app.py

"""
GRADIO INTERFACE FOR LANGGRAPH AI AGENT

Features:
- Interactive Q&A with AI agent
- Support for task_id and file uploads
- Real-time processing with Qwen3-8B
- Beautiful UI với LangGraph workflow visualization
"""

import os
import gradio as gr
import requests
import pandas as pd

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

# --- Basic Agent Definition ---
# ----- THIS IS WHERE 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):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # Determine HF Space Runtime URL and Repo URL
    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"

    # Instantiate Agent
    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)

    # Fetch Questions
    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.", 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

    # Run Agent on each question
    results_log = []
    answers_payload = []
    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:
            continue
        try:
            submitted_answer = agent(question_text)
            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 on task {task_id}: {e}")
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"ERROR: {e}"})

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "No answers to submit.", pd.DataFrame(results_log)

    # Submit answers
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    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"Score: {result_data.get('score', 'N/A')}%\n"
            f"Message: {result_data.get('message', '')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        print(f"Submission failed: {e}")
        return f"Submission failed: {e}", pd.DataFrame(results_log)

# --- Build Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1. Log in with your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to fetch, run, and submit.
        """
    )

    login_btn = 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,
        inputs=[login_btn],
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    demo.launch(debug=True, share=False)