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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 = "xtuner/llava-llama-3-8b-v1_1-transformers"
processor = AutoProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
)
model.to("cuda:0")
model.generation_config.eos_token_id = 128009
@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]
if image is None:
# Handle the case where image is None
image = "http://files.tatl.tv/ignore.png"
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.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:
gr.ChatInterface(
fn=bot_streaming,
description="Try [LLaVA Llama-3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).",
stop_btn="Stop Generation",
multimodal=True,
textbox=chat_input,
chatbot=chatbot,
)
demo.queue(api_open=False)
demo.launch(show_api=False, share=False) |