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

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

# --- Initialize Model ---
model_name = "mosaicml/mpt-7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Offload folder pour éviter crash GPU
offload_folder = "offload"
os.makedirs(offload_folder, exist_ok=True)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    offload_folder=offload_folder,
    torch_dtype=torch.float16,
)

# --- Define a simple tool for demonstration ---
def AddTwoNumbers(a: int, b: int) -> int:
    return a + b

# --- Reasoning Agent Definition ---
class ReasoningAgent:
    def __init__(self):
        print("ReasoningAgent initialized.")
        self.tools_description = (
            "You have one tool available:\n"
            "- AddTwoNumbers(a, b): returns the sum of two integers a and b.\n"
            "Use this tool if you need to add numbers."
        )

    def __call__(self, question: str) -> str:
        print(f"\n=== New Question ===\n{question}\n")

        # Prompt structuré
        prompt = f"""
You are an AI reasoning agent.
{self.tools_description}

Answer the question in the following JSON format:
{{
    "thought": "Describe your reasoning step by step",
    "action": "Action you decide to take (like calling a tool) or 'None' if no action",
    "observation": "Result of the action (or empty if no action)",
    "answer": "Final answer to the user"
}}

Question: {question}
"""
        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
        output = model.generate(**inputs, max_new_tokens=300)
        text_output = tokenizer.decode(output[0], skip_special_tokens=True)
        print(f"Raw model output:\n{text_output[:1000]}...\n")  # on affiche un gros morceau du texte

        # Parser le JSON
        try:
            parsed = json.loads(text_output)
        except json.JSONDecodeError as e:
            print(f"⚠️ JSON decode error: {e}")
            parsed = {"thought": "", "action": "None", "observation": "", "answer": text_output}

        thought = parsed.get("thought", "")
        action = parsed.get("action", "None")
        observation = parsed.get("observation", "")
        answer = parsed.get("answer", "No answer returned.")

        # Exécution de l'outil si nécessaire
        if action.startswith("AddTwoNumbers"):
            try:
                numbers = action[action.find("(")+1:action.find(")")].split(",")
                a, b = int(numbers[0].strip()), int(numbers[1].strip())
                observation = AddTwoNumbers(a, b)
                parsed["observation"] = str(observation)
                print(f"✅ Tool executed: {action} -> {observation}")
            except Exception as e:
                observation = f"⚠️ Error executing tool: {e}"
                parsed["observation"] = observation
                print(observation)

        # Print du raisonnement actuel
        print(f"💭 Thought: {thought}")
        print(f"🔧 Action: {action}")
        print(f"👀 Observation: {observation}")
        print(f"📝 Answer: {answer}\n{'-'*50}\n")

        return answer

# --- Main Evaluation & Submission Function ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

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

    # Instantiate Agent
    try:
        agent = ReasoningAgent()
    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:
        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
    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:
            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 "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # Submit
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    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.')}"
        )
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except Exception as e:
        results_df = pd.DataFrame(results_log)
        return f"Submission Failed: {e}", results_df

# --- Build Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# Reasoning Agent with Tool Example")
    gr.Markdown("""
    **Instructions:**
    1. Log in to Hugging Face.
    2. Click 'Run Evaluation & Submit All Answers'.
    3. The agent can use the AddTwoNumbers tool if needed.
    """)
    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, outputs=[status_output, results_table])

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