import gradio as gr import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, StoppingCriteria import gradio as gr import spaces import torch import numpy as np import torch import torchvision.transforms as T from PIL import Image from torchvision.transforms.functional import InterpolationMode from transformers import AutoModel, AutoTokenizer from threading import Thread import re import time from PIL import Image import torch import spaces import subprocess import os subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) torch.set_default_device('cuda') 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 model = AutoModel.from_pretrained( "5CD-AI/Vintern-3B-beta", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True, ).eval().cuda() tokenizer = AutoTokenizer.from_pretrained("5CD-AI/Vintern-3B-beta", trust_remote_code=True, use_fast=False) @spaces.GPU def chat(message, history): print("history",history) print("message",message) if len(history) != 0 and len(message["files"]) != 0: return """Chúng tôi hiện chỉ hổ trợ 1 ảnh ở đầu ngữ cảnh! Vui lòng tạo mới cuộc trò chuyện. We currently only support one image at the start of the context! Please start a new conversation.""" if len(history) == 0 and len(message["files"]) != 0: test_image = message["files"][0]["path"] pixel_values = load_image(test_image, max_num=6).to(torch.bfloat16).cuda() elif len(history) == 0 and len(message["files"]) == 0: pixel_values = None elif history[0][0][0] is not None and os.path.isfile(history[0][0][0]): test_image = history[0][0][0] pixel_values = load_image(test_image, max_num=6).to(torch.bfloat16).cuda() else: pixel_values = None generation_config = dict(max_new_tokens= 512, do_sample=False, num_beams = 3, repetition_penalty=2.0) if len(history) == 0: if pixel_values is not None: question = '\n'+message["text"] else: question = message["text"] response, conv_history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True) else: conv_history = [] if history[0][0][0] is not None and os.path.isfile(history[0][0][0]): start_index = 1 else: start_index = 0 for i, chat_pair in enumerate(history[start_index:]): if i == 0 and start_index == 1: conv_history.append(tuple(['\n'+chat_pair[0],chat_pair[1]])) else: conv_history.append(tuple(chat_pair)) print("conv_history",conv_history) 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 # buffer = "" # for new_text in response: # buffer += new_text # generated_text_without_prompt = buffer[:] # time.sleep(0.005) # yield generated_text_without_prompt CSS =""" # @media only screen and (max-width: 600px){ # #component-3 { # height: 90dvh !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); # } # } #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-3B-beta](https://huggingface.co/5CD-AI/Vintern-3B-beta) in this demo. Vintern-3B-beta consists of [InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px), an MLP projector, and [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct). Bias, Risks, and Limitations The model might have biases because it learned from data that could be biased. Users should be cautious of these possible biases when using the model.""", examples=[{"text": "Mô tả hình ảnh.", "files":["./demo_3.jpg"]}, {"text": "Trích xuất các thông tin từ ảnh.", "files":["./demo_1.jpg"]}, {"text": "Mô tả hình ảnh một cách chi tiết.", "files":["./demo_2.jpg"]}], title="❄️ Vintern-3B-beta Test ❄️", multimodal=True, css=CSS ) demo.queue().launch()