app.py

import os
import gradio as gr
import requests
import pandas as pd
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
DEFAULT_HF_MODEL = "mistralai/Mistral-7B-Instruct-v0.1"  # Free model on Hugging Face

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self, hf_token=None, model_name=DEFAULT_HF_MODEL):
        print("Initializing BasicAgent with LLM...")
        self.hf_token = hf_token
        self.model_name = model_name
        self.llm = None
        
        if hf_token:
            try:
                print(f"Loading model: {model_name}")
                self.tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
                self.model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_token)
                self.llm = pipeline(
                    "text-generation",
                    model=self.model,
                    tokenizer=self.tokenizer,
                    device_map="auto"
                )
                print("Model loaded successfully")
            except Exception as e:
                print(f"Error loading model: {e}")
                raise Exception(f"Could not load model: {e}")
        else:
            print("No HF token provided - agent will use default answers")
    
    def __call__(self, question: str) -> str:
        if not self.llm:
            return "This is a default answer (no LLM initialized)"
            
        try:
            print(f"Generating answer for question: {question[:50]}...")
            response = self.llm(
                question,
                max_new_tokens=150,
                do_sample=True,
                temperature=0.7,
                top_p=0.9
            )
            return response[0]['generated_text']
        except Exception as e:
            print(f"Error generating answer: {e}")
            return f"Error generating answer: {e}"

def run_and_submit_all(profile: gr.OAuthProfile | None, hf_token: str):
    """Main function to run evaluation and submit answers"""
    space_id = os.getenv("SPACE_ID")
    if not profile:
        return "Please Login to Hugging Face with the button.", None

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

    # Initialize agent
    try:
        agent = BasicAgent(hf_token=hf_token)
    except Exception as e:
        return f"Error initializing agent: {e}", None
    
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # Fetch questions
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            return "Fetched questions list is empty or invalid format.", None
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # Process questions
    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:
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

    if not answers_payload:
        return "Agent did not produce any 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"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.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# LLM Agent Evaluation Runner")
    gr.Markdown("""
        **Instructions:**
        1. Get your Hugging Face API token from [your settings](https://huggingface.co/settings/tokens)
        2. Enter your token below
        3. Log in to your Hugging Face account
        4. Click 'Run Evaluation & Submit All Answers'
    """)

    with gr.Row():
        hf_token_input = gr.Textbox(
            label="Hugging Face API Token",
            type="password",
            placeholder="hf_xxxxxxxxxxxxxxxx",
            info="Required for LLM access"
        )
    
    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status", lines=5)
    results_table = gr.DataFrame(label="Results", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        inputs=[gr.OAuthProfile(), hf_token_input],
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    demo.launch()