from threading import Thread from typing import Iterator from transformers import AutoModel, AutoTokenizer, AutoImageProcessor, TextIteratorStreamer from PIL import Image as PILImage import tempfile import torch import gradio as gr def get_gradio_demo(model, tokenizer, image_processor) -> gr.Interface: def get_prompt(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> str: texts = [f'#instruction: {system_prompt}\n', '#context:\n'] texts += [f"human: {user_input.strip()}\nagent: {response.strip()}\n" for user_input, response in chat_history if isinstance(user_input, str)] texts += [f'human: {message.strip()}'] return ''.join(texts) def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int: prompt = get_prompt(message, chat_history, system_prompt) input_ids = tokenizer([prompt], return_tensors='np', add_special_tokens=False)['input_ids'] return input_ids.shape[-1] def run(image: PILImage.Image, message: str, chat_history: list[tuple[str, str]], system_prompt: str, max_new_tokens: int = 192, temperature: float = 0.1, top_p: float = 0.9, top_k: int = 50) -> Iterator[str]: prompt = get_prompt(message, chat_history, system_prompt) patch_images = image_processor([image], return_tensors="pt").pixel_values.to(torch.float16).to('cuda') inputs = tokenizer([prompt], return_tensors='pt').to('cuda') streamer = TextIteratorStreamer(tokenizer, timeout=10.) # generate_kwargs = dict( inputs, patch_images=patch_images, streamer=streamer, max_length=max_new_tokens, do_sample=True, top_p=top_p, top_k=top_k, temperature=temperature, num_beams=1, ) t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() outputs = [] for text in streamer: outputs.append(text) yield ''.join(outputs).replace("not yet.", "").replace("", "").replace("", "").strip() # ------- DEFAULT_SYSTEM_PROMPT = """can you specify which region the context describes?""" MAX_MAX_NEW_TOKENS = 512 DEFAULT_MAX_NEW_TOKENS = 128 MAX_INPUT_TOKEN_LENGTH = 512 DESCRIPTION = """

TiO Demo

https://huggingface.co./jxu124/TiO
""" LICENSE = """

--- """ if not torch.cuda.is_available(): DESCRIPTION += '\n

Running on CPU đŸĨļ This demo does not work on CPU.

' def upload_image(file_obj): chatbot = [[(file_obj.name,), None]] return (gr.update(visible=False), gr.update(interactive=True, placeholder='Type a message...',), chatbot) def clear_and_save_textbox(message: str) -> tuple[str, str]: return '', message def display_input(message: str, history: list[tuple[str, str]]) -> list[tuple[str, str]]: if len(history) == 0: raise gr.Error(f'Upload an image first and try again.') history.append((message, '')) return history def delete_prev_fn( history: list[tuple[str, str]]) -> tuple[list[tuple[str, str]], str]: try: message, _ = history.pop() if not isinstance(message, str): message, _ = history.pop() except IndexError: message = '' return history, message or '' def generate( message: str, history_with_input: list[tuple[str, str]], system_prompt: str, max_new_tokens: int, temperature: float, top_p: float, top_k: int, ) -> Iterator[list[tuple[str, str]]]: if max_new_tokens > MAX_MAX_NEW_TOKENS: raise ValueError image = PILImage.open(history_with_input[0][0][0]) history = history_with_input[:-1] generator = run(image, message, history, system_prompt, max_new_tokens, temperature, top_p, top_k) try: first_response = next(generator) yield history + [(message, first_response)] except StopIteration: yield history + [(message, '')] for response in generator: if "region:" in response: bboxes = model.utils.sbbox_to_bbox(response) if len(bboxes): with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f: model.utils.show_mask(image, bboxes).save(f) chatbot = history + [(message, "OK, I see."), (None, (f.name,))] else: chatbot = history + [(message, response)] yield chatbot def process_example(message: str) -> tuple[str, list[tuple[str, str]]]: generator = generate(message, [], DEFAULT_SYSTEM_PROMPT, 192, 1, 0.95, 50) for x in generator: pass return '', x def check_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> None: input_token_length = get_input_token_length(message, chat_history[:-1], system_prompt) if input_token_length > MAX_INPUT_TOKEN_LENGTH: raise gr.Error(f'The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.') with gr.Blocks() as demo: gr.Markdown(DESCRIPTION) with gr.Group(): chatbot = gr.Chatbot(label='Chatbot') imagebox = gr.File( file_types=["image"], show_label=False, ) with gr.Row(): textbox = gr.Textbox( container=False, show_label=False, interactive=False, placeholder='Upload an image...', scale=10, ) submit_button = gr.Button('Submit', variant='primary', scale=1, min_width=0) with gr.Row(): retry_button = gr.Button('🔄 Retry', variant='secondary') undo_button = gr.Button('↩ī¸ Undo', variant='secondary') clear_button = gr.Button('🗑ī¸ Clear', variant='secondary') saved_input = gr.State() with gr.Accordion(label='Advanced options', open=False): system_prompt = gr.Textbox(label='System prompt', value=DEFAULT_SYSTEM_PROMPT, lines=6) max_new_tokens = gr.Slider( label='Max new tokens', minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS, ) temperature = gr.Slider( label='Temperature', minimum=0.1, maximum=4.0, step=0.1, value=0.5, ) top_p = gr.Slider( label='Top-p (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9, ) top_k = gr.Slider( label='Top-k', minimum=1, maximum=1000, step=1, value=20, ) gr.Markdown(LICENSE) imagebox.upload( fn=upload_image, inputs=imagebox, outputs=[imagebox, textbox, chatbot], api_name=None, queue=False, ) textbox.submit( fn=clear_and_save_textbox, inputs=textbox, outputs=[textbox, saved_input], api_name=None, queue=False, ).then( fn=display_input, inputs=[saved_input, chatbot], outputs=chatbot, api_name=None, queue=False, ).then( fn=check_input_token_length, inputs=[saved_input, chatbot, system_prompt], api_name=None, queue=False, ).success( fn=generate, inputs=[ saved_input, chatbot, system_prompt, max_new_tokens, temperature, top_p, top_k, ], outputs=chatbot, api_name="generate", ) button_event_preprocess = submit_button.click( fn=clear_and_save_textbox, inputs=textbox, outputs=[textbox, saved_input], api_name=None, queue=False, ).then( fn=display_input, inputs=[saved_input, chatbot], outputs=chatbot, api_name=None, queue=False, ).then( fn=check_input_token_length, inputs=[saved_input, chatbot, system_prompt], api_name=None, queue=False, ).success( fn=generate, inputs=[ saved_input, chatbot, system_prompt, max_new_tokens, temperature, top_p, top_k, ], outputs=chatbot, api_name=None, ) retry_button.click( fn=delete_prev_fn, inputs=chatbot, outputs=[chatbot, saved_input], api_name=None, queue=False, ).then( fn=display_input, inputs=[saved_input, chatbot], outputs=chatbot, api_name=None, queue=False, ).then( fn=generate, inputs=[ saved_input, chatbot, system_prompt, max_new_tokens, temperature, top_p, top_k, ], outputs=chatbot, api_name=None, ) undo_button.click( fn=delete_prev_fn, inputs=chatbot, outputs=[chatbot, saved_input], api_name=None, queue=False, ).then( fn=lambda x: x, inputs=[saved_input], outputs=textbox, api_name=None, queue=False, ) clear_button.click( fn=lambda: ([], '', gr.update(value=None, visible=True), gr.update(interactive=False, placeholder='Upload an image...',)), outputs=[chatbot, saved_input, imagebox, textbox], queue=False, api_name=None, ) return demo def main(model_id: str = 'jxu124/TiO', host: str = "0.0.0.0", port: int = None): assert torch.cuda.is_available() model = AutoModel.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16).cuda() tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False) image_processor = AutoImageProcessor.from_pretrained(model_id) # ---- gradio demo ---- model.get_gradio_demo(tokenizer, image_processor).queue(max_size=20).launch(server_name=host, server_port=port) if __name__ == "__main__": import fire fire.Fire(main)