app.py

import time
from threading import Thread

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

import spaces


PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/64ccdc322e592905f922a06e/DDIW0kbWmdOQWwy4XMhwX.png" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">LLaVA-Llama-3-8B</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Llava-Llama-3-8b is a LLaVA model fine-tuned from Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner</p>
</div>
"""

model_id_llama3 = "xtuner/llava-llama-3-8b-v1_1-transformers"
model_id_phi3 = "xtuner/llava-llama-3-8b-v1_1-transformers"

processor = AutoProcessor.from_pretrained(model_id_llama3)
processor = AutoProcessor.from_pretrained(model_id_phi3)

model_llama3 = LlavaForConditionalGeneration.from_pretrained(
    model_id_llama3,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
)
model_llama3.to("cuda:0")
model_llama3.generation_config.eos_token_id = 128009

model_phi3 = LlavaForConditionalGeneration.from_pretrained(
    model_id_phi3,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
)
model_phi3.to("cuda:0")
model_phi3.generation_config.eos_token_id = 128009


@spaces.GPU
def bot_streaming_llama3(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 LLaVA to work.")
    except NameError:
        # Handle the case where 'image' is not defined at all
        gr.Error("You need to upload an image for LLaVA to work.")

    prompt = f"<|start_header_id|>user<|end_header_id|>\n\n<image>\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
    # print(f"prompt: {prompt}")
    image = Image.open(image)
    inputs = processor(prompt, image, return_tensors='pt').to(0, torch.float16)

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

    thread = Thread(target=model_llama3.generate, kwargs=generation_kwargs)
    thread.start()

    text_prompt = f"<|start_header_id|>user<|end_header_id|>\n\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
    # print(f"text_prompt: {text_prompt}")

    buffer = ""
    time.sleep(0.5)
    for new_text in streamer:
        # find <|eot_id|> and remove it from the new_text
        if "<|eot_id|>" in new_text:
            new_text = new_text.split("<|eot_id|>")[0]
        buffer += new_text

        # generated_text_without_prompt = buffer[len(text_prompt):]
        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


@spaces.GPU
def bot_streaming_phi3(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 LLaVA to work.")
    except NameError:
        # Handle the case where 'image' is not defined at all
        gr.Error("You need to upload an image for LLaVA to work.")

    prompt = f"<|start_header_id|>user<|end_header_id|>\n\n<image>\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
    # print(f"prompt: {prompt}")
    image = Image.open(image)
    inputs = processor(prompt, image, return_tensors='pt').to(0, torch.float16)

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

    thread = Thread(target=model_phi3.generate, kwargs=generation_kwargs)
    thread.start()

    text_prompt = f"<|start_header_id|>user<|end_header_id|>\n\n{message['text']}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
    # print(f"text_prompt: {text_prompt}")

    buffer = ""
    time.sleep(0.5)
    for new_text in streamer:
        # find <|eot_id|> and remove it from the new_text
        if "<|eot_id|>" in new_text:
            new_text = new_text.split("<|eot_id|>")[0]
        buffer += new_text

        # generated_text_without_prompt = buffer[len(text_prompt):]
        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:
  with gr.Row():
    chatbot1 = gr.Chatbot(
    [],
    elem_id="llama3",
    bubble_full_width=False,
    label='LLaVa-Llama3'
    )
    chatbot2 = gr.Chatbot(
    [],
    elem_id="phi3",
    bubble_full_width=False,
    label='LLaVa-Phi3'
    )
      
  chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)

  gr.Examples(examples=[[{"text": "What is on the flower?", "files": ["./bee.png"]}],],
        {"text": "How to make this pastry?", "files": ["./baklava.png"]},],
              inputs=chat_input)

  #chat_input.submit(lambda: gr.MultimodalTextbox(interactive=False), None, [chat_input]).then(bot_streaming_llama3, [chat_input, chatbot1,], [chatbot1,])
  
  chat_msg1 = chat_input.submit(bot_streaming_llama3, [chat_input, chatbot1,], [chatbot1,])
  chat_msg2 = chat_input.submit(bot_streaming_phi3, [chat_input, chatbot2,], [chatbot2,])

  #bot_msg1 = chat_msg1.then(bot, chatbot1, chatbot1, api_name="bot_response1")
  #chat_msg1.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
  #bot_msg2 = chat_msg2.then(bot, chatbot2, chatbot2, api_name="bot_response2")
  #bot_msg2.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])

  chatbot1.like(print_like_dislike, None, None)
  chatbot2.like(print_like_dislike, None, None)

 
    #gr.ChatInterface(
    #fn=bot_streaming_llama3,
    #title="LLaVA Llama-3-8B",
    #examples=[{"text": "What is on the flower?", "files": ["./bee.jpg"]},
    #          {"text": "How to make this pastry?", "files": ["./baklava.png"]}],
    #description="Try [LLaVA Llama-3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers). 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)