app.py

import os
import gradio as gr
import requests
import pandas as pd
from typing import Tuple, Optional

from retriever import EnAgent as RetrieverAgent

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

class EnAgent:
    def __init__(self):
        self.retriever_agent = RetrieverAgent()

    def __call__(self, question: str) -> str:
        return self.retriever_agent.answer_question(question)


def run_and_submit_all(profile: gr.OAuthProfile | None) -> Tuple[str, Optional[pd.DataFrame]]:
    if not profile:
        return "❌ Please Login to Hugging Face with the button.", None

    username = profile.username
    space_id = os.getenv("SPACE_ID")
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    try:
        agent = EnAgent()
    except Exception as e:
        return f"❌ Error initializing agent: {str(e)}", None

    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 requests.exceptions.RequestException as e:
        return f"❌ Error fetching questions: {str(e)}", None
    except Exception as e:
        return f"❌ Unexpected error fetching questions: {str(e)}", None

    results_log = []
    answers_payload = []

    for item in questions_data:
        task_id = item.get("task_id") or item.get("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: {str(e)}"
            })

    if not answers_payload:
        return "⚠️ Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    submission_data = {
        "username": username.strip(),
        "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
        "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"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 requests.exceptions.RequestException as e:
        return f"❌ Submission Failed: {str(e)}", pd.DataFrame(results_log)


# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# En Agent")
    gr.Markdown("""
    **Instructions:**
    1. Log in to Hugging Face below.
    2. Click the button to run your agent on questions and submit answers.
    """)
    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])

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