import gradio as gr import os import numpy as np import torch import torchvision.transforms as T # from decord import VideoReader, cpu from PIL import Image from torchvision.transforms.functional import InterpolationMode from transformers import AutoModel, AutoTokenizer import matplotlib.pyplot as plt import glob import spaces IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) def build_transform(input_size): MEAN, STD = IMAGENET_MEAN, IMAGENET_STD transform = T.Compose([ T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=MEAN, std=STD) ]) return transform def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): best_ratio_diff = float('inf') best_ratio = (1, 1) area = width * height for ratio in target_ratios: target_aspect_ratio = ratio[0] / ratio[1] ratio_diff = abs(aspect_ratio - target_aspect_ratio) if ratio_diff < best_ratio_diff: best_ratio_diff = ratio_diff best_ratio = ratio elif ratio_diff == best_ratio_diff: if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: best_ratio = ratio return best_ratio def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = set( (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num) target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio( aspect_ratio, target_ratios, orig_width, orig_height, image_size) # calculate the target width and height target_width = image_size * target_aspect_ratio[0] target_height = image_size * target_aspect_ratio[1] blocks = target_aspect_ratio[0] * target_aspect_ratio[1] # resize the image resized_img = image.resize((target_width, target_height)) processed_images = [] for i in range(blocks): box = ( (i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size ) # split the image split_img = resized_img.crop(box) processed_images.append(split_img) assert len(processed_images) == blocks if use_thumbnail and len(processed_images) != 1: thumbnail_img = image.resize((image_size, image_size)) processed_images.append(thumbnail_img) return processed_images def load_image(image_file, input_size=448, max_num=12): image = Image.open(image_file).convert('RGB') transform = build_transform(input_size=input_size) images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) pixel_values = [transform(image) for image in images] pixel_values = torch.stack(pixel_values) return pixel_values # Save the original cuda() method original_cuda = torch.Tensor.cuda # Define a new cuda() method def safe_cuda(self, *args, **kwargs): if torch.cuda.is_available(): return original_cuda(self, *args, **kwargs) # Use the original cuda() method else: return self # Return the tensor itself (stays on CPU) # Monkey-patch the cuda() method torch.Tensor.cuda = safe_cuda model_name = "YuukiAsuna/Vintern-1B-v2-ViTable-docvqa" model = AutoModel.from_pretrained( model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True ).eval().cuda() tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False) # @spaces.GPU def chat(message, history): print(history) print(message) if len(history) == 0 or len(message["files"]) != 0: test_image = message["files"][0] else: test_image = history[0][0][0] pixel_values = load_image(test_image, max_num=12).to(torch.bfloat16).cuda() generation_config = dict(max_new_tokens= 1024, do_sample=True, num_beams = 3, repetition_penalty=2.5) if len(history) == 0: question = '\n'+message["text"] response, conv_history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) else: conv_history = [] for chat_pair in history: if chat_pair[1] is not None: if len(conv_history) == 0 and len(message["files"]) == 0: chat_pair[0] = '\n' + chat_pair[0] conv_history.append(tuple(chat_pair)) print(conv_history) if len(message["files"]) != 0: question = '\n'+message["text"] else: question = message["text"] response, conv_history = model.chat(tokenizer, pixel_values, question, generation_config, history=conv_history, return_history=True) print(f'User: {question}\nAssistant: {response}') return response CSS =""" #component-3 { height: 50dvh !important; transform-origin: top; /* Đảm bảo rằng phần tử mở rộng từ trên xuống */ border-style: solid; overflow: hidden; flex-grow: 1; min-width: min(160px, 100%); border-width: var(--block-border-width); } /* Đảm bảo ảnh bên trong nút hiển thị đúng cách cho các nút có aria-label chỉ định */ button.svelte-1lcyrx4[aria-label="user's message: a file of type image/jpeg, "] img.svelte-1pijsyv { width: 100%; object-fit: contain; height: 100%; border-radius: 13px; /* Thêm bo góc cho ảnh */ max-width: 50vw; /* Giới hạn chiều rộng ảnh */ } /* Đặt chiều cao cho nút và cho phép chọn văn bản chỉ cho các nút có aria-label chỉ định */ button.svelte-1lcyrx4[aria-label="user's message: a file of type image/jpeg, "] { user-select: text; text-align: left; height: 300px; } /* Thêm bo góc và giới hạn chiều rộng cho ảnh không thuộc avatar container */ .message-wrap.svelte-1lcyrx4 > div.svelte-1lcyrx4 .svelte-1lcyrx4:not(.avatar-container) img { border-radius: 13px; max-width: 50vw; } .message-wrap.svelte-1lcyrx4 .message.svelte-1lcyrx4 img { margin: var(--size-2); max-height: 500px; } """ demo = gr.ChatInterface( fn=chat, description="""Try [Vintern-1B-v2-ViTable-docvqa](https://huggingface.co/YuukiAsuna/Vintern-1B-v2-ViTable-docvqa) in this demo. Vintern-1B-v2-ViTable-docvqa is a finetuned version of [Vintern-1B-v2](https://huggingface.co/5CD-AI/Vintern-1B-v2)""", title="Vintern-1B-v2-ViTable-docvqa", multimodal=True, css=CSS ) demo.queue().launch()