstance(question_text, str):
+            print(f"Skipping item with missing or invalid task_id or question: {item}")
+            continue
+        print(f"Processing Task ID: {task_id}") # Debugging task ID
+        try:
+            # Here, we only pass the question text for now, as the API doesn't support video input
+            # The video processing logic is added but not triggered by this function
+            submitted_answer = agent(question_text)
+            print(f"Agent returned answer for {task_id}: {submitted_answer[:50]}...") # Debugging returned 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})
+        except Exception as e:
+             print(f"Error running agent on task {task_id}: {e}")
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    # 4. Prepare Submission
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"Submission Successful!\n"
+            f"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', 'No message received.')}"
+        )
+        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)}"
+            status_message = f"Submission Failed: {error_detail}"
+            print(status_message)
+            # If submission fails, also return the results log so the user can see what was attempted
+            results_df = pd.DataFrame(results_log)
+            return status_message, results_df
+        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
+        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)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+
 with gr.Blocks() as demo:
     gr.Markdown(
         """