import gradio as gr

with gr.Blocks() as demo:
    image = gr.Image(type="filepath")
    dropdown = gr.Dropdown(choices=['flip_vertically', 'sepia_filter', 'black_and_white'])

    @image.upload(inputs=[image, dropdown], outputs=[image])
    def fn_1(image, dropdown):
        from PIL import Image, ImageEnhance
        import numpy as np
        import os
    
        def apply_sepia(im):
            # Convert image to numpy array
            im = np.array(im)
            # Apply sepia filter
            r = im[:, :, 0] * 0.393 + im[:, :, 1] * 0.769 + im[:, :, 2] * 0.189
            g = im[:, :, 0] * 0.349 + im[:, :, 1] * 0.686 + im[:, :, 2] * 0.168
            b = im[:, :, 0] * 0.272 + im[:, :, 1] * 0.534 + im[:, :, 2] * 0.131
            # Clamp the values to ensure they are in the 0-255 range
            r = np.clip(r, 0, 255).astype(np.uint8)
            g = np.clip(g, 0, 255).astype(np.uint8)
            b = np.clip(b, 0, 255).astype(np.uint8)
            # Merge channels back into an image
            sepia_im = np.dstack((r, g, b))
            return Image.fromarray(sepia_im)
    
        def convert_to_black_and_white(im):
            converter = ImageEnhance.Color(im)
            bw_im = converter.enhance(0.0)
            return bw_im
    
        # Open the image file
        im = Image.open(image)
    
        # Apply the selected operation
        if dropdown == 'flip_vertically':
            result_im = im.transpose(Image.FLIP_TOP_BOTTOM)
        elif dropdown == 'sepia_filter':
            result_im = apply_sepia(im)
        elif dropdown == 'black_and_white':
            result_im = convert_to_black_and_white(im)
        else:
            raise ValueError("Invalid dropdown option")
    
        # Save the result to a new file
        base, ext = os.path.splitext(image)
        output_path = f"{base}_modified{ext}"
        result_im.save(output_path)
    
        return output_path

demo.launch()