from __future__ import annotations

import spaces

import gradio as gr
from threading import Thread
from transformers import TextIteratorStreamer
import hashlib
import os

from transformers import AutoModel, AutoProcessor
import torch
import sys
import subprocess
from PIL import Image

from cobra import load
import time

subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'packaging'])
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'ninja'])
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'mamba-ssm'])
subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'causal-conv1d'])

vlm = load("cobra+3b")

if torch.cuda.is_available():
    DEVICE = "cuda"
    DTYPE = torch.bfloat16
else:
    DEVICE = "cpu"
    DTYPE = torch.float32
vlm.to(DEVICE, dtype=DTYPE)

prompt_builder = vlm.get_prompt_builder()

@spaces.GPU
def bot_streaming(message, history, temperature, top_k, max_new_tokens):
    if len(history) == 0:
        prompt_builder.prompt, prompt_builder.turn_count = "", 0
    print(message)
    if message["files"]:
        image = message["files"][-1]["path"]
    else:
        # if there's no image uploaded for this turn, look for images in the past turns
        # kept inside tuples, take the last one
        for hist in history:
            if type(hist[0])==tuple:
                image = hist[0][0]
  
    image = Image.open(image).convert("RGB")
    
    prompt_builder.add_turn(role="human", message=message['text'])
    prompt_text = prompt_builder.get_prompt()
    
    # Generate from the VLM
    with torch.no_grad():
        generated_text = vlm.generate(
            image,
            prompt_text,
            cg=True,
            do_sample=True,
            temperature=temperature,
            top_k=top_k,
            max_new_tokens=max_new_tokens,
        )
    prompt_builder.add_turn(role="gpt", message=generated_text)

    time.sleep(0.04)
    yield generated_text
    
    
demo = gr.ChatInterface(fn=bot_streaming, 
                        additional_inputs=[gr.Slider(0, 1, value=0.2, label="Temperature"),
                                           gr.Slider(1, 3, value=1, step=1, label="Top k"),
                                           gr.Slider(1, 2048, value=256, step=1, label="Max New Tokens")],
                        title="Cobra", 
                        description="Try [Cobra](https://huggingface.co/papers/2403.14520) in this demo. Upload an image and start chatting about it.",
                        stop_btn="Stop Generation", multimodal=True,
                        examples=[{"text": "Describe this image", "files":["./cobra.png"]}])
demo.launch(debug=True)