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| import os | |
| os.system("cp /home/user/.pyenv/versions/3.10.13/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so /home/user/.pyenv/versions/3.10.13/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so") | |
| import gradio as gr | |
| import mdtex2html | |
| from model.openllama import OpenLLAMAPEFTModel | |
| import torch | |
| from io import BytesIO | |
| from PIL import Image as PILImage | |
| import cv2 | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| from torchvision import transforms | |
| # init the model | |
| args = { | |
| 'model': 'openllama_peft', | |
| 'imagebind_ckpt_path': './pretrained_ckpt/imagebind_ckpt/imagebind_huge.pth', | |
| 'vicuna_ckpt_path': './pretrained_ckpt/vicuna_ckpt/7b_v0', | |
| 'anomalygpt_ckpt_path': './ckpt/train_supervised/pytorch_model.pt', | |
| 'delta_ckpt_path': './pretrained_ckpt/pandagpt_ckpt/7b/pytorch_model.pt', | |
| 'stage': 2, | |
| 'max_tgt_len': 128, | |
| 'lora_r': 32, | |
| 'lora_alpha': 32, | |
| 'lora_dropout': 0.1 | |
| } | |
| model = OpenLLAMAPEFTModel(**args) | |
| delta_ckpt = torch.load(args['delta_ckpt_path'], map_location=torch.device('cpu')) | |
| model.load_state_dict(delta_ckpt, strict=False) | |
| delta_ckpt = torch.load(args['anomalygpt_ckpt_path'], map_location=torch.device('cpu')) | |
| model.load_state_dict(delta_ckpt, strict=False) | |
| model = model.eval()#.half()#.cuda() | |
| # model.image_decoder = model.image_decoder.cuda() | |
| # model.prompt_learner = model.prompt_learner.cuda() | |
| """Override Chatbot.postprocess""" | |
| def postprocess(self, y): | |
| if y is None: | |
| return [] | |
| for i, (message, response) in enumerate(y): | |
| y[i] = ( | |
| None if message is None else mdtex2html.convert((message)), | |
| None if response is None else mdtex2html.convert(response), | |
| ) | |
| return y | |
| gr.Chatbot.postprocess = postprocess | |
| def parse_text(text): | |
| """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/""" | |
| lines = text.split("\n") | |
| lines = [line for line in lines if line != ""] | |
| count = 0 | |
| for i, line in enumerate(lines): | |
| if "```" in line: | |
| count += 1 | |
| items = line.split('`') | |
| if count % 2 == 1: | |
| lines[i] = f'<pre><code class="language-{items[-1]}">' | |
| else: | |
| lines[i] = f'<br></code></pre>' | |
| else: | |
| if i > 0: | |
| if count % 2 == 1: | |
| line = line.replace("`", "\`") | |
| line = line.replace("<", "<") | |
| line = line.replace(">", ">") | |
| line = line.replace(" ", " ") | |
| line = line.replace("*", "*") | |
| line = line.replace("_", "_") | |
| line = line.replace("-", "-") | |
| line = line.replace(".", ".") | |
| line = line.replace("!", "!") | |
| line = line.replace("(", "(") | |
| line = line.replace(")", ")") | |
| line = line.replace("$", "$") | |
| lines[i] = "<br>"+line | |
| text = "".join(lines) | |
| return text | |
| def predict( | |
| input, | |
| image_path, | |
| normal_img_path, | |
| chatbot, | |
| max_length, | |
| top_p, | |
| temperature, | |
| history, | |
| modality_cache, | |
| ): | |
| if image_path is None and normal_img_path is None: | |
| return [(input, "There is no input data provided! Please upload your data and start the conversation.")] | |
| else: | |
| print(f'[!] image path: {image_path}\n[!] normal image path: {normal_img_path}\n') | |
| # prepare the prompt | |
| prompt_text = '' | |
| for idx, (q, a) in enumerate(history): | |
| if idx == 0: | |
| prompt_text += f'{q}\n### Assistant: {a}\n###' | |
| else: | |
| prompt_text += f' Human: {q}\n### Assistant: {a}\n###' | |
| if len(history) == 0: | |
| prompt_text += f'{input}' | |
| else: | |
| prompt_text += f' Human: {input}' | |
| response, pixel_output = model.generate({ | |
| 'prompt': prompt_text, | |
| 'image_paths': [image_path] if image_path else [], | |
| 'normal_img_paths': [normal_img_path] if normal_img_path else [], | |
| 'audio_paths': [], | |
| 'video_paths': [], | |
| 'thermal_paths': [], | |
| 'top_p': top_p, | |
| 'temperature': temperature, | |
| 'max_tgt_len': max_length, | |
| 'modality_embeds': modality_cache | |
| },web_demo=True) | |
| chatbot.append((parse_text(input), parse_text(response))) | |
| history.append((input, response)) | |
| plt.imshow(pixel_output.to(torch.float16).reshape(224,224).detach().cpu(), cmap='binary_r') | |
| plt.axis('off') | |
| plt.savefig('output.png',bbox_inches='tight',pad_inches = 0) | |
| target_size = 435 | |
| original_width, original_height = PILImage.open(image_path).size | |
| if original_width > original_height: | |
| new_width = target_size | |
| new_height = int(target_size * (original_height / original_width)) | |
| else: | |
| new_height = target_size | |
| new_width = int(target_size * (original_width / original_height)) | |
| new_image = PILImage.new('L', (target_size, target_size), 255) # 'L' mode for grayscale | |
| paste_x = (target_size - new_width) // 2 | |
| paste_y = (target_size - new_height) // 2 | |
| pixel_output = PILImage.open('output.png').resize((new_width, new_height), PILImage.LANCZOS) | |
| new_image.paste(pixel_output, (paste_x, paste_y)) | |
| new_image.save('output.png') | |
| image = cv2.imread('output.png', cv2.IMREAD_GRAYSCALE) | |
| kernel = np.ones((3, 3), np.uint8) | |
| eroded_image = cv2.erode(image, kernel, iterations=1) | |
| cv2.imwrite('output.png', eroded_image) | |
| output = PILImage.open('output.png').convert('L') | |
| return chatbot, history, modality_cache, output | |
| def reset_user_input(): | |
| return gr.update(value='') | |
| def reset_state(): | |
| return gr.update(value=''), None, None, [], [], [], PILImage.open('ffffff.png') | |
| examples = ['hazelnut_cut.png','capsule_crack.png','carpet_normal.jpg'] | |
| with gr.Blocks() as demo: | |
| gr.HTML("""<h1 align="center">Demo of AnomalyGPT</h1>""") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| with gr.Row(): | |
| image_path = gr.Image(type="filepath", label="Query Image", value=examples[0]) | |
| with gr.Row(): | |
| normal_img_path = gr.Image(type="filepath", label="Normal Image (optional)", value=None) | |
| with gr.Row(): | |
| gr.Examples(examples=examples, inputs=[image_path]) | |
| with gr.Row(): | |
| max_length = gr.Slider(0, 512, value=512, step=1.0, label="Max length", interactive=True) | |
| top_p = gr.Slider(0, 1, value=0.01, step=0.01, label="Top P", interactive=True) | |
| temperature = gr.Slider(0, 1, value=1.0, step=0.01, label="Temperature", interactive=True) | |
| with gr.Column(scale=3): | |
| with gr.Row(): | |
| with gr.Column(scale=6): | |
| chatbot = gr.Chatbot().style(height=440) | |
| with gr.Column(scale=4): | |
| # gr.Image(output) | |
| image_output = gr.Image(interactive=False, label="Localization Output", type='pil',value=PILImage.open('ffffff.png')) | |
| with gr.Row(): | |
| user_input = gr.Textbox(show_label=False, placeholder="Input...", lines=12).style(container=False) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| submitBtn = gr.Button("Submit", variant="primary") | |
| with gr.Column(scale=1): | |
| emptyBtn = gr.Button("Clear History") | |
| history = gr.State([]) | |
| modality_cache = gr.State([]) | |
| submitBtn.click( | |
| predict, [ | |
| user_input, | |
| image_path, | |
| normal_img_path, | |
| chatbot, | |
| max_length, | |
| top_p, | |
| temperature, | |
| history, | |
| modality_cache, | |
| ], [ | |
| chatbot, | |
| history, | |
| modality_cache, | |
| image_output | |
| ], | |
| show_progress=True | |
| ) | |
| submitBtn.click(reset_user_input, [], [user_input]) | |
| emptyBtn.click(reset_state, outputs=[ | |
| user_input, | |
| image_path, | |
| normal_img_path, | |
| chatbot, | |
| history, | |
| modality_cache, | |
| image_output | |
| ], show_progress=True) | |
| demo.queue().launch() | |