app.py

from transformers import MllamaForConditionalGeneration, AutoProcessor, TextIteratorStreamer
from PIL import Image
import requests
import torch
from threading import Thread
import gradio as gr
from gradio import FileData
import time
import spaces
ckpt = "Xkev/Llama-3.2V-11B-cot"
model = MllamaForConditionalGeneration.from_pretrained(ckpt,
    torch_dtype=torch.bfloat16).to("cuda")
processor = AutoProcessor.from_pretrained(ckpt)


@spaces.GPU
def bot_streaming(message, history, max_new_tokens=250):
    
    txt = message["text"]
    ext_buffer = f"{txt}"
    
    messages= [] 
    images = []
    

    for i, msg in enumerate(history): 
        if isinstance(msg[0], tuple):
            messages.append({"role": "user", "content": [{"type": "text", "text": history[i+1][0]}, {"type": "image"}]})
            messages.append({"role": "assistant", "content": [{"type": "text", "text": history[i+1][1]}]})
            images.append(Image.open(msg[0][0]).convert("RGB"))
        elif isinstance(history[i-1], tuple) and isinstance(msg[0], str):
            # messages are already handled
            pass
        elif isinstance(history[i-1][0], str) and isinstance(msg[0], str): # text only turn
            messages.append({"role": "user", "content": [{"type": "text", "text": msg[0]}]})
            messages.append({"role": "assistant", "content": [{"type": "text", "text": msg[1]}]})

    # add current message
    if len(message["files"]) == 1:
        
        if isinstance(message["files"][0], str): # examples
            image = Image.open(message["files"][0]).convert("RGB")
        else: # regular input
            image = Image.open(message["files"][0]["path"]).convert("RGB")
        images.append(image)
        messages.append({"role": "user", "content": [{"type": "text", "text": txt}, {"type": "image"}]})
    else:
        messages.append({"role": "user", "content": [{"type": "text", "text": txt}]})


    texts = processor.apply_chat_template(messages, add_generation_prompt=True)

    if images == []:
        inputs = processor(text=texts, return_tensors="pt").to("cuda")
    else:
        inputs = processor(text=texts, images=images, return_tensors="pt").to("cuda")
        
    generation_kwargs = dict(inputs, max_new_tokens=max_new_tokens)
    
    with torch.no_grad():
        output = model.generate(**inputs, **generation_kwargs)
    output_text = processor.decode(output[0][inputs['input_ids'].shape[1]:]).replace('<|eot_id|>', '')

    yield output_text


demo = gr.ChatInterface(fn=bot_streaming, title="LLaVA-CoT",
      textbox=gr.MultimodalTextbox(), 
      additional_inputs = [gr.Slider(
              minimum=512,
              maximum=1024,
              value=512,
              step=1,
              label="Maximum number of new tokens to generate",
          )
        ],
      cache_examples=False,
      description="Upload an image, and start chatting about it. To learn more about LLaVA-CoT, visit [oir GitHub page](https://github.com/PKU-YuanGroup/LLaVA-CoT). ",
      stop_btn="Stop Generation", 
      fill_height=True,
    multimodal=True)
    
demo.launch(debug=True)