import os
import time
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import gradio as gr
from threading import Thread

HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL = "AGI-0/Art-v0-3B"

device = "cuda"  # Use "cpu" if no GPU available

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
    MODEL, torch_dtype=torch.bfloat16, device_map="auto"
)
end_of_sentence = tokenizer.convert_tokens_to_ids("<|im_end|>")

class ConversationManager:
    def __init__(self):
        self.user_history = []  # User-facing history with formatting
        self.model_history = []  # Model-facing history without formatting

    def add_exchange(self, user_message, model_response):
        formatted_response = self.format_response(model_response)
        self.model_history.append((user_message, model_response))
        self.user_history.append((user_message, formatted_response))
        print(f"\nModel History Updated: {self.model_history}")
        print(f"\nUser History Updated: {self.user_history}")

    def format_response(self, response):
        """Format response for UI while keeping raw text for model."""
        if "<|end_reasoning|>" in response:
            parts = response.split("<|end_reasoning|>")
            reasoning, rest = parts[0], parts[1] if len(parts) > 1 else ""
            return f"<details><summary>Click to see reasoning</summary>\n\n{reasoning}\n\n</details>\n\n{rest}"
        return response

    def get_user_history(self):
        return self.user_history

    def get_model_history(self):
        return self.model_history

conversation_manager = ConversationManager()

@spaces.GPU()
def stream_chat(
    message: str,
    history: list,
    system_prompt: str,
    temperature: float = 0.2,
    max_new_tokens: int = 4096,
    top_p: float = 1.0,
    top_k: int = 1,
    penalty: float = 1.1,
):
    print(f'User Message: {message}')
    
    model_history = conversation_manager.get_model_history()
    print(f'Model History: {model_history}')
    
    conversation = []
    for prompt, answer in model_history:
        conversation.extend([
            {"role": "user", "content": prompt},
            {"role": "assistant", "content": answer},
        ])
    conversation.append({"role": "user", "content": message})

    print(f'Formatted Conversation for Model: {conversation}')
    
    input_ids = tokenizer.apply_chat_template(
        conversation, add_generation_prompt=True, return_tensors="pt"
    ).to(model.device)

    streamer = TextIteratorStreamer(
        tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True
    )

    generate_kwargs = dict(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=False if temperature == 0 else True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        repetition_penalty=penalty,
        eos_token_id=[end_of_sentence],
        streamer=streamer,
    )

    buffer = ""
    original_response = ""

    with torch.no_grad():
        thread = Thread(target=model.generate, kwargs=generate_kwargs)
        thread.start()

        for new_text in streamer:
            buffer += new_text
            original_response += new_text
            print(f'Streaming: {buffer}')
            formatted_buffer = conversation_manager.format_response(buffer)
            yield formatted_buffer, history + [[message, formatted_buffer]]


    conversation_manager.add_exchange(message, original_response)

chatbot = gr.Chatbot(height=600, placeholder="<center><p>Hi! How can I help you today?</p></center>")

demo = gr.Blocks()
with demo:
    gr.HTML("<h2>Link to the model: <a href='https://huggingface.co/AGI-0/Art-v0-3B'>click here</a></h2>")
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Textbox(value="", label="System Prompt", render=False),
            gr.Slider(minimum=0, maximum=1, step=0.1, value=0.2, label="Temperature", render=False),
            gr.Slider(minimum=128, maximum=8192, step=1, value=4096, label="Max new tokens", render=False),
            gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="top_p", render=False),
            gr.Slider(minimum=1, maximum=50, step=1, value=1, label="top_k", render=False),
            gr.Slider(minimum=0.0, maximum=2.0, step=0.1, value=1.1, label="Repetition penalty", render=False),
        ],
        examples=[
            ["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
            ["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
            ["Tell me a random fun fact about the Roman Empire."],
            ["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
        ],
        cache_examples=False,
    )

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