import os
import gradio as gr
import requests
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch

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

# --- Agent Definition ---
class BasicAgent:
    def __init__(self):
        # Change this model to one you have access to
        model_name = "Qwen/Qwen3-0.6B-MLX-bf16"
        print(f"Loading model {model_name}")
        # Load tokenizer and model
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        # Create generation pipeline
        self.generator = pipeline(
            "text-generation",
            model=self.model,
            tokenizer=self.tokenizer,
            max_new_tokens=100,
            temperature=0.0,
            do_sample=False
        )

    def __call__(self, question: str) -> str:
        print("Question:", question)
        prompt = question.strip()
        output = self.generator(prompt)[0]["generated_text"]
        # Remove the prompt prefix so only the answer remains
        if output.startswith(prompt):
            answer = output[len(prompt):].strip()
        else:
            answer = output.strip()
        # Take first line if multiple lines
        answer = answer.split("\n")[0].strip()
        # Optionally strip trailing punctuation
        answer = answer.rstrip(" .,:;!?")
        print("Answer:", answer)
        return answer

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")

    if not profile:
        return "Please Login to Hugging Face with the button.", None
    username = profile.username
    print("User:", username)

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

    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"

    # Fetch questions
    try:
        resp = requests.get(questions_url, timeout=15)
        resp.raise_for_status()
        questions_data = resp.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    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
        try:
            ans = agent(question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": ans})
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": ans
            })
        except Exception as e:
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": f"ERROR: {e}"
            })

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

    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }

    try:
        post_resp = requests.post(submit_url, json=submission_data, timeout=60)
        post_resp.raise_for_status()
        result = post_resp.json()
        status_text = (
            f"Submission Successful!\n"
            f"User: {result.get('username')}\n"
            f"Overall Score: {result.get('score', 'N/A')}% "
            f"({result.get('correct_count', '?')}/{result.get('total_attempted', '?')} correct)\n"
            f"Message: {result.get('message', '')}"
        )
        return status_text, 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("# Agent Evaluation Runner")
    gr.Markdown(
        """
        1. Login with Hugging Face  
        2. Click “Run Evaluation & Submit All Answers”  
        3. Wait for score and see your answers  
        """
    )

    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_out = 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_out, results_table])

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