Update app.py

app.py

@@ -6,6 +6,7 @@ from PIL import Image
 from io import BytesIO
 import torch
 import clip
+import json
 
 # Load the segmentation model
 sam_checkpoint = "sam_vit_h_4b8939.pth"
@@ -17,7 +18,6 @@ model, preprocess = clip.load("ViT-B/32")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model.to(device).eval()
 
-
 def find_similarity(base64_image, text_input):
     try:
         # Decode the base64 image to bytes
@@ -44,9 +44,8 @@ def find_similarity(base64_image, text_input):
 
         return similarity
     except Exception as e:
-        return str(e)
+        return json.dumps({"error": str(e)})
 
-# Define a function for image segmentation
 def segment_image(input_image, text_input):
     try:
         image_bytes = base64.b64decode(input_image)
@@ -81,11 +80,11 @@ def segment_image(input_image, text_input):
         # 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
+        # Return the segmented images in a JSON format
+        return json.dumps(segmented_regions)
 
     except Exception as e:
-        return str(e)
+        return json.dumps({"error": str(e})
 
 # Create Gradio components
 input_image = gr.Textbox(label="Base64 Image", lines=8)