Spaces:
Running
on
Zero
Running
on
Zero
| 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") | |
| 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] | |
| 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=100) | |
| 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/papers/2310.03744) in this demo. Upload an image and start chatting about it, or simply try one of the examples below.", | |
| stop_btn="Stop Generation", multimodal=True) | |
| demo.launch(debug=True) |