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| import gradio as gr | |
| from multiocr import OcrEngine | |
| from multiocr.utils import draw_bounding_boxes | |
| # Define a function that takes an input image and processes it | |
| def process_image(input_image, ocr_type): | |
| # Perform y`our internal processing here | |
| # Example: Convert the image to grayscale | |
| ocr = OcrEngine(ocr_type) | |
| data = ocr.text_extraction(input_image) | |
| json_response = ocr.text_extraction_to_df(data) | |
| output_image = draw_bounding_boxes(input_image, data) | |
| return output_image, json_response | |
| # Define the Gradio interface | |
| def app(): | |
| ocr_type = gr.inputs.Dropdown(choices=["tesseract", "paddle_ocr", "easy_ocr", "aws_textract", "doctr_ocr"], label="selct one ocr") | |
| # Create an input component for uploading the image | |
| image_input = gr.inputs.Image(label="Upload Image", type="filepath") | |
| # Create an output component for displaying the processed image | |
| image_output = gr.outputs.Image(label="Output Image", type="pil") | |
| # Create an output component for displaying the JSON response | |
| df = gr.outputs.Dataframe(label="DataFrame", type="pandas") | |
| # Create a function that will be called when the app is run | |
| def process_and_display_image(input_image, ocr_type): | |
| processed_image, json_response = process_image(input_image, ocr_type) | |
| return processed_image, json_response | |
| # Create the Gradio interface | |
| examples = [["./data/simple.jpg"], ["./data/invoice.jpg"], ["./data/publay_sample.jpeg"], ["./data/complicated.jpg"]] | |
| gr.Interface(fn=process_and_display_image, inputs=[image_input, ocr_type], outputs=[image_output, df], examples=examples).launch(server_port=7860,server_name="0.0.0.0") | |
| if __name__ == "__main__": | |
| # Run the app | |
| app() | |