Update app.py

app.py

@@ -92,44 +92,10 @@ def segment_image(input_image, text_input):
     # Return the segmented images in descending order of similarity
     return segmented_regions
 
-
-# def segment_image(input_image, text_input):
-#     image_bytes = base64.b64decode(input_image)
-#     image = Image.open(BytesIO(image_bytes))
-    
-#     image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
-#     mask_generator = SamAutomaticMaskGenerator(sam)
-#     masks = mask_generator.generate(image)
-
-#     segmented_regions = []  # List to store segmented regions with similarity scores
-
-#     for i, mask_dict in enumerate(masks):
-#         mask_data = (mask_dict['segmentation'] * 255).astype(np.uint8)
-#         segmented_region = cv2.bitwise_and(image, image, mask=mask_data)
-
-#         x, y, w, h = map(int, mask_dict['bbox'])
-#         cropped_region = segmented_region[y:y+h, x:x+w]
-
-#         # Convert to base64 image
-#         _, buffer = cv2.imencode(".png", cv2.cvtColor(cropped_region, cv2.COLOR_BGR2RGB))
-#         segmented_image_base64 = base64.b64encode(buffer).decode()
-
-#         # Calculate similarity for the segmented image
-#         similarity = find_similarity(segmented_image_base64, text_input)
-
-#         # Append the segmented image and its similarity score
-#         segmented_regions.append({"image": segmented_image_base64, "similarity": similarity})
-
-#     # Sort the segmented images by similarity in descending order
-#     segmented_regions.sort(key=lambda x: x["similarity"], reverse=True)
-
-#     # Return the segmented images in descending order of similarity
-#     return segmented_regions
-
 # Create Gradio components
 input_image = gr.Textbox(label="Base64 Image", lines=8)
 text_input = gr.Textbox(label="Text Input")  # Use Textbox with a label
 output_images = gr.outputs.JSON()
 
 # Create a Gradio interface
-gr.Interface(fn=segment_image, inputs=[input_image, text_input], outputs=output_images).launch()
+gr.Interface(fn=segment_image, inputs=[input_image, text_input], outputs=output_images).launch(max_responses=6)