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| import gradio as gr | |
| import numpy as np | |
| from transformers import TrOCRProcessor, VisionEncoderDecoderModel | |
| #from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering | |
| from PIL import Image | |
| #client = InferenceClient("models/microsoft/trocr-base-handwritten") | |
| #processor = AutoProcessor.from_pretrained("microsoft/trocr-base-handwritten") | |
| processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten') | |
| #model = AutoModelForDocumentQuestionAnswering.from_pretrained("microsoft/trocr-base-handwritten") | |
| model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten') | |
| def sepia(input_img): | |
| sepia_filter = np.array([ | |
| [0.393, 0.769, 0.189], | |
| [0.349, 0.686, 0.168], | |
| [0.272, 0.534, 0.131] | |
| ]) | |
| sepia_img = input_img.dot(sepia_filter.T) | |
| sepia_img /= sepia_img.max() | |
| sepia_values = repr(sepia_img) | |
| return sepia_img, sepia_values | |
| ## https://www.gradio.app/docs/gradio/blocks | |
| ## required positional arguments: 'inputs' and 'outputs' | |
| def process_image(image): | |
| try: | |
| pixel_values = processor(images=image, return_tensors="pt").pixel_values | |
| generated_ids = model.generate(pixel_values) | |
| generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return generated_text | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def additional_input(text): | |
| return f"Additional input received: {text}" | |
| sepia_interface = gr.Interface(sepia, gr.Image(), "image") | |
| with gr.Blocks() as generated_output: | |
| with gr.Column(): | |
| sepia_values_text=gr.Textbox(label="Sepia Values") | |
| output_img = gr.Image(label="Output Image") | |
| gr.Interface(fn=sepia, | |
| inputs=gr.Image( | |
| #this makes the camera stream live | |
| sources=["webcam"], | |
| streaming=True | |
| ), | |
| outputs=[output_img, sepia_values_text], | |
| live=True, | |
| show_progress="full") | |
| with gr.Row(): | |
| output_img.change( | |
| fn=process_image, | |
| inputs=output_img, | |
| outputs=gr.Textbox(label="Recognized Text"), | |
| show_progress="full") | |
| #with gr.Blocks() as generated_output: | |
| # inp = gr.Interface(sepia, gr.Image(), "image") | |
| # out = gr.Textbox() | |
| #demo = gr.TabbedInterface([sepia_interface, generated_output], ["RGB Sepia Filter", "Handwritten to Text"]) | |
| #with gr.Blocks() as demo: | |
| # with gr.Row(): | |
| # input_img = gr.Image(label="Input Image") | |
| # submit_button = gr.Button("Submit") | |
| # output_img = gr.Image(label="Output Image") | |
| # sepia_values_text = gr.Textbox(label="Sepia Values") | |
| # submit_button.click(sepia, inputs=input_img, outputs=[output_img, sepia_values_text]) | |
| if __name__ == "__main__": | |
| generated_output.launch() |