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| #!/usr/bin/env python | |
| import gradio as gr | |
| import os | |
| import re | |
| from PIL import Image | |
| import base64 | |
| import time | |
| DESCRIPTION = '''# <a href="https://github.com/THUDM/VisualGLM">VisualGLM</a>''' | |
| MAINTENANCE_NOTICE='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.' | |
| NOTES = 'This app is adapted from <a href="https://github.com/THUDM/VisualGLM">https://github.com/THUDM/VisualGLM</a>. It would be recommended to check out the repo if you want to see the detail of our model and training process.' | |
| import json | |
| import requests | |
| import base64 | |
| URL = os.environ.get("URL") | |
| def process_image(image_prompt): | |
| image = Image.open(image_prompt) | |
| print(f"height:{image.height}, width:{image.width}") | |
| resized_image = image.resize((224, 224), ) | |
| timestamp = int(time.time()) | |
| file_ext = os.path.splitext(image_prompt)[1] | |
| filename = f"examples/{timestamp}{file_ext}" | |
| resized_image.save(filename) | |
| print(f"temporal filename {filename}") | |
| with open(filename, "rb") as image_file: | |
| encoded_img = str(base64.b64encode(image_file.read()), encoding='utf-8') | |
| os.remove(filename) | |
| return encoded_img | |
| def is_chinese(text): | |
| zh_pattern = re.compile(u'[\u4e00-\u9fa5]+') | |
| return zh_pattern.search(text) | |
| def post( | |
| 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] == "": | |
| del result_text[i] | |
| print(f"history {result_text}") | |
| 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 | |
| elif input_text == "": | |
| print("Text empty") | |
| result_text.append((input_text, 'Text empty! Please enter text and retry.')) | |
| return "", result_text | |
| headers = { | |
| "Content-Type": "application/json; charset=UTF-8", | |
| "User-Agent": "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.87 Safari/537.36", | |
| } | |
| if image_prompt: | |
| encoded_img = process_image(image_prompt) | |
| else: | |
| encoded_img = None | |
| print('开始请求...') | |
| data = json.dumps({ | |
| 'text': input_text, | |
| 'image_prompt': encoded_img, | |
| 'temperature': temperature, | |
| 'top_p': top_p, | |
| 'history': result_text | |
| }) | |
| try: | |
| response = requests.request("POST", URL, headers=headers, data=data, timeout=(60, 100)).json() | |
| except Exception as e: | |
| print("error message", 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 | |
| print('请求完毕...') | |
| answer = str(response['result']) | |
| result_text.append((input_text, answer)) | |
| print(result_text) | |
| print('finished') | |
| return "", result_text | |
| 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(): | |
| 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: | |
| gr.Markdown(DESCRIPTION) | |
| gr.Markdown(MAINTENANCE_NOTICE) | |
| with gr.Row(): | |
| with gr.Column(): | |
| 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=0.95, minimum=0, label='Temperature') | |
| top_p = gr.Slider(maximum=1, value=0.7, minimum=0, label='Top P') | |
| result_text = gr.components.Chatbot(label='Multi-round conversation History', value=[("", "Hi, What do you want to know about this image?")]) | |
| 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], | |
| outputs=[input_text, result_text]) | |
| input_text.submit(fn=post,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.change(fn=clear_fn2, inputs=clear_button, outputs=[result_text]) | |
| print(gr.__version__) | |
| demo.queue(concurrency_count=10) | |
| demo.launch() | |
| if __name__ == '__main__': | |
| main() |