app.py

import os
import gradio as gr
import requests
import pandas as pd
from transformers import pipeline

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
HF_MODEL_NAME = "facebook/bart-large-mnli"  # Free model that works in Spaces

# --- Agent Definition ---
class BasicAgent:
    def __init__(self):
        print("Initializing Agent...")
        try:
            self.llm = pipeline(
                "text-generation",
                model=HF_MODEL_NAME,
                device_map="auto"
            )
        except Exception as e:
            print(f"LLM initialization failed: {e}")
            self.llm = None

    def __call__(self, question: str) -> str:
        if not self.llm:
            return "Default answer (LLM not available)"
        
        try:
            response = self.llm(question, max_length=100)
            return response[0]['generated_text']
        except Exception as e:
            return f"Error: {str(e)}"

def run_and_submit_all():
    """Simplified version that works with Gradio auth"""
    # Get username from Gradio's auth system
    username = os.getenv("GRADIO_AUTH_USERNAME")
    if not username:
        return "Please login first", None

    space_id = os.getenv("SPACE_ID")
    api_url = DEFAULT_API_URL
    agent = BasicAgent()
    
    # Fetch questions
    try:
        response = requests.get(f"{api_url}/questions", timeout=15)
        questions = response.json()
    except Exception as e:
        return f"Failed to get questions: {str(e)}", None

    # Process questions
    results = []
    answers = []
    for q in questions:
        try:
            answer = agent(q.get("question", ""))
            answers.append({
                "task_id": q.get("task_id"),
                "submitted_answer": answer
            })
            results.append({
                "Task ID": q.get("task_id"),
                "Question": q.get("question"),
                "Answer": answer
            })
        except Exception as e:
            results.append({
                "Task ID": q.get("task_id"),
                "Question": q.get("question"),
                "Answer": f"Error: {str(e)}"
            })

    # Submit answers
    try:
        response = requests.post(
            f"{api_url}/submit",
            json={
                "username": username,
                "agent_code": f"https://huggingface.co/spaces/{space_id}",
                "answers": answers
            },
            timeout=60
        )
        result = response.json()
        return (
            f"Success! Score: {result.get('score', 'N/A')}%\n"
            f"Correct: {result.get('correct_count', 0)}/{result.get('total_attempted', 0)}",
            pd.DataFrame(results)
        )
    except Exception as e:
        return f"Submission failed: {str(e)}", pd.DataFrame(results)

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# LLM Agent Evaluation")
    
    with gr.Accordion("Instructions", open=False):
        gr.Markdown("""
        1. Click the login button
        2. Authorize with your Hugging Face account
        3. Click 'Run Evaluation'
        """)
    
    gr.LoginButton()
    
    run_btn = gr.Button("Run Evaluation", variant="primary")
    status = gr.Textbox(label="Status")
    results = gr.DataFrame(label="Results", wrap=True)
    
    run_btn.click(
        fn=run_and_submit_all,
        outputs=[status, results]
    )

if __name__ == "__main__":
    demo.launch(auth_message="Please login with your Hugging Face account")