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()