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()
    return sepia_img


## 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.Row():
    #    sepia_interface
    #with gr.Column():
    #    gr.Texbox()
    with gr.Row():
        input_img = gr.Image(label="Input Image")
        clear_button = gr.Button("Clear")
        submit_button = gr.Button("Submit")
        output_img = gr.Image(label="Output Image")
        sepia_vales_text = gr.Textbox(label="Sepia Values")
    submit_button.click(sepia, inputs=input_img, outputs=[sepia_img, sepia_vales_text])

#with gr.Blocks() as generated_output:
#    inp = gr.Interface(sepia, gr.Image(), "image")
#    out = gr.Textbox()


demoApp = gr.TabbedInterface([sepia_interface, generated_output], ["RGB Sepia Filter", "Handwritten to Text"])

if __name__ == "__main__":
    demoApp.launch()