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| import gradio as gr | |
| import torch | |
| from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification | |
| import pytesseract | |
| import os | |
| # Explicitly set the Tesseract path for Hugging Face Spaces | |
| pytesseract.pytesseract.tesseract_cmd = "/usr/bin/tesseract" | |
| # Debugging: Print Tesseract version and PATH details | |
| try: | |
| tesseract_version = pytesseract.get_tesseract_version() | |
| print("Tesseract Version:", tesseract_version) | |
| print("Tesseract Path:", pytesseract.pytesseract.tesseract_cmd) | |
| print("Environment PATH:", os.environ["PATH"]) | |
| except Exception as e: | |
| print("Tesseract Debugging Error:", e) | |
| # For local development on Windows | |
| # Uncomment the line below if running locally on Windows | |
| # 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: | |
| # Debugging: Log any errors encountered during processing | |
| print("Error during processing:", str(e)) | |
| return {"error": str(e)} | |
| # 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__": | |
| # Debugging: Check if the app is starting correctly | |
| print("Starting Table OCR App...") | |
| interface.launch(share=True) | |