Switched to smolagent Framework

app.py

@@ -1,33 +1,24 @@
-import concurrent.futures
-import logging
 import os
-import sys
-import traceback
-
 import gradio as gr
-import pandas as pd
 import requests
-from dotenv import load_dotenv
-
-# from agent_gemini import BasicAgent
-from agent_mistral import BasicAgent
-# from agent_openrouter_llama import BasicAgent
-
-# Load environment variables from .env file
-load_dotenv()
-
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# Configure logging
-logging.basicConfig(
-    level=logging.DEBUG,
-    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-    handlers=[logging.StreamHandler(sys.stdout)],
-)
-logger = logging.getLogger(__name__)
+import inspect
+import pandas as pd
+from agent import BasicAgent
 
 # (Keep Constants as is)
 # --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+# class BasicAgent:
+#     def __init__(self):
+#         print("BasicAgent initialized.")
+#     def __call__(self, question: str) -> str:
+#         print(f"Agent received question (first 50 chars): {question[:50]}...")
+#         fixed_answer = "This is a default answer."
+#         print(f"Agent returning fixed answer: {fixed_answer}")
+#         return fixed_answer
 
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
@@ -35,249 +26,146 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
     """
-    try:
-        # --- 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}"
-            logger.info(f"User logged in: {username}")
-        else:
-            logger.warning("User not logged in.")
-            return "Please Login to Hugging Face with the button.", None
-
-        api_url = os.getenv("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)
-        try:
-            logger.info("Instantiating agent...")
-            agent = BasicAgent()
-            logger.info("Agent instantiated successfully")
-        except Exception as e:
-            logger.error(f"Error instantiating agent: {e}", exc_info=True)
-            return (
-                f"Error initializing agent: {str(e)}\n{traceback.format_exc()}",
-                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}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
 
-        # In the case of an app running as a hugging Face space, this link points toward your codebase
-        agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-        logger.info(f"Agent code: {agent_code}")
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
 
-        # 2. Fetch Questions
-        logger.info(f"Fetching questions from: {questions_url}")
+    # 1. Instantiate Agent ( modify this part to create your agent)
+    try:
+        agent = BasicAgent()
+    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"
+    print(agent_code)
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+            print("Fetched questions list is empty.")
+            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(f"Response text: {response.text[:500]}")
+        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...")
+    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
         try:
-            response = requests.get(questions_url, timeout=15)
-            response.raise_for_status()
-            questions_data = response.json()
-            if not questions_data:
-                logger.warning("Fetched questions list is empty.")
-                return (
-                    "Fetched questions list is empty or invalid format.",
-                    None,
-                )
-            logger.info(f"Fetched {len(questions_data)} questions.")
-        except requests.exceptions.RequestException as e:
-            logger.error(f"Error fetching questions: {e}", exc_info=True)
-            return f"Error fetching questions: {str(e)}", None
-        except requests.exceptions.JSONDecodeError as e:
-            logger.error(
-                f"Error decoding JSON response from questions endpoint: {e}",
-                exc_info=True,
+            submitted_answer = agent(question_text)
+            answers_payload.append(
+                {"task_id": task_id, "submitted_answer": submitted_answer}
             )
-            logger.error(f"Response text: {response.text[:500]}")
-            return (
-                f"Error decoding server response for questions: {str(e)}",
-                None,
+            results_log.append(
+                {
+                    "Task ID": task_id,
+                    "Question": question_text,
+                    "Submitted Answer": submitted_answer,
+                }
             )
         except Exception as e:
-            logger.error(
-                f"An unexpected error occurred fetching questions: {e}",
-                exc_info=True,
+            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}",
+                }
             )
-            return (
-                f"An unexpected error occurred fetching questions: {str(e)}",
-                None,
-            )
-
-        # 3. Run your Agent
-        results_log = []
-        answers_payload = []
-        logger.info(f"Running agent on {len(questions_data)} questions...")
-
-        # Limit the number of questions to process to avoid timeouts
-        max_questions = 20  # Process only 20 questions at a time
-
-        tasks_to_process = [
-            # "99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3",
-            # "1f975693-876d-457b-a649-393859e79bf3",
-            # "840bfca7-4f7b-481a-8794-c560c340185d",
-            # "7bd855d8-463d-4ed5-93ca-5fe35145f733",
-        ]
-
-        # questions_to_process = questions_data[:max_questions]
-
-        if tasks_to_process:
-            questions_to_process = [
-                x
-                for x in questions_data
-                if x.get("task_id") in tasks_to_process
-            ]
-        else:
-            questions_to_process = questions_data[:max_questions]
 
-        logger.info(
-            f"Processing {len(questions_to_process)} out of {len(questions_data)} questions"
+    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
         )
 
-        for item in questions_to_process:
-            task_id = item.get("task_id")
-            question_text = item.get("question")
-            if not task_id or question_text is None:
-                logger.warning(
-                    f"Skipping item with missing task_id or question: {item}"
-                )
-                continue
-            try:
-                logger.info(f"Processing task {task_id}: {question_text}")
-
-                # Use concurrent.futures for thread-safe timeout
-                with concurrent.futures.ThreadPoolExecutor() as executor:
-                    try:
-                        future = executor.submit(agent, question_text, task_id)
-                        try:
-                            submitted_answer = future.result(
-                                timeout=180
-                            )  # 60 second timeout
-                            logger.info(
-                                f"Answer for task {task_id}: {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,
-                                }
-                            )
-                        except concurrent.futures.TimeoutError:
-                            logger.error(f"Timeout processing task {task_id}")
-                            results_log.append(
-                                {
-                                    "Task ID": task_id,
-                                    "Question": question_text,
-                                    "Submitted Answer": "TIMEOUT ERROR: Question processing timed out after 60 seconds",
-                                }
-                            )
-                    finally:
-                        # Clean up temporary directory after processing
-                        try:
-                            import shutil
-
-                            # shutil.rmtree(temp_dir) ## TBD
-                            logger.info(
-                                f"Cleaned up temporary directory for task {task_id}"
-                            )
-                        except Exception as e:
-                            logger.error(
-                                f"Error cleaning up temporary directory for task {task_id}: {e}"
-                            )
-            except Exception as e:
-                logger.error(
-                    f"Error running agent on task {task_id}: {e}",
-                    exc_info=True,
-                )
-                results_log.append(
-                    {
-                        "Task ID": task_id,
-                        "Question": question_text,
-                        "Submitted Answer": f"AGENT ERROR: {str(e)}",
-                    }
-                )
-
-        if not answers_payload:
-            logger.warning("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}'..."
-        logger.info(status_update)
-
-        # 5. Submit
-        logger.info(
-            f"Submitting {len(answers_payload)} answers to: {submit_url}"
+    # 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:
-            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.')}"
-            )
-            logger.info("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}."
+            error_json = e.response.json()
+            error_detail += (
+                f" Detail: {error_json.get('detail', e.response.text)}"
             )
-            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}"
-            logger.error(status_message, exc_info=True)
-            results_df = pd.DataFrame(results_log)
-            return status_message, results_df
-        except requests.exceptions.Timeout:
-            status_message = "Submission Failed: The request timed out."
-            logger.error(status_message, exc_info=True)
-            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}"
-            logger.error(status_message, exc_info=True)
-            results_df = pd.DataFrame(results_log)
-            return status_message, results_df
-        except Exception as e:
-            status_message = (
-                f"An unexpected error occurred during submission: {e}"
-            )
-            logger.error(status_message, exc_info=True)
-            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
+    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
     except Exception as e:
-        logger.error(
-            f"Unhandled exception in run_and_submit_all: {e}", exc_info=True
-        )
-        return f"Critical error: {str(e)}\n{traceback.format_exc()}", None
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
 
 
 # --- Build Gradio Interface using Blocks ---
@@ -286,9 +174,11 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
+
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
         ---
         **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).
@@ -342,4 +232,4 @@ if __name__ == "__main__":
     print("-" * (60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=True)