import time
from threading import Thread

import gradio as gr
import spaces
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
from transformers import TextIteratorStreamer

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

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">microsoft/Phi-3-vision-128k-instruct</h1>
</div>
"""
user_prompt = '<|user|>\n'
assistant_prompt = '<|assistant|>\n'
prompt_suffix = "<|end|>\n"



model_id = "microsoft/Phi-3-vision-128k-instruct"

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    trust_remote_code=True,
)

model.to("cuda:0")


@spaces.GPU
def bot_streaming(message, history):
    print(message)
    if message["files"]:
        # message["files"][-1] is a Dict or just a string
        if type(message["files"][-1]) == dict:
            image = message["files"][-1]["path"]
        else:
            image = message["files"][-1]
    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]
    try:
        if image is None:
            # Handle the case where image is None
            gr.Error("You need to upload an image for Phi-3-vision to work.")
    except NameError:
        # Handle the case where 'image' is not defined at all
        gr.Error("You need to upload an image for Phi-3-vision to work.")

    # prompt = f"{message['text']}<|image_1|>\nCan you convert the table to markdown format?{prompt_suffix}{assistant_prompt}"
    chat = [
        {"role": "user", "content": f"<|image_1|>\n{message['text']}"},
    ]
    prompt = processor.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

    # need to remove last <|endoftext|> if it is there, which is used for training, not inference. For training, make sure to add <|endoftext|> in the end.
    if prompt.endswith("<|endoftext|>"):
        prompt = prompt.rstrip("<|endoftext|>")
    
    print(f">>> Prompt\n{prompt})")
    
    image = Image.open(image)
    inputs = processor(prompt, [image], return_tensors='pt').to("cuda:0")

    streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": False, "skip_prompt": True})
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024, do_sample=False, eos_token_id=processor.tokenizer.eos_token_id)

    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    buffer = ""
    time.sleep(0.5)
    for new_text in streamer:
        buffer += new_text

        generated_text_without_prompt = buffer
        # print(generated_text_without_prompt)
        time.sleep(0.06)
        # print(f"new_text: {generated_text_without_prompt}")
        yield generated_text_without_prompt


chatbot = gr.Chatbot(placeholder=PLACEHOLDER, scale=1)
chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...",
                                  show_label=False)
with gr.Blocks(fill_height=True, ) as demo:
    gr.ChatInterface(
        fn=bot_streaming,
        title="Phi-3 Vision 128k Instruct",
        examples=[{"text": "What is on the flower?", "files": ["./bee.jpg"]},
                  {"text": "How to make this pastry?", "files": ["./baklava.png"]}],
        description="Try [microsoft/Phi-3-vision-128k-instruct](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct). Upload an image and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error.",
        stop_btn="Stop Generation",
        multimodal=True,
        textbox=chat_input,
        chatbot=chatbot,
    )

demo.queue(api_open=False)
demo.launch(show_api=False, share=False)