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Update app.py
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import gradio as gr
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, TextIteratorStreamer
from threading import Thread
import re
import time
from PIL import Image
import torch
import spaces
processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True)
model.to("cuda:0")
@spaces.GPU
def bot_streaming(message, history):
print(message)
if message["files"]:
image = message["files"][-1]["path"]
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]
if image is None:
gr.Error("You need to upload an image for LLaVA to work.")
prompt=f"[INST] <image>\n{message['text']} [/INST]"
image = Image.open(image).convert("RGB")
inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": True})
generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=300)
generated_text = ""
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
text_prompt =f"[INST] \n{message['text']} [/INST]"
buffer = ""
for new_text in streamer:
buffer += new_text
generated_text_without_prompt = buffer[len(text_prompt):]
time.sleep(0.04)
yield generated_text_without_prompt
demo = gr.ChatInterface(fn=bot_streaming, title="LLaVA NeXT", examples=[{"text": "What is on the flower?", "files":["./bee.jpg"]},
{"text": "How to make this pastry?", "files":["./baklava.png"]}],
description="Try [LLaVA NeXT](https://huggingface.co/docs/transformers/main/en/model_doc/llava_next) in this demo (more specifically, the [Mistral-7B variant](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf)). 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)
demo.launch(debug=True)