final

app.py

@@ -8,8 +8,8 @@ feature_extractor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101
 dmodel = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101")
 
 i1 = gr.inputs.Image(type="pil", label="Input image")
-i2 = gr.inputs.Number(default=400, label="Custom Width (optional)")
-i3 = gr.inputs.Number(default=400, label="Custom Height (optional)")
+i2 = gr.inputs.Number(default=400, label="Custom Width")
+i3 = gr.inputs.Number(default=400, label="Custom Height")
 o1 = gr.outputs.Image(type="pil", label="Cropped part")
 
 def extract_image(image, custom_width, custom_height):
@@ -45,13 +45,10 @@ def extract_image(image, custom_width, custom_height):
     ymax = min(image.height, ymax)
 
     cropped_image = image.crop((xmin, ymin, xmax, ymax))
-
-    # Return the coordinates of the cropped area
-    coordinates = f"xmin: {xmin}, ymin: {ymin}, xmax: {xmax}, ymax: {ymax}"
     
     return cropped_image
 
-title = "Social Media Crop"
-description = "<p style='color:white'>Crop an image with the area containing the most detected objects while maintaining custom dimensions and adding a 10-pixel bleed. The area is centralized within the custom dimensions.</p>"
+title = "Auto Crop"
+description = "<p style='color:black'>Crop an image with the area containing the most detected objects. </p>"
 examples = [['ex3.jpg', 800, 400], ['cat.png', 400, 400]]
 gr.Interface(fn=extract_image, inputs=[i1, i2, i3], outputs=[o1], title=title, description=description, examples=examples, enable_queue=True).launch()