bitcloud2/Final_Assignment_Template

1import os2import gradio as gr3import requests4import inspect5import pandas as pd6 7from my_agent import SmolAgent  # Import the new agent8 9# (Keep Constants as is)10# --- Constants ---11DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"12 13 14# --- Basic Agent Definition ---15# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------16class BasicAgent:17    def __init__(self):18        print("BasicAgent initialized.")19 20    def __call__(self, question: str) -> str:21        print(f"Agent received question (first 50 chars): {question[:50]}...")22        fixed_answer = "This is a default answer."23        print(f"Agent returning fixed answer: {fixed_answer}")24        return fixed_answer25 26 27def instantiate_agent():28    """Instantiates the agent."""29    try:30        # agent = BasicAgent()31        agent = SmolAgent()  # Use the new agent32        return agent, None  # Return agent and no error33    except Exception as e:34        print(f"Error instantiating agent: {e}")35        return None, f"Error initializing agent: {e}"  # Return None and error message36 37 38def fetch_questions(questions_url: str):39    """Fetches questions from the specified URL."""40    print(f"Fetching questions from: {questions_url}")41    try:42        response = requests.get(questions_url, timeout=15)43        response.raise_for_status()44        questions_data = response.json()45        if not questions_data:46            print("Fetched questions list is empty.")47            return None, "Fetched questions list is empty or invalid format."48        print(f"Fetched {len(questions_data)} questions.")49        return questions_data, None  # Return data and no error50    except requests.exceptions.RequestException as e:51        print(f"Error fetching questions: {e}")52        return None, f"Error fetching questions: {e}"53    except requests.exceptions.JSONDecodeError as e:54        print(f"Error decoding JSON response from questions endpoint: {e}")55        print(f"Response text: {response.text[:500]}")56        return None, f"Error decoding server response for questions: {e}"57    except Exception as e:58        print(f"An unexpected error occurred fetching questions: {e}")59        return None, f"An unexpected error occurred fetching questions: {e}"60 61 62def run_agent_on_questions(agent, questions_data):63    """Runs the agent on each question and collects results."""64    results_log = []65    answers_payload = []66    print(f"Running agent on {len(questions_data)} questions...")67    for item in questions_data:68        task_id = item.get("task_id")69        question_text = item.get("question")70        if not task_id or question_text is None:71            print(f"Skipping item with missing task_id or question: {item}")72            continue73        try:74            submitted_answer = agent(question_text)75            answers_payload.append(76                {"task_id": task_id, "submitted_answer": submitted_answer}77            )78            results_log.append(79                {80                    "Task ID": task_id,81                    "Question": question_text,82                    "Submitted Answer": submitted_answer,83                }84            )85        except Exception as e:86            print(f"Error running agent on task {task_id}: {e}")87            results_log.append(88                {89                    "Task ID": task_id,90                    "Question": question_text,91                    "Submitted Answer": f"AGENT ERROR: {e}",92                }93            )94    return answers_payload, results_log95 96 97def dev_run():98    """99    Fetches all questions, runs the BasicAgent on them,100    and displays the results.101    """102    api_url = DEFAULT_API_URL103    questions_url = f"{api_url}/questions"104 105    agent, error_message = instantiate_agent()106    if error_message:107        return error_message, None  # Return error message and None for results_df108 109    # 2. Fetch Questions110    questions_data, error_message = fetch_questions(questions_url)111    if error_message:112        # Return the error message from fetch_questions and None for the results DataFrame113        return error_message, None114 115    # 3. Run your Agent116    answers_payload, results_log = run_agent_on_questions(agent, questions_data)117 118    if not answers_payload:119        print("Agent did not produce any answers to submit.")120        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)121 122    return answers_payload, pd.DataFrame(results_log)123 124 125def run_and_submit_all(profile: gr.OAuthProfile | None):126    """127    Fetches all questions, runs the BasicAgent on them, submits all answers,128    and displays the results.129    """130    # --- Determine HF Space Runtime URL and Repo URL ---131    space_id = os.getenv("SPACE_ID")  # Get the SPACE_ID for sending link to the code132 133    if profile:134        username = f"{profile.username}"135        print(f"User logged in: {username}")136    else:137        print("User not logged in.")138        return "Please Login to Hugging Face with the button.", None139 140    api_url = DEFAULT_API_URL141    questions_url = f"{api_url}/questions"142    submit_url = f"{api_url}/submit"143 144    # 1. Instantiate Agent ( modify this part to create your agent)145    agent, error_message = instantiate_agent()146    if error_message:147        return error_message, None  # Return error message and None for results_df148 149    # 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)150    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"151    print(agent_code)152 153    # 2. Fetch Questions154    questions_data, error_message = fetch_questions(questions_url)155    if error_message:156        # Return the error message from fetch_questions and None for the results DataFrame157        return error_message, None158 159    # 3. Run your Agent160    answers_payload, results_log = run_agent_on_questions(agent, questions_data)161 162    if not answers_payload:163        print("Agent did not produce any answers to submit.")164        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)165 166    # 4. Prepare Submission167    submission_data = {168        "username": username.strip(),169        "agent_code": agent_code,170        "answers": answers_payload,171    }172    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."173    print(status_update)174 175    # 5. Submit176    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")177    try:178        response = requests.post(submit_url, json=submission_data, timeout=60)179        response.raise_for_status()180        result_data = response.json()181        final_status = (182            f"Submission Successful!\n"183            f"User: {result_data.get('username')}\n"184            f"Overall Score: {result_data.get('score', 'N/A')}% "185            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"186            f"Message: {result_data.get('message', 'No message received.')}"187        )188        print("Submission successful.")189        results_df = pd.DataFrame(results_log)190        return final_status, results_df191    except requests.exceptions.HTTPError as e:192        error_detail = f"Server responded with status {e.response.status_code}."193        try:194            error_json = e.response.json()195            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"196        except requests.exceptions.JSONDecodeError:197            error_detail += f" Response: {e.response.text[:500]}"198        status_message = f"Submission Failed: {error_detail}"199        print(status_message)200        results_df = pd.DataFrame(results_log)201        return status_message, results_df202    except requests.exceptions.Timeout:203        status_message = "Submission Failed: The request timed out."204        print(status_message)205        results_df = pd.DataFrame(results_log)206        return status_message, results_df207    except requests.exceptions.RequestException as e:208        status_message = f"Submission Failed: Network error - {e}"209        print(status_message)210        results_df = pd.DataFrame(results_log)211        return status_message, results_df212    except Exception as e:213        status_message = f"An unexpected error occurred during submission: {e}"214        print(status_message)215        results_df = pd.DataFrame(results_log)216        return status_message, results_df217 218 219# # --- Build Gradio Interface using Blocks ---220# with gr.Blocks() as demo:221#     gr.Markdown("# Basic Agent Evaluation Runner")222#     gr.Markdown(223#         """224#         **Instructions:**225 226#         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...227#         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.228#         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.229 230#         ---231#         **Disclaimers:**232#         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).233#         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.234#         """235#     )236 237#     gr.LoginButton()238 239#     run_button = gr.Button("Run Evaluation & Submit All Answers")240 241#     status_output = gr.Textbox(242#         label="Run Status / Submission Result", lines=5, interactive=False243#     )244#     # Removed max_rows=10 from DataFrame constructor245#     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)246 247#     run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])248 249# if __name__ == "__main__":250#     print("\n" + "-" * 30 + " App Starting " + "-" * 30)251#     # Check for SPACE_HOST and SPACE_ID at startup for information252#     space_host_startup = os.getenv("SPACE_HOST")253#     space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup254 255#     if space_host_startup:256#         print(f"✅ SPACE_HOST found: {space_host_startup}")257#         print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")258#     else:259#         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")260 261#     if space_id_startup:  # Print repo URLs if SPACE_ID is found262#         print(f"✅ SPACE_ID found: {space_id_startup}")263#         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")264#         print(265#             f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main"266#         )267#     else:268#         print(269#             "ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."270#         )271 272#     print("-" * (60 + len(" App Starting ")) + "\n")273 274#     print("Launching Gradio Interface for Basic Agent Evaluation...")275#     demo.launch(debug=True, share=False)276 277if __name__ == "__main__":278    answers_payload, results_log = dev_run()279    print(answers_payload)280    print(results_log)281