app.py

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

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

# --- BasicAgent Class ---
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        self.llm = AutoModelForCausalLM.from_pretrained("gpt2")
        self.tokenizer = AutoTokenizer.from_pretrained("gpt2")
        self.agent_prompt = (
            "You are a general AI assistant. I will ask you a question. "
            "Finish your answer with the format: FINAL ANSWER: [YOUR FINAL ANSWER]."
        )

    def __call__(self, question: str) -> str:
        prompt = f"{self.agent_prompt}\n\nQuestion: {question}"
        inputs = self.tokenizer(prompt, return_tensors="pt")
        outputs = self.llm.generate(**inputs, max_new_tokens=50)
        result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        final = result.split("FINAL ANSWER:")[-1].strip()
        return f"FINAL ANSWER: {final}" if final else "FINAL ANSWER: UNKNOWN"

# --- Run and Submit Function ---
def run_and_submit_all(profile):
    space_id = os.getenv("SPACE_ID", "your-username/your-space")  # fallback
    if not profile or not getattr(profile, "username", None):
        return "❌ Please log in to Hugging Face first.", None

    username = profile.username
    agent = BasicAgent()

    try:
        questions = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15).json()
    except Exception as e:
        return f"❌ Error fetching questions: {e}", None

    answers = []
    log = []

    for q in questions:
        task_id = q.get("task_id")
        question = q.get("question")
        if not task_id or not question:
            continue
        try:
            answer = agent(question)
            answers.append({"task_id": task_id, "submitted_answer": answer})
            log.append({"Task ID": task_id, "Question": question, "Submitted Answer": answer})
        except Exception as e:
            log.append({"Task ID": task_id, "Question": question, "Submitted Answer": f"ERROR: {e}"})

    if not answers:
        return "⚠️ No answers were generated.", pd.DataFrame(log)

    submission = {
        "username": username.strip(),
        "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
        "answers": answers
    }

    try:
        r = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=30)
        r.raise_for_status()
        res = r.json()
        return (
            f"✅ Submission Successful!\n"
            f"User: {res.get('username')}\n"
            f"Score: {res.get('score', 'N/A')}% "
            f"({res.get('correct_count', '?')}/{res.get('total_attempted', '?')})\n"
            f"Message: {res.get('message', '')}"
        ), pd.DataFrame(log)
    except Exception as e:
        return f"❌ Submission failed: {e}", pd.DataFrame(log)

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation")
    gr.Markdown("Login with Hugging Face and click the button to run evaluation and submit your answers.")

    profile = gr.LoginButton()
    run_button = gr.Button("Run and Submit")
    status_output = gr.Textbox(label="Submission Status", lines=4)
    results_table = gr.DataFrame(label="Answers Submitted")

    run_button.click(fn=run_and_submit_all, inputs=[profile], outputs=[status_output, results_table])

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