Delete app.py

app.py

@@ -1,66 +0,0 @@
-import gradio as gr
-import cv2
-import numpy as np
-
-def edge_detection(image, threshold1, threshold2):
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    edges = cv2.Canny(gray, threshold1, threshold2)
-    return edges
-
-def image_segmentation(image):
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    _, segmented = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-    return segmented
-
-def image_inpainting(image, mask):  
-    if mask.shape[:2] != image.shape[:2]:
-        mask = cv2.resize(mask, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)
-    
-    if len(mask.shape) == 3:
-        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
-    mask = (mask > 0).astype(np.uint8) * 255
-    inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)
-    return inpainted
-
-with gr.Blocks() as demo:
-    gr.Markdown("Computer Vision")
-    with gr.Tab("Edge Detection"):
-        inputs = [
-                gr.Image(type="numpy", label="Upload Image"),
-                gr.Slider(0, 255, value=50, step=1, label="Threshold 1"),
-                gr.Slider(0, 255, value=100, step=1, label="Threshold 2"),
-            ]
-        output = gr.Image(type="numpy", label="Edge Image") 
-        gr.Interface(
-            fn=edge_detection,
-            inputs=inputs,
-            outputs=output,
-            description="Upload an image and adjust the thresholds to perform edge detection"
-        )
-    with gr.Tab("Image Segmentation"):
-        inputs = [
-                gr.Image(type="numpy", label="Upload Image")
-            ]
-        output = gr.Image(type="numpy", label="Segmented Image") 
-        gr.Interface(
-            fn=image_segmentation,
-            inputs=inputs,
-            outputs=output,
-            description="Upload an image to perform image segmentation"
-        )
-    with gr.Tab("Image Inpainting"):
-        inputs = [
-                gr.Image(type="numpy", label="Upload Image"),
-                gr.Image(type="numpy", label="Upload Mask")
-            ]
-        output = gr.Image(type="numpy", label="Inpainted Image") 
-        gr.Interface(
-            fn=image_inpainting,
-            inputs=inputs,
-            outputs=output,
-            description="Upload an image and adjust the thresholds to perform image inpainting",
-            allow_flagging='never'
-        )
-
-if __name__ == "__main__":
-    demo.launch(share=True)