import os import string import copy import gradio as gr import PIL.Image import torch from transformers import BitsAndBytesConfig, pipeline import re import time model_id = "llava-hf/llava-1.5-7b-hf" quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16 ) pipe = pipeline("image-to-text", model=model_id, model_kwargs={"quantization_config": quantization_config}) def extract_response_pairs(text): turns = re.split(r'(USER:|ASSISTANT:)', text)[1:] turns = [turn.strip() for turn in turns if turn.strip()] conv_list = [] for i in range(0, len(turns[1::2]), 2): if i + 1 < len(turns[1::2]): conv_list.append([turns[1::2][i].lstrip(":"), turns[1::2][i + 1].lstrip(":")]) return conv_list def add_text(history, text): history = history.append([text, None]) return history, text def infer(image, prompt, temperature, length_penalty, repetition_penalty, max_length, min_length, top_p): print("类型是:",type(image)) outputs = pipe(images=image, prompt=prompt, generate_kwargs={"temperature":temperature, "length_penalty":length_penalty, "repetition_penalty":repetition_penalty, "max_length":max_length, "min_length":min_length, "top_p":top_p}) inference_output = outputs[0]["generated_text"] return inference_output def bot(history_chat, text_input, image, temperature, length_penalty, repetition_penalty, max_length, min_length, top_p): if text_input == "": gr.Warning("Please input text") if image==None: gr.Warning("Please input image or wait for image to be uploaded before clicking submit.") chat_history = " ".join(history_chat) # history as a str to be passed to model chat_history = chat_history + f"USER: \n{text_input}\nASSISTANT:" # add text input for prompting inference_result = infer(image, chat_history, temperature, length_penalty, repetition_penalty, max_length, min_length, top_p) # return inference and parse for new history chat_val = extract_response_pairs(inference_result) # create history list for yielding the last inference response chat_state_list = copy.deepcopy(chat_val) chat_state_list[-1][1] = "" # empty last response # add characters iteratively for character in chat_val[-1][1]: chat_state_list[-1][1] += character time.sleep(0.05) # yield history but with last response being streamed yield chat_state_list css = """ #mkd { height: 500px; overflow: auto; border: 1px solid #ccc; } """ with gr.Blocks(css="style.css") as demo: gr.Markdown(DESCRIPTION) gr.Markdown("""## LLaVA, one of the greatest multimodal chat models is now available in Transformers with 4-bit quantization! ⚡️ See the docs here: https://huggingface.co/docs/transformers/main/en/model_doc/llava.""") chatbot = gr.Chatbot(label="Chat", show_label=False) gr.Markdown("Input image and text and start chatting 👇") with gr.Row(): image = gr.Image(type="pil") text_input = gr.Text(label="Chat Input", show_label=False, max_lines=3, container=False) history_chat = gr.State(value=[]) with gr.Accordion(label="Advanced settings", open=False): temperature = gr.Slider( label="Temperature", info="Used with nucleus sampling.", minimum=0.5, maximum=1.0, step=0.1, value=1.0, ) length_penalty = gr.Slider( label="Length Penalty", info="Set to larger for longer sequence, used with beam search.", minimum=-1.0, maximum=2.0, step=0.2, value=1.0, ) repetition_penalty = gr.Slider( label="Repetition Penalty", info="Larger value prevents repetition.", minimum=1.0, maximum=5.0, step=0.5, value=1.5, ) max_length = gr.Slider( label="Max Length", minimum=1, maximum=500, step=1, value=200, ) min_length = gr.Slider( label="Minimum Length", minimum=1, maximum=100, step=1, value=1, ) top_p = gr.Slider( label="Top P", info="Used with nucleus sampling.", minimum=0.5, maximum=1.0, step=0.1, value=0.9, ) chat_output = [ chatbot, history_chat ] chat_inputs = [ image, text_input, temperature, length_penalty, repetition_penalty, max_length, min_length, top_p, history_chat ] with gr.Row(): clear_chat_button = gr.Button("Clear") cancel_btn = gr.Button("Stop Generation") chat_button = gr.Button("Submit", variant="primary") chat_event1 = chat_button.click(add_text, [chatbot, text_input], [chatbot, text_input]).then(bot, [chatbot, text_input, image, temperature, length_penalty, repetition_penalty, max_length, min_length, top_p], chatbot) chat_event2 = text_input.submit( add_text, [chatbot, text_input], [chatbot, text_input] ).then( fn=bot, inputs=[chatbot, text_input, image, temperature, length_penalty, repetition_penalty, max_length, min_length, top_p], outputs=chatbot ) clear_chat_button.click( fn=lambda: ([], []), inputs=None, outputs=[ chatbot, history_chat ], queue=False, api_name="clear", ) image.change( fn=lambda: ([], []), inputs=None, outputs=[ chatbot, history_chat ], queue=False) cancel_btn.click( None, [], [], cancels=[chat_event1, chat_event2] ) examples = [["./examples/baklava.png", "How to make this pastry?"],["./examples/bee.png","Describe this image."]] gr.Examples(examples=examples, inputs=[image, text_input, chat_inputs]) if __name__ == "__main__": demo.queue(max_size=10).launch(debug=True)