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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
sepia_interface = gr.Interface(sepia, gr.Image(), "image")
## 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}"
with gr.Blocks() as generated_output:
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input Image")
clear_button = gr.Button("Clear")
submit_button = gr.Button("Submit")
with gr.Column():
output_text = gr.Textbox(label="Output Text")
additional_text = gr.Textbox(label="Additional Input")
#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()
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