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import gradio as gr
# import cv2
# from PIL import Image
# from pillow_heif import register_heif_opener
# import numpy as np
# import imghdr
# def process_image(input_image, width = None, height = None):
# # Process the image here
# # For example, you can apply some image filters or transformations
# if input_image:
# # Convert the PIL image to OpenCV format
# image = Image.open(input_image)
# image = np.array(image)
# if width is None:
# width = image.shape[1]
# if height is None:
# height = image.shape[0]
# # Apply some image processing
# print(f"Width: {width}, Height: {height}")
# image = cv2.resize(image, (height, width))
# print(f"Image shape: {image.shape}")
# return image
# else:
# return None
# def display_image_info(input_image):
# if not input_image:
# return "No image uploaded"
# try:
# format = imghdr.what(input_image)
# return f"The format of the image is '{format}'"
# except:
# return f"The image format is unknown. However the file extension is '{input_image.split('.')[-1]}'"
# with gr.Blocks("Image-Processing") as demo:
# with gr.Row():
# with gr.Column():
# input_image = gr.Image("input_image", type="filepath")
# with gr.Column():
# output_image = gr.Image("output_image", type="filepath")
# # ========= For width and height ==========
# with gr.Row():
# width = gr.Slider(1, 1000, value=256, label="Width")
# height = gr.Slider(1, 1000, value=192, label="Height")
# with gr.Row():
# output_textbox = gr.Textbox("output_textbox", type="text", label="Output Textbox")
# input_image.change(process_image, [input_image, width, height], output_image)
# input_image.change(display_image_info, input_image, output_textbox)
# width.change(process_image, [input_image, width, height], output_image)
# height.change(process_image, [input_image, width, height], output_image)
# demo.launch()
import gradio as gr
def greet(name):
return "Hello " + name + "!!"
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()
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