social

app.py

@@ -8,17 +8,13 @@ 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.Textbox(label="Input text")
-i3 = gr.inputs.Number(default=0.96, label="Threshold percentage score")
-i4 = gr.inputs.Number(default=400, label="Custom Width (optional)")
-i5 = gr.inputs.Number(default=400, label="Custom Height (optional)")
+i2 = gr.inputs.Number(default=400, label="Custom Width (optional)")
+i3 = gr.inputs.Number(default=400, label="Custom Height (optional)")
 o1 = gr.outputs.Image(type="pil", label="Cropped part")
-o2 = gr.outputs.Textbox(label="Similarity score")
 
-def extract_image(image, text, prob, custom_width, custom_height):
+def extract_image(image, custom_width, custom_height):
     inputs = feature_extractor(images=image, return_tensors="pt")
     outputs = dmodel(**inputs)
-    pout = ""
 
     target_sizes = torch.tensor([image.size[::-1]])
     results = feature_extractor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
@@ -53,9 +49,8 @@ def extract_image(image, text, prob, custom_width, custom_height):
     # Return the coordinates of the cropped area
     coordinates = f"xmin: {xmin}, ymin: {ymin}, xmax: {xmax}, ymax: {ymax}"
     
-    return cropped_image, coordinates
+    return cropped_image
 
-title = "ClipnCrop"
 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>"
 examples = [['ex3.jpg', 'people', 0.96, 800, 400], ['ex2.jpg', 'smiling face', 0.85, 300, 400]]
-gr.Interface(fn=extract_image, inputs=[i1, i2, i3, i4, i5], outputs=[o1, o2], title=title, description=description, examples=examples, enable_queue=True).launch()
+gr.Interface(fn=extract_image, inputs=[i1, i2, i3], outputs=[o1], title=title, description=description, examples=examples, enable_queue=True).launch()