app.py

import os
import json
import requests
import traceback
import gradio as gr
import pandas as pd
from typing import Optional
from dotenv import load_dotenv
from huggingface_hub import InferenceClient

# ────────────────  Load Environment ────────────────
load_dotenv()
hf_token = os.getenv("HF_TOKEN")
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MODEL_ID = "mistralai/Mistral-7B-Instruct-v0.1"

# ────────────────  Load Inference Client ────────────────
try:
    client = InferenceClient(model=MODEL_ID, token=hf_token)
except Exception as e:
    raise RuntimeError(f"Model loading failed: {e}")

# ────────────────  Agent Class ────────────────
class MistralAgent:
    def __init__(self):
        print("✅ MistralAgent (Inference API) initialized.")

    def __call__(self, question: str) -> str:
        try:
            prompt = f"[INST] {question.strip()} [/INST]"
            response = client.text_generation(prompt, max_new_tokens=100, temperature=0.0, do_sample=False)
            return response.strip()
        except Exception as e:
            return f"LLM Error: {e}"

# ────────────────  API Helpers ────────────────
def get_all_questions(api_url: str) -> list[dict]:
    resp = requests.get(f"{api_url}/questions", timeout=15)
    resp.raise_for_status()
    return resp.json()

def submit_answers(api_url: str, username: str, code_link: str, answers: list[dict]) -> dict:
    payload = {
        "username": username,
        "agent_code": code_link,
        "answers": answers
    }
    resp = requests.post(f"{api_url}/submit", json=payload, timeout=60)
    resp.raise_for_status()
    return resp.json()

# ────────────────  Gradio Evaluation Logic ────────────────
def run_and_submit_all(profile: Optional[gr.OAuthProfile]):
    if not profile:
        return "❌ Please log in to Hugging Face using the button above.", None
    username = profile.username.strip()

    space_id = os.getenv("SPACE_ID", "")
    code_link = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else ""

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

    try:
        questions_data = get_all_questions(DEFAULT_API_URL)
    except Exception as e:
        return f"❌ Failed to load questions: {e}", None

    answers_payload = []
    results_log = []

    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question", "")
        if not task_id or not question_text:
            continue
        try:
            submitted_answer = agent(question_text)
        except Exception as e:
            submitted_answer = f"AGENT ERROR: {e}"
        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})

    if not answers_payload:
        return "❌ No answers submitted.", pd.DataFrame(results_log)

    try:
        result_data = submit_answers(DEFAULT_API_URL, username, code_link, answers_payload)
    except requests.exceptions.HTTPError as e:
        try:
            detail = e.response.json().get("detail", e.response.text)
        except Exception:
            detail = e.response.text
        return f"❌ Submission Failed: HTTP {e.response.status_code}. Detail: {detail}", pd.DataFrame(results_log)
    except Exception as e:
        return f"❌ Submission Error: {e}", pd.DataFrame(results_log)

    score = result_data.get("score", "N/A")
    correct_count = result_data.get("correct_count", "?")
    total = result_data.get("total_attempted", "?")
    message = result_data.get("message", "")

    final_status = (
        f"✅ Submission Successful!\n"
        f"User: {username}\n"
        f"Score: {score}% ({correct_count}/{total} correct)\n"
        f"Message: {message}"
    )
    return final_status, pd.DataFrame(results_log)

# ────────────────  Gradio UI ────────────────
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 Mistral-7B Agent Evaluation (via Inference API)")
    gr.Markdown(
        """
        **Instructions:**

        1. Copy this Space and define your own agent logic.
        2. Log in with your Hugging Face account.
        3. Click “Run Evaluation & Submit All Answers” to test and submit.
        """
    )
    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Status", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Agent Answers", wrap=True)

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

if __name__ == "__main__":
    print("Launching Gradio Interface...")
    demo.launch(debug=True, server_name="0.0.0.0", server_port=7860)