Update app.py

app.py

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+import numpy as np
+import torch
+import torchvision.transforms as T
+import gradio as gr
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+
+from transformers import AutoModel, AutoTokenizer
+
+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, input_size=448, max_num=12):
+    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
+
+# Load model and tokenizer globally to avoid reloading for each request
+@torch.inference_mode()
+def load_models():
+    model = AutoModel.from_pretrained(
+        "5CD-AI/Vintern-1B-v3_5",
+        torch_dtype=torch.bfloat16,
+        low_cpu_mem_usage=True,
+        trust_remote_code=True,
+        use_flash_attn=False,
+    ).eval()
+    
+    # Move model to GPU if available
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = model.to(device)
+    
+    tokenizer = AutoTokenizer.from_pretrained("5CD-AI/Vintern-1B-v3_5", trust_remote_code=True, use_fast=False)
+    
+    return model, tokenizer, device
+
+model, tokenizer, device = load_models()
+
+def generate_response(image, question):
+    if image is None:
+        return "Vui lòng tải lên một hình ảnh."
+    if not question:
+        question = '<image>\nTrích xuất thông tin chính trong ảnh và trả về dạng markdown.'
+    
+    # Convert image to PIL if it's a file upload
+    if isinstance(image, str):
+        image = Image.open(image).convert('RGB')
+    else:
+        image = Image.fromarray(image).convert('RGB')
+    
+    # Process image
+    pixel_values = load_image(image, max_num=6).to(torch.bfloat16).to(device)
+    
+    # Generate response
+    generation_config = dict(max_new_tokens=1024, do_sample=False, num_beams=3, repetition_penalty=2.5)
+    
+    response, _ = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+    
+    return response
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Vintern-1B-v3.5 Demo")
+    gr.Markdown("Tải lên hình ảnh và đặt câu hỏi (hoặc để trống để trích xuất thông tin).")
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Hình ảnh")
+            question_input = gr.Textbox(
+                placeholder="<image>\nTrích xuất thông tin chính trong ảnh và trả về dạng markdown.", 
+                label="Câu hỏi (để trống để trích xuất thông tin)"
+            )
+            submit_btn = gr.Button("Gửi")
+        
+        with gr.Column():
+            output = gr.Markdown(label="Kết quả")
+    
+    submit_btn.click(
+        fn=generate_response,
+        inputs=[image_input, question_input],
+        outputs=output
+    )
+    
+    # gr.Examples(
+    #     [
+    #         ["example1.jpg", "<image>\nTrích xuất thông tin chính trong ảnh và trả về dạng markdown."],
+    #         ["example2.jpg", "<image>\nĐây là hình ảnh gì?"],
+    #     ],
+    #     inputs=[image_input, question_input],
+    # )
+
+demo.launch()