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

from smolagents import (
    CodeAgent,
    DuckDuckGoSearchTool,
    VisitWebpageTool,
    InferenceClientModel
)

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


# -----------------------------
# REAL AGENT (FIXED VERSION)
# -----------------------------
class BasicAgent:
    def __init__(self):
        print("Initializing real agent...")

        # Model (Hugging Face hosted inference)
        self.model = InferenceClientModel()

        # Tools
        tools = [
            DuckDuckGoSearchTool(),
            VisitWebpageTool()
        ]

        # CodeAgent (actual reasoning agent)
        self.agent = CodeAgent(
            tools=tools,
            model=self.model
        )

    def __call__(self, question: str) -> str:
        print(f"Question: {question}")
        try:
            result = self.agent.run(question)
            return str(result)
        except Exception as e:
            print(f"Agent error: {e}")
            return f"ERROR: {e}"


# -----------------------------
# RUN + SUBMIT FUNCTION
# -----------------------------
def run_and_submit_all(profile: gr.OAuthProfile | None):

    space_id = os.getenv("SPACE_ID")

    if profile:
        username = profile.username
        print(f"User logged in: {username}")
    else:
        return "Please login first using Hugging Face button.", None

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

    # Create agent
    agent = BasicAgent()

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # Fetch questions
    response = requests.get(questions_url, timeout=30)
    response.raise_for_status()
    questions_data = response.json()

    results_log = []
    answers_payload = []

    # Run agent
    for item in questions_data:
        task_id = item["task_id"]
        question = item["question"]

        answer = agent(question)

        answers_payload.append({
            "task_id": task_id,
            "submitted_answer": answer
        })

        results_log.append({
            "Task ID": task_id,
            "Question": question,
            "Answer": answer
        })

    # Submit results
    submission_data = {
        "username": username,
        "agent_code": agent_code,
        "answers": answers_payload
    }

    submit_response = requests.post(submit_url, json=submission_data, timeout=60)
    submit_response.raise_for_status()
    result = submit_response.json()

    status = (
        f"Submission Successful!\n"
        f"Score: {result.get('score')}%\n"
        f"Correct: {result.get('correct_count')}/{result.get('total_attempted')}\n"
        f"Message: {result.get('message')}"
    )

    return status, pd.DataFrame(results_log)


# -----------------------------
# GRADIO UI
# -----------------------------
with gr.Blocks() as demo:
    gr.Markdown("# Unit 4 Agent Evaluation (Fixed Version)")

    gr.Markdown(
        "1. Login with Hugging Face\n"
        "2. Click Run Evaluation\n"
        "3. Wait for scoring"
    )

    gr.LoginButton()

    btn = gr.Button("Run Evaluation")

    status = gr.Textbox(label="Status")
    table = gr.DataFrame(label="Results")

    btn.click(
        fn=run_and_submit_all,
        outputs=[status, table]
    )


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