Create app.py

app.py

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+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
+import gradio as gr
+
+# Dùng CPU thay vì GPU
+device = torch.device("cpu")
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    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=IMAGENET_MEAN, std=IMAGENET_STD)
+    ])
+    return transform
+
+def load_image(image, input_size=448):
+    transform = build_transform(input_size=input_size)
+    pixel_values = transform(image).unsqueeze(0)  # Thêm batch dimension
+    return pixel_values
+
+# Load model trên CPU
+model = AutoModel.from_pretrained(
+    "5CD-AI/Vintern-1B-v3_5",
+    torch_dtype=torch.float32,  # Dùng float32 cho CPU
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+).eval().to(device)
+
+tokenizer = AutoTokenizer.from_pretrained("5CD-AI/Vintern-1B-v3_5", trust_remote_code=True, use_fast=False)
+
+def process_image(image):
+    pixel_values = load_image(image).to(device)
+    generation_config = dict(max_new_tokens=1024, do_sample=False, num_beams=3, repetition_penalty=2.5)
+
+    question = "<image>\nTrích xuất toàn bộ thông tin trong ảnh và trả về dạng text."
+    response, _ = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+
+    return response
+
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Vietnamese Hand Writing ORC",
+    description="Extract all the information from the image and return it in text form."
+)
+
+if __name__ == "__main__":
+    iface.launch()