app.py

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

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=torch.float16,
    low_cpu_mem_usage=True,
    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}"
    # 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.generate, kwargs=generation_kwargs)
    thread.start()

    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
        # 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)