Upload app.py

app.py

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+import torch
+import gradio as gr
+from transformers import TrOCRProcessor, VisionEncoderDecoderModel
+from PIL import Image
+from torchvision import transforms
+from torchvision.transforms import InterpolationMode
+
+# Device configuration
+device = "cpu"
+
+# Load processor (for text tokenization/decoding only)
+processor = TrOCRProcessor.from_pretrained("microsoft/trocr-small-printed")
+
+# Load and prepare the quantized model
+model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-small-printed")
+model = torch.quantization.quantize_dynamic(
+    model, {torch.nn.Linear}, dtype=torch.qint8
+)
+model.load_state_dict(torch.load("best_model_int8.pt", map_location="cpu"), strict=False)
+model.to(device)
+model.eval()
+
+# Define the EXACT same preprocessing used during training (INFERENCE version)
+# Critical: Must match the training pipeline's resize method (LANCZOS interpolation)
+inference_transform = transforms.Compose([
+    # 1. Sharp resizing - same as training (LANCZOS preserves thin strokes)
+    transforms.Resize((384, 384), interpolation=InterpolationMode.LANCZOS),
+    # 2. Convert to tensor (range [0, 1])
+    transforms.ToTensor(),
+])
+
+def predict(img: Image.Image):
+    """
+    Process image with training-matched preprocessing and run OCR inference.
+    
+    Args:
+        img: PIL Image in RGB format
+        
+    Returns:
+        Recognized text string
+    """
+    # Step 1: Ensure image is in RGB mode (consistent with training)
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    
+    # Step 2: Apply the SAME transformation as in training
+    # This gives us a tensor in [C, H, W] format, range [0, 1]
+    pixel_values = inference_transform(img)
+    
+    # Step 3: Add batch dimension -> [1, C, H, W]
+    pixel_values = pixel_values.unsqueeze(0)
+    pixel_values = pixel_values.to(device)
+    
+    # Step 4: Run inference
+    with torch.no_grad():
+        generated_ids = model.generate(pixel_values)
+    
+    # Step 5: Decode the generated token IDs to text
+    text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
+    return text
+
+# Create Gradio interface
+gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload word image"),
+    outputs=gr.Textbox(label="Recognized Text"),
+    title="TrOCR OCR (CPU Optimized)",
+    description="Fine-tuned TrOCR on IIIT-5K  | CPU inference"
+).launch(share=True)