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"

# --- Basic Agent Logic ---
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:
        input_text = f"{self.agent_prompt}\n\nQuestion: {question}"
        inputs = self.tokenizer(input_text, return_tensors="pt")
        outputs = self.llm.generate(**inputs)
        decoded = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        final = decoded.split("FINAL ANSWER:")[-1].strip()
        return f"FINAL ANSWER: {final}" if final else "FINAL ANSWER: UNKNOWN"

# --- Submission Function ---
def run_and_submit_all(username):
    space_id = os.getenv("SPACE_ID", "your-username/your-space")  # fallback
    if not username.strip():
        return "Username is required for submission.", None

    agent = BasicAgent()

    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_API_URL}/submit"
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e:
        return f"Failed to fetch questions: {e}", None

    answers = []
    log = []

    for item in questions_data:
        task_id = item.get("task_id")
        question = item.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 submitted.", pd.DataFrame(log)

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

    try:
        response = requests.post(submit_url, json=payload, timeout=30)
        response.raise_for_status()
        result = response.json()
        status = (
            f"Submission Successful!\n"
            f"User: {result.get('username')}\n"
            f"Score: {result.get('score', 'N/A')}% "
            f"({result.get('correct_count', '?')}/{result.get('total_attempted', '?')} correct)\n"
            f"Message: {result.get('message', '')}"
        )
        return status, 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 & Submission")
    gr.Markdown("Enter your Hugging Face username and press **Run and Submit** to evaluate your agent and submit your results.")
    
    username_input = gr.Textbox(label="Hugging Face Username", placeholder="e.g. your-hf-username")
    run_button = gr.Button("Run and Submit")
    status_output = gr.Textbox(label="Submission Status", lines=4, interactive=False)
    results_table = gr.DataFrame(label="Submitted Answers")

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

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