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

import gradio as gr
from PIL import Image 
from transformers import AutoModelForCausalLM 
from transformers import AutoProcessor 
from transformers import TextIteratorStreamer
import time
from threading import Thread
import torch
import spaces

model_id = "microsoft/Phi-3-vision-128k-instruct" 
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto")
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) 
model.to("cuda:0")

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;"> vision model</p>
</div>
"""

@spaces.GPU
def bot_streaming(message, history):
    print(f'message is - {message}')
    print(f'history is - {history}')
    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
            raise gr.Error("You need to upload an image for  Vision to work. Close the error and try again with an Image.")
    except NameError:
        # Handle the case where 'image' is not defined at all
        raise gr.Error("You need to upload an image for Vision to work. Close the error and try again with an Image.")

    conversation = []
    flag=False
    for user, assistant in history:
        if assistant is None:
            #pass
            flag=True
            conversation.extend([{"role": "user", "content":""}])
            continue
        if flag==True:
            conversation[0]['content'] = f"<|image_1|>\n{user}"   
            conversation.extend([{"role": "assistant", "content": assistant}])
            flag=False
            continue
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])

    if len(history) == 0:
        conversation.append({"role": "user", "content": f"<|image_1|>\n{message['text']}"})
    else:
        conversation.append({"role": "user", "content": message['text']})
    print(f"prompt is -\n{conversation}")
    prompt = processor.tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
    image = Image.open(image)
    inputs = processor(prompt, image, return_tensors="pt").to("cuda:0") 

    streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True, "skip_prompt": True, 'clean_up_tokenization_spaces':False,}) 
    generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024, do_sample=False, temperature=0.0, eos_token_id=processor.tokenizer.eos_token_id,)

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

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer


chatbot=gr.Chatbot(scale=1, placeholder=PLACEHOLDER)
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="Vision",
    examples=[{"text": "Describe the image in details?", "files": ["./robo.jpg"]},
              {"text": "Count the number of apples.", "files": ["./setofmark6.png"]},
              
             ],
    description="Upload an image and start chatting.This is not the official demo.",
    stop_btn="Stop Generation",
    multimodal=True,
    textbox=chat_input,
    chatbot=chatbot,
    cache_examples=False,
    examples_per_page=3
    )

demo.queue()
demo.launch(debug=True, quiet=True)