import subprocess
from threading import Thread

import torch
import spaces
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer

#subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

MODEL_ID = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
CHAT_TEMPLATE = "َAuto"
MODEL_NAME = MODEL_ID.split("/")[-1]
CONTEXT_LENGTH = 16000
  


latex_delimiters_set = [{
        "left": "\\(",
        "right": "\\)",
        "display": False 
    }, {
        "left": "\\begin{equation}",
        "right": "\\end{equation}",
        "display": True 
    }, {
        "left": "\\begin{align}",
        "right": "\\end{align}",
        "display": True
    }, {
        "left": "\\begin{alignat}",
        "right": "\\end{alignat}",
        "display": True
    }, {
        "left": "\\begin{gather}",
        "right": "\\end{gather}",
        "display": True
    }, {
        "left": "\\begin{CD}",
        "right": "\\end{CD}",
        "display": True
    }, {
        "left": "\\[",
        "right": "\\]",
        "display": True
    }]


def predict(message, history, system_prompt, temperature, max_new_tokens, top_k, repetition_penalty, top_p):
    # Format history with a given chat template
    
    
    stop_tokens = ["<|endoftext|>", "<|im_end|>","|im_end|"]
    instruction = '<|im_start|>system\n' + system_prompt + '\n<|im_end|>\n'
    for user, assistant in history:
        instruction += f'<|im_start|>user\n{user}\n<|im_end|>\n<|im_start|>assistant\n{assistant}\n<|im_end|>\n'
    instruction += f'<|im_start|>user\n{message}\n<|im_end|>\n<|im_start|>assistant\n'
    
    print(instruction)
    
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)
    enc = tokenizer(instruction, return_tensors="pt", padding=True, truncation=True)
    input_ids, attention_mask = enc.input_ids, enc.attention_mask

    if input_ids.shape[1] > CONTEXT_LENGTH:
        input_ids = input_ids[:, -CONTEXT_LENGTH:]
        attention_mask = attention_mask[:, -CONTEXT_LENGTH:]

    generate_kwargs = dict(
        input_ids=input_ids.to(device),
        attention_mask=attention_mask.to(device),
        streamer=streamer,
        do_sample=True,
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_k=top_k,
        repetition_penalty=repetition_penalty,
        top_p=top_p
    )
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    outputs = []
    for new_token in streamer:
        outputs.append(new_token)
        if new_token in stop_tokens:
            
            break
        yield "".join(outputs)


tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID)

# Create Gradio interface
gr.ChatInterface(
    predict,
    
    additional_inputs_accordion=gr.Accordion(label="Parameters", open=False),
    additional_inputs=[
        gr.Textbox("You are a useful assistant. first recognize user request and then reply carfuly and thinking", label="System prompt"),
        gr.Slider(0, 1, 0.6, label="Temperature"),
        gr.Slider(0, 32000, 10000, label="Max new tokens"),
        gr.Slider(1, 80, 40, label="Top K sampling"),
        gr.Slider(0, 2, 1.1, label="Repetition penalty"),
        gr.Slider(0, 1, 0.95, label="Top P sampling"),
    ],
).queue().launch()