Update app.py

app.py

@@ -25,7 +25,7 @@ def find_similarity(base64_image, text_input):
         image_bytes = base64.b64decode(base64_image)
 
         # Convert the bytes to a PIL image
-        image = Image.open(BytesIO(image_bytes))
+        image = Image.open(BytesIO(image_bytes)
 
         # Preprocess the image
         image = preprocess(image).unsqueeze(0).to(device)
@@ -52,10 +52,10 @@ 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)
+    image = np.array(image)  # Remove the color mode conversion
     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):
@@ -66,7 +66,7 @@ def segment_image(input_image, text_input):
         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))
+        _, buffer = cv2.imencode(".png", cropped_region)
         segmented_image_base64 = base64.b64encode(buffer).decode()
 
         # Calculate similarity for the segmented image
@@ -88,4 +88,3 @@ output_images = gr.outputs.JSON()
 
 # Create a Gradio interface
 gr.Interface(fn=segment_image, inputs=[input_image, text_input], outputs=output_images).launch()
-