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| #!/usr/bin/env python | |
| import gradio as gr | |
| from PIL import Image | |
| import os | |
| import json | |
| from model import is_chinese, get_infer_setting, generate_input, chat | |
| import torch | |
| def generate_text_with_image(input_text, image, history=[], request_data=dict(), is_zh=True): | |
| input_para = { | |
| "max_length": 2048, | |
| "min_length": 50, | |
| "temperature": 0.8, | |
| "top_p": 0.4, | |
| "top_k": 100, | |
| "repetition_penalty": 1.2 | |
| } | |
| input_para.update(request_data) | |
| input_data = generate_input(input_text, image, history, input_para, image_is_encoded=False) | |
| input_image, gen_kwargs = input_data['input_image'], input_data['gen_kwargs'] | |
| with torch.no_grad(): | |
| answer, history, _ = chat(None, model, tokenizer, input_text, history=history, image=input_image, \ | |
| max_length=gen_kwargs['max_length'], top_p=gen_kwargs['top_p'], \ | |
| top_k = gen_kwargs['top_k'], temperature=gen_kwargs['temperature'], english=not is_zh) | |
| return answer | |
| def request_model(input_text, temperature, top_p, image_prompt, result_previous): | |
| result_text = [(ele[0], ele[1]) for ele in result_previous] | |
| for i in range(len(result_text)-1, -1, -1): | |
| if result_text[i][0] == "" or result_text[i][1] == "": | |
| del result_text[i] | |
| print(f"history {result_text}") | |
| is_zh = is_chinese(input_text) | |
| if image_prompt is None: | |
| 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 | |
| elif input_text == "": | |
| result_text.append((input_text, 'Text empty! Please enter text and retry.')) | |
| return "", result_text | |
| request_para = {"temperature": temperature, "top_p": top_p} | |
| image = Image.open(image_prompt) | |
| try: | |
| answer = generate_text_with_image(input_text, image, result_text.copy(), request_para, is_zh) | |
| except Exception as e: | |
| print(f"error: {e}") | |
| if is_zh: | |
| result_text.append((input_text, '超时！请稍等几分钟再重试。')) | |
| else: | |
| result_text.append((input_text, 'Timeout! Please wait a few minutes and retry.')) | |
| return "", result_text | |
| result_text.append((input_text, answer)) | |
| print(result_text) | |
| return "", result_text | |
| DESCRIPTION = '''# <a href="https://github.com/THUDM/VisualGLM-6B">VisualGLM</a>''' | |
| 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 <a href="https://github.com/THUDM/VisualGLM-6B">https://github.com/THUDM/VisualGLM-6B</a>. It would be recommended to check out the repo if you want to see the detail of our model and training process.' | |
| def clear_fn(value): | |
| return "", [("", "Hi, What do you want to know about this image?")], None | |
| def clear_fn2(value): | |
| return [("", "Hi, What do you want to know about this image?")] | |
| def main(args): | |
| gr.close_all() | |
| global model, tokenizer | |
| model, tokenizer = get_infer_setting(gpu_device=0, quant=args.quant) | |
| with gr.Blocks(css='style.css') as demo: | |
| gr.Markdown(DESCRIPTION) | |
| 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=0.8, minimum=0, label='Temperature') | |
| top_p = gr.Slider(maximum=1, value=0.4, minimum=0, label='Top P') | |
| with gr.Group(): | |
| with gr.Row(): | |
| maintenance_notice = gr.Markdown(MAINTENANCE_NOTICE1) | |
| with gr.Column(scale=5.5): | |
| result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[("", "Hi, What do you want to know about this image?")]).style(height=550) | |
| gr.Markdown(NOTES) | |
| print(gr.__version__) | |
| run_button.click(fn=request_model,inputs=[input_text, temperature, top_p, image_prompt, result_text], | |
| outputs=[input_text, result_text]) | |
| input_text.submit(fn=request_model,inputs=[input_text, temperature, top_p, image_prompt, result_text], | |
| outputs=[input_text, result_text]) | |
| 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(share=args.share) | |
| if __name__ == '__main__': | |
| import argparse | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--quant", choices=[8, 4], type=int, default=None) | |
| parser.add_argument("--share", action="store_true") | |
| args = parser.parse_args() | |
| args.share = "True" | |
| main(args) | |