app.py

import gradio as gr
import numpy as np
from huggingface_hub import InferenceClient

client = InferenceClient("models/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):
    # Your image processing logic here
    return "Processed Text"

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():
        gr.Interface(fn=sepia,
                     inputs=gr.Image(), 
                     outputs="image",
                     show_progress="minimal")
        with gr.Row():
            gr.Textbox(label="text")
            #gr.Interface(sepia, 
            #            inputs = gr.Image(label="image"),
            #            outputs = gr.Textbox())

#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__":
    demo.launch()