app.py

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

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

# -----------------------------------------------------
# REAL AGENT USING HUGGINGFACE MODEL (NO API KEY NEEDED)
# -----------------------------------------------------
class BasicAgent:
    def __init__(self):
        print("Loading HF model...")
        model_name = "Qwen/Qwen2.5-1.5B-Instruct"

        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float32,
            device_map="auto"
        )

    def __call__(self, question: str) -> str:

        prompt = f"""
You are a strict exam solver.
Give ONLY the final answer. No explanation. No extra words.

Question:
{question}

Final Answer:
"""

        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)

        output_ids = self.model.generate(
            **inputs,
            max_new_tokens=80,
            temperature=0.1,
            do_sample=False
        )

        decoded = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)

        # lấy câu trả lời sau "Final Answer:"
        if "Final Answer:" in decoded:
            decoded = decoded.split("Final Answer:")[-1].strip()

        return decoded


# -----------------------------------------------------
# RUN + SUBMIT ANSWERS
# -----------------------------------------------------
def run_and_submit_all(profile: gr.OAuthProfile | None):

    space_id = os.getenv("SPACE_ID")

    if profile:
        username = f"{profile.username}"
    else:
        return "Please login to HuggingFace.", 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:
        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=20)
        response.raise_for_status()
        questions = response.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    results_log = []
    answers_payload = []

    for q in questions:
        task_id = q["task_id"]
        text = q["question"]

        try:
            ans = agent(text)
        except Exception as e:
            ans = f"ERROR: {e}"

        answers_payload.append({"task_id": task_id, "submitted_answer": ans})
        results_log.append({"Task ID": task_id, "Question": text, "Submitted Answer": ans})

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

    # Submit answers
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()

        final_msg = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Score: {result_data.get('score')}%\n"
            f"Correct: {result_data.get('correct_count')}/{result_data.get('total_attempted')}\n"
            f"Message: {result_data.get('message')}"
        )

        return final_msg, pd.DataFrame(results_log)

    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)


# -----------------------------------------------------
# GRADIO UI
# -----------------------------------------------------
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 GAIA Unit 4 Agent Evaluation Runner")
    gr.Markdown("Login → Run agent → Auto-submit to leaderboard")

    gr.LoginButton()

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

    status_box = gr.Textbox(label="Status", lines=5)
    table = gr.DataFrame(label="Agent Answers")

    run_btn.click(
        run_and_submit_all,
        outputs=[status_box, table]
    )

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