app.py

import os
import gradio as gr
import requests
import pandas as pd
import json
import re
from langchain_core.messages import HumanMessage
from langgraph_agent import build_graph

DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        self.graph = build_graph()

    def __call__(self, question: str, task_id: str = None) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        messages = [HumanMessage(content=question)]
        messages = self.graph.invoke({"messages": messages})

        if not messages or not messages.get('messages') or messages['messages'][-1].content is None:
            return "I am unable to determine the information using the available tools."

        answer = messages['messages'][-1].content

        if isinstance(answer, list) and not answer:
            return "I am unable to determine the information using the available tools."

        if not isinstance(answer, str):
            answer = str(answer)

        answer = answer.strip()

        match = re.search(r'FINAL ANSWER:\s*(.*)', answer, re.IGNORECASE | re.DOTALL)
        if match:
            final_answer = match.group(1).strip()
            if (final_answer.startswith('"') and final_answer.endswith('"')) or \
               (final_answer.startswith("'") and final_answer.endswith("'")):
                final_answer = final_answer[1:-1].strip()
            answer = final_answer
        else:
            print("Warning: 'FINAL ANSWER:' not found; submitting full answer.")

        if not answer:
            answer = "I am unable to determine the information using the available tools."

        return answer

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = profile.username
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    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 Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

    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:
            model_answer = agent(question_text, task_id=task_id).strip()
            # Clean trailing quotes if any
            if (model_answer.startswith('"') and model_answer.endswith('"')) or \
               (model_answer.startswith("'") and model_answer.endswith("'")):
                model_answer = model_answer[1:-1].strip()

            print(f"Answer for task {task_id}: '{model_answer}'")
            answers_payload.append({
                "task_id": task_id,
                "submitted_answer": model_answer
            })
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": model_answer
            })
        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            answers_payload.append({
                "task_id": task_id,
                "submitted_answer": f"AGENT ERROR: {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)

    data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }

    print(f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'...")
    print("Submission payload preview (first 3 answers):")
    for ans in answers_payload[:3]:
        print(json.dumps(ans, ensure_ascii=False))

    print(f"Submitting answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=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 Exception as e:
        status_message = f"Submission Failed: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    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).
        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 separate action or even to answer the questions asynchronously.
        """
    )

    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]
    )


if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    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(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 Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)