app.py

#!/usr/bin/env python

from __future__ import annotations

import os
import string

import gradio as gr
import PIL.Image
import torch
from transformers import AutoProcessor, Blip2ForConditionalGeneration
import re

DESCRIPTION = "# LLaVA 🌋"

model_id = "llava-hf/llava-1.5-7b-hf"
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)
pipe = pipeline("image-to-text", model=model_id, model_kwargs={"quantization_config": quantization_config})


def extract_response_pairs(text):
    pattern = re.compile(r'(USER:.*?)ASSISTANT:(.*?)(?:$|USER:)', re.DOTALL)
    matches = pattern.findall(text)

    pairs = [(user.strip(), assistant.strip()) for user, assistant in matches]

    return pairs


def postprocess_output(output: str) -> str:
    if output and output[-1] not in string.punctuation:
        output += "."
    return output



def chat(image, text, temperature, length_penalty,
         repetition_penalty, max_length, min_length, num_beams, top_p,
         history_chat):
  
  prompt = " ".join(history_chat)
  prompt = f"USER: <image>\n{text}\nASSISTANT:"
  
  outputs = pipe(image, prompt=prompt, 
                  generate_kwargs={"temperature":temperature,
                  "length_penalty":length_penalty,
                  "repetition_penalty":repetition_penalty,
                  "max_length":max_length,
                  "min_length":min_length,
                  "num_beams":num_beams,
                  "top_p":top_p})
  
  output = postprocess_output(outputs[0]["generated_text"])
  history_chat.append(output)

  chat_val =  extract_response_pairs(" ".join(history_chat))
  return chat_val, history_chat


css = """
  #mkd {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
  """
with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.Markdown("**LLaVA, one of the greatest multimodal chat models is now available in transformers with 4-bit quantization! ⚡️  **")
    gr.Markdown("**Try it in this demo 🤗 **")

    chatbot = gr.Chatbot(label="Chat", show_label=False)
    gr.Markdown("Input image and text and start chatting 👇")
    with gr.Row():
      
      image = gr.Image(type="pil")
      text_input = gr.Text(label="Chat Input", show_label=False, max_lines=3, container=False)
    
      
      
    history_chat = gr.State(value=[])
    with gr.Row():
        clear_chat_button = gr.Button("Clear")
        chat_button = gr.Button("Submit", variant="primary")
    with gr.Accordion(label="Advanced settings", open=False):
        temperature = gr.Slider(
            label="Temperature",
            info="Used with nucleus sampling.",
            minimum=0.5,
            maximum=1.0,
            step=0.1,
            value=1.0,
        )
        length_penalty = gr.Slider(
            label="Length Penalty",
            info="Set to larger for longer sequence, used with beam search.",
            minimum=-1.0,
            maximum=2.0,
            step=0.2,
            value=1.0,
        )
        repetition_penalty = gr.Slider(
            label="Repetition Penalty",
            info="Larger value prevents repetition.",
            minimum=1.0,
            maximum=5.0,
            step=0.5,
            value=1.5,
        )
        max_length = gr.Slider(
            label="Max Length",
            minimum=1,
            maximum=512,
            step=1,
            value=50,
        )
        min_length = gr.Slider(
            label="Minimum Length",
            minimum=1,
            maximum=100,
            step=1,
            value=1,
        )
        num_beams = gr.Slider(
            label="Number of Beams",
            minimum=1,
            maximum=10,
            step=1,
            value=5,
        )
        top_p = gr.Slider(
            label="Top P",
            info="Used with nucleus sampling.",
            minimum=0.5,
            maximum=1.0,
            step=0.1,
            value=0.9,
        )
    chat_output = [
        chatbot,
        history_chat
    ]
    chat_button.click(fn=chat, inputs=[image, 
            text_input,
            temperature,
            length_penalty,
            repetition_penalty,
            max_length,
            min_length,
            num_beams,
            top_p,
            history_chat],
        outputs=chat_output,
        api_name="Chat",
    )

    chat_inputs = [
        image,
        text_input,
        temperature,
        length_penalty,
        repetition_penalty,
        max_length,
        min_length,
        num_beams,
        top_p,
        history_chat
    ]
    text_input.submit(
        fn=chat,
        inputs=chat_inputs,
        outputs=chat_output
    ).success(
        fn=lambda: "",
        outputs=chat_inputs,
        queue=False,
        api_name=False,
    )
    clear_chat_button.click(
        fn=lambda: ([], []),
        inputs=None,
        outputs=[
            chatbot,
            history_chat
        ],
        queue=False,
        api_name="clear",
    )
    image.change(
        fn=lambda: ([], []),
        inputs=None,
        outputs=[
            chatbot,
            history_chat
        ],
        queue=False,
    )
    

if __name__ == "__main__":
    demo.queue(max_size=10).launch()