app.py

import os
import gradio as gr
import requests
import pandas as pd

# --- Constants ---
# ✅ correct backend API base URL
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Basic Agent Definition ---
# 👇 customize this class to make your own agent smarter
class BasicAgent:
    def __init__(self):
        print("✅ BasicAgent initialized.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question: {question[:50]}...")
        # For now, it returns a placeholder answer
        fixed_answer = "This is a default answer."
        print(f"Agent returning: {fixed_answer}")
        return fixed_answer


# --- Evaluation Logic ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Fetches all questions, runs agent, submits answers, shows results."""
    space_id = os.getenv("SPACE_ID")  # for linking to code repo

    if profile:
        username = f"{profile.username}"
        print(f"👤 Logged in as: {username}")
    else:
        return "Please log in with your Hugging Face account.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # --- Instantiate your 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" if space_id else "N/A"
    print(f"🔗 Code link: {agent_code}")

    # --- Fetch Questions ---
    print(f"📡 Fetching from {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            return "No questions fetched.", None
        print(f"✅ {len(questions_data)} questions retrieved.")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # --- Run Agent ---
    results_log = []
    answers_payload = []
    print(f"🤖 Running agent on {len(questions_data)} questions...")
    for item in questions_data:
        task_id = item.get("task_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, "Answer": submitted_answer})
        except Exception as e:
            results_log.append({"Task ID": task_id, "Question": question_text, "Answer": f"ERROR: {e}"})

    if not answers_payload:
        return "No answers produced by the agent.", pd.DataFrame(results_log)

    # --- Prepare Submission ---
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    print(f"🚀 Submitting {len(answers_payload)} answers...")

    # --- Submit ---
    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')}%\n"
            f"Correct: {result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')}\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission failed: {e}", pd.DataFrame(results_log)


# --- Build Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        ### Instructions
        1️⃣ Clone this space on your Hugging Face profile.  
        2️⃣ Modify the `BasicAgent` class to add your logic.  
        3️⃣ Log in below, then click **Run Evaluation & Submit All Answers**.  

        ---
        The process might take a few minutes while the agent runs all questions.  
        You can enhance your agent with reasoning, web tools, or retrieval modules.
        """
    )

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

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=6, interactive=False)
    results_table = gr.DataFrame(label="🧾 Questions and Agent Answers")

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

# --- Run ---
if __name__ == "__main__":
    print("\n" + "-" * 40)
    print("🌐 App Starting")
    print("-" * 40)
    demo.launch(debug=True, share=False)