Upload app.py

app.py

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+import gradio as gr
+import torch
+from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
+import pytesseract
+
+# Set the Tesseract executable path (for Windows users)
+import pytesseract
+pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
+
+
+# Load the model and processor
+processor = LayoutLMv3Processor.from_pretrained("quadranttechnologies/Table_OCR")
+model = LayoutLMv3ForTokenClassification.from_pretrained("quadranttechnologies/Table_OCR")
+model.eval()
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def process_image(image):
+    try:
+        # Preprocess the image using the processor
+        encoding = processor(image, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
+
+        # Move inputs to the same device as the model
+        encoding = {key: val.to(device) for key, val in encoding.items()}
+
+        # Perform inference
+        with torch.no_grad():
+            outputs = model(**encoding)
+            predictions = torch.argmax(outputs.logits, dim=-1)
+
+        # Extract input IDs, bounding boxes, and predicted labels
+        words = encoding["input_ids"]
+        bboxes = encoding["bbox"]
+        labels = predictions.squeeze().tolist()
+
+        # Format output as JSON
+        structured_output = []
+        for word_id, bbox, label in zip(words.squeeze().tolist(), bboxes.squeeze().tolist(), labels):
+            # Decode the word ID to text
+            word = processor.tokenizer.decode([word_id]).strip()
+            if word:  # Avoid adding empty words
+                structured_output.append({
+                    "word": word,
+                    "bounding_box": bbox,
+                    "label": model.config.id2label[label]  # Convert label ID to label name
+                })
+
+        return structured_output
+
+    except Exception as e:
+        return {"error": str(e)}  # Return error details if any issue occurs
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil"),  # Accepts image input
+    outputs="json",  # Outputs JSON structure
+    title="Table OCR",
+    description="Upload an image (e.g., receipt or document) to extract structured information in JSON format."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch(share=True)
+
+
+
+