import os
import gradio as gr
import requests
from transformers import AutoModelForCausalLM, AutoTokenizer

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MODEL_NAME = "meta-llama/Llama-4-Scout-17B-16E-Instruct"

# --- Basic Agent Using Llama 4 ---
class BasicAgent:
    def __init__(self, hf_token: str):
        print("Initializing Llama 4 Agent...")
        self.tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_auth_token=hf_token)
        self.model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, use_auth_token=hf_token)

    def __call__(self, question: str) -> str:
        question = question.encode('utf-8', errors='ignore').decode('utf-8')
        inputs = self.tokenizer(question, return_tensors="pt")
        outputs = self.model.generate(**inputs, max_new_tokens=50)
        answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        # 只回覆答案，不包含 prompt
        if question in answer:
            answer = answer.replace(question, '').strip()
        return answer

# --- Function to run and submit ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    hf_token = os.getenv('HF_TOKEN')
    if not hf_token:
        return "HF_TOKEN not set!", None

    if not profile:
        return "Please login to Hugging Face.", None

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

    # 1. Create agent
    try:
        agent = BasicAgent(hf_token)
    except Exception as e:
        return f"Error initializing agent: {e}", None

    # 2. Fetch questions
    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
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # 3. Run agent on questions
    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 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, "Submitted Answer": submitted_answer})
        except Exception as e:
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

    if not answers_payload:
        return "No answers generated.", results_log

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

    # 5. 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', '')}")
        return final_status, results_log
    except Exception as e:
        return f"Submission Failed: {e}", results_log