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| import transformers | |
| print("transformers version", transformers.__version__) | |
| from transformers import TextIteratorStreamer, ChameleonForConditionalGeneration, ChameleonProcessor | |
| import torch | |
| from PIL import Image | |
| import requests | |
| import spaces | |
| from threading import Thread | |
| import gradio as gr | |
| from gradio import FileData | |
| import time | |
| processor = ChameleonProcessor.from_pretrained("facebook/chameleon-30b") | |
| model = ChameleonForConditionalGeneration.from_pretrained("facebook/chameleon-30b", torch_dtype=torch.float16).to("cuda") | |
| def bot_streaming(message, history): | |
| txt = message.text | |
| ext_buffer = f"{txt}" | |
| if message.files: | |
| if len(message.files) == 1: | |
| image = [message.files[0].path] | |
| # interleaved images or video | |
| elif len(message.files) > 1: | |
| image = [msg.path for msg in message.files] | |
| else: | |
| def has_file_data(lst): | |
| return any(isinstance(item, FileData) for sublist in lst if isinstance(sublist, tuple) for item in sublist) | |
| def extract_paths(lst): | |
| return [item.path for sublist in lst if isinstance(sublist, tuple) for item in sublist if isinstance(item, FileData)] | |
| latest_text_only_index = -1 | |
| for i, item in enumerate(history): | |
| if all(isinstance(sub_item, str) for sub_item in item): | |
| latest_text_only_index = i | |
| image = [path for i, item in enumerate(history) if i < latest_text_only_index and has_file_data(item) for path in extract_paths(item)] | |
| if message.files is None: | |
| gr.Error("You need to upload an image or video for LLaVA to work.") | |
| image_extensions = Image.registered_extensions() | |
| image_extensions = tuple([ex for ex, f in image_extensions.items()]) | |
| if len(image) == 1: | |
| image = Image.open(image[0]).convert("RGB") | |
| prompt = f"{message.text}<image>" | |
| elif len(image) > 1: | |
| image_list = [] | |
| user_prompt = message.text | |
| for img in image: | |
| img = Image.open(img).convert("RGB") | |
| image_list.append(img) | |
| toks = "<image>" * len(image_list) | |
| prompt = user_prompt + toks | |
| image = image_list | |
| inputs = processor(prompt, image, return_tensors="pt").to("cuda", torch.float16) | |
| 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() | |
| buffer = "" | |
| for new_text in streamer: | |
| buffer += new_text | |
| generated_text_without_prompt = buffer#[len(ext_buffer):] | |
| time.sleep(0.01) | |
| yield buffer | |
| demo = gr.ChatInterface(fn=bot_streaming, title="Chameleon 🦎", examples=[ | |
| {"text": "There are two images in the input. What is the relationship between this image and this image?", "files":["./bee.jpg", "./depth-bee.png"]}, | |
| {"text": "What is on the flower?", "files":["./bee.jpg"]}], | |
| textbox=gr.MultimodalTextbox(file_count="multiple"), | |
| description="Try Chameleon-30B by Meta in this demo. Upload image(s), 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) | |