Create app.py

app.py

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+import gradio as gr
+from transformers import VisionEncoderDecoderModel, TrOCRProcessor,AutoTokenizer,ViTFeatureExtractor
+from PIL import Image
+import torch
+
+def preprocess_image(image):
+    # Resize while maintaining aspect ratio
+    target_size = (224, 224)
+    original_size = image.size
+
+    # Calculate the new size while maintaining aspect ratio
+    aspect_ratio = original_size[0] / original_size[1]
+    if aspect_ratio > 1:  # Width is greater than height
+        new_width = target_size[0]
+        new_height = int(target_size[0] / aspect_ratio)
+    else:  # Height is greater than width
+        new_height = target_size[1]
+        new_width = int(target_size[1] * aspect_ratio)
+
+    # Resize the image
+    resized_img = image.resize((new_width, new_height))
+
+    # Calculate padding values
+    padding_width = target_size[0] - new_width
+    padding_height = target_size[1] - new_height
+
+    # Apply padding to center the resized image
+    pad_left = padding_width // 2
+    pad_top = padding_height // 2
+    pad_image = Image.new('RGB', target_size, (255, 255, 255))  # White background
+    pad_image.paste(resized_img, (pad_left, pad_top))
+    return pad_image
+
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModel,ViTFeatureExtractor,TrOCRProcessor,VisionEncoderDecoderModel
+
+tokenizer = AutoTokenizer.from_pretrained("syubraj/TrOCR_Nepali")
+model1 = VisionEncoderDecoderModel.from_pretrained("syubraj/TrOCR_Nepali")
+feature_extractor1 = ViTFeatureExtractor.from_pretrained("syubraj/TrOCR_Nepali")
+
+processor1 = TrOCRProcessor(feature_extractor=feature_extractor1, tokenizer=tokenizer)
+
+
+# tokenizer = AutoTokenizer.from_pretrained("paudelanil/trocr-devanagari")
+# model = VisionEncoderDecoderModel.from_pretrained("paudelanil/trocr-devanagari")
+# feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+model1.to(device)
+def predict(image):
+    # Preprocess the image
+    image = Image.open(image).convert("RGB")
+    image = preprocess_image(image)
+    pixel_values = processor1(image, return_tensors="pt").pixel_values.to(device)
+    
+    # Generate text from the image
+    generated_ids = model1.generate(pixel_values)
+    generated_text = processor1.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
+    return generated_text
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="filepath"),
+    outputs="text",
+    title="Devanagari OCR with TrOCR",
+    description="Upload an image with Devanagari script and get the text prediction using a pre-trained Vision-Text model."
+)
+
+# Launch the interface
+interface.launch(share=True)