#!/usr/bin/env python # this code modify from https://huggingface.co/spaces/lykeven/visualglm-6b import gradio as gr import re from PIL import Image import torch from io import BytesIO import hashlib import os from transformers import LlamaForCausalLM, LlamaTokenizer, BlipImageProcessor, BitsAndBytesConfig, AutoModelForCausalLM DESCRIPTION = '''# Ziya-Blip2-14B''' MAINTENANCE_NOTICE1 = 'Hint 1: If the app report "Something went wrong, connection error out", please turn off your proxy and retry.\nHint 2: If you upload a large size of image like 10MB, it may take some time to upload and process. Please be patient and wait.' MAINTENANCE_NOTICE2 = '提示1: 如果应用报了“Something went wrong, connection error out”的错误，请关闭代理并重试。\n提示2: 如果你上传了很大的图片，比如10MB大小，那将需要一些时间来上传和处理，请耐心等待。' NOTES = 'This app is adapted from https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1. It would be recommended to check out the repo if you want to see the detail of our model. And most of the codes attach to this demo are modify from lykeven/visualglm-6b.' import json default_chatbox = [] def is_chinese(text): zh_pattern = re.compile(u'[\u4e00-\u9fa5]+') return zh_pattern.search(text) AUTH_TOKEN = os.getenv("AUTH_TOKEN") LM_MODEL_PATH = "gxy/Ziya-LLaMA-13B-v1" lm_model = LlamaForCausalLM.from_pretrained( LM_MODEL_PATH, device_map="auto", torch_dtype=torch.float16, use_auth_token=AUTH_TOKEN, quantization_config=BitsAndBytesConfig(load_in_8bit=True)) tokenizer = LlamaTokenizer.from_pretrained(LM_MODEL_PATH, use_auth_token=AUTH_TOKEN) # visual model OPENAI_CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073] OPENAI_CLIP_STD = [0.26862954, 0.26130258, 0.27577711] # demo.py is in the project path, so we can use local path ".". Otherwise you should use "IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1" model = AutoModelForCausalLM.from_pretrained( "IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1", trust_remote_code=True, use_auth_token=AUTH_TOKEN, torch_dtype=torch.float16) model.cuda() # if you use on cpu, comment this line model.language_model = lm_model image_size = model.config.vision_config.image_size image_processor = BlipImageProcessor( size={"height": image_size, "width": image_size}, image_mean=OPENAI_CLIP_MEAN, image_std=OPENAI_CLIP_STD, ) def post( input_text, temperature, top_p, image_prompt, result_previous, hidden_image ): result_text = [(ele[0], ele[1]) for ele in result_previous] previous_querys = [] previous_outputs = [] for i in range(len(result_text)-1, -1, -1): if result_text[i][0] == "": del result_text[i] else: previous_querys.append(result_text[i][0]) previous_outputs.append(result_text[i][1]) is_zh = is_chinese(input_text) if image_prompt is None: print("Image empty") if is_zh: result_text.append((input_text, '图片为空！请上传图片并重试。')) else: result_text.append((input_text, 'Image empty! Please upload a image and retry.')) return input_text, result_text, hidden_image elif input_text == "": print("Text empty") result_text.append((input_text, 'Text empty! Please enter text and retry.')) return "", result_text, hidden_image generate_config = { "max_new_tokens": 128, "top_p": top_p, "temperature": temperature, "repetition_penalty": 1.18, } img = Image.open(image_prompt) pixel_values = image_processor( img, return_tensors="pt").pixel_values.to( model.device).to(model.dtype) output_buffer = BytesIO() img.save(output_buffer, "PNG") byte_data = output_buffer.getvalue() md = hashlib.md5() md.update(byte_data) img_hash = md.hexdigest() if img_hash != hidden_image: previous_querys = [] previous_outputs = [] result_text = [] answer = model.chat( tokenizer=tokenizer, pixel_values=pixel_values, query=input_text, previous_querys=previous_querys, previous_outputs=previous_outputs, **generate_config, ) result_text.append((input_text, answer)) print(result_text) return "", result_text, img_hash def clear_fn(value): return "", default_chatbox, None def clear_fn2(value): return default_chatbox def io_fn(a, b, c): print(f"call io_fn") return a, b def change_language(value): if value == "Change hint to English": return "提示变为中文", MAINTENANCE_NOTICE1 else: return "Change hint to English", MAINTENANCE_NOTICE2 def main(): gr.close_all() examples = [] with open("./examples/example_inputs.jsonl") as f: for line in f: data = json.loads(line) examples.append(data) with gr.Blocks(css='style.css') as demo: with gr.Row(): with gr.Column(scale=4.5): with gr.Group(): input_text = gr.Textbox(label='Input Text', placeholder='Please enter text prompt below and press ENTER.') with gr.Row(): run_button = gr.Button('Generate') clear_button = gr.Button('Clear') image_prompt = gr.Image(type="filepath", label="Image Prompt", value=None) with gr.Row(): temperature = gr.Slider(maximum=1, value=0.7, minimum=0, label='Temperature') top_p = gr.Slider(maximum=1, value=0.1, minimum=0, label='Top P') with gr.Group(): with gr.Row(): with gr.Column(scale=7): maintenance_notice = gr.Markdown(MAINTENANCE_NOTICE1) with gr.Column(scale=2): change_button = gr.Button('Change hint to English', visible=False) with gr.Column(scale=5.5): result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[]).style(height=550) hidden_image_hash = gr.Textbox(visible=False) gr_examples = gr.Examples(examples=[[example["text"], example["image"]] for example in examples], inputs=[input_text, image_prompt], label="Example Inputs (Click to insert an examplet into the input box)", examples_per_page=3) gr.Markdown(NOTES) print(gr.__version__) run_button.click(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash], outputs=[input_text, result_text, hidden_image_hash]) input_text.submit(fn=post,inputs=[input_text, temperature, top_p, image_prompt, result_text, hidden_image_hash], outputs=[input_text, result_text, hidden_image_hash]) clear_button.click(fn=clear_fn, inputs=clear_button, outputs=[input_text, result_text, image_prompt]) image_prompt.upload(fn=clear_fn2, inputs=clear_button, outputs=[result_text]) image_prompt.clear(fn=clear_fn2, inputs=clear_button, outputs=[result_text]) print(gr.__version__) demo.queue(concurrency_count=10) demo.launch(server_name="0.0.0.0") if __name__ == '__main__': main()