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

from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel, PythonInterpreterTool, WikipediaSearchTool, FinalAnswerTool

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

# --- Hugging Face API Key ---
HF_API_KEY = os.environ.get("HF_TOKEN")
if not HF_API_KEY:
    raise ValueError("❌ Hugging Face API key not found. Please add it under Settings → Variables and secrets.")

# --- Initialize Hugging Face Model ---
model = InferenceClientModel(
    model_id="HuggingFaceH4/zephyr-7b-beta",  # ✅ chat model
    token=HF_API_KEY
)

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self, model):
        self.model = model
        self.agent = CodeAgent(
            tools=[
                DuckDuckGoSearchTool(),
                PythonInterpreterTool(),
                WikipediaSearchTool(),
                FinalAnswerTool()
            ],
            model=self.model
        )
        print("✅ BasicAgent initialized with Hugging Face model.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        try:
            answer = self.agent.run(question)
            if not answer:
                return "⚠️ Model returned no answer."
            return answer
        except Exception as e:
            print(f"❌ Error during agent run: {e}")
            return f"AGENT ERROR: {str(e)}"


# --- Run & Submit Function ---
def run_and_submit_all(profile: gr.OAuthProfile | None = None):
    if not profile:
        return "Please login to Hugging Face with the button.", None
    username = profile.username
    print(f"User logged in: {username}")

    # Initialize agent
    agent = BasicAgent(model=model)

    # Fetch questions
    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_API_URL}/submit"
    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 Exception as e:
        return f"Error fetching questions: {e}", None

    # Run agent on all questions
    results_log = []
    answers_payload = []
    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
        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})

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

    # Prepare submission
    space_id = os.getenv("SPACE_ID", "your_space_id_here")  # optional fallback
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}

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

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
    **Instructions:**
    1. Login with your Hugging Face account.
    2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, and submit.
    """)

    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,
        inputs=(),
        outputs=[status_output, results_table]
    )

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