Update app.py

app.py

@@ -7,6 +7,7 @@ from PIL import Image
 from io import BytesIO
 import torch
 import clip
+from gradio_client import Client
 
 # Load the segmentation model
 sam_checkpoint = "sam_vit_h_4b8939.pth"
@@ -47,7 +48,6 @@ def find_similarity(base64_image, text_input):
     except Exception as e:
         return str(e)
 
-# Define a function for image segmentation
 def segment_image(input_image, text_input):
     image_bytes = base64.b64decode(input_image)
     image = Image.open(BytesIO(image_bytes))
@@ -92,12 +92,25 @@ def segment_image(input_image, text_input):
     # Limit the output to the top 6 key-value pairs
     segmented_regions = segmented_regions[:6]
 
-    # Print the top 6 results
+    # Run each segmented image through the inference API and store results
+    results_with_similarity = []
     for result in segmented_regions:
+        image_base64 = result["image"]
+        similarity = result["similarity"]
+        
+        # Make an API call with image_base64, get the API result
+        client = Client("https://ktllc-clip-model-inputbase64.hf.space/--replicas/dv889/")
+        api_result = client.predict(image_base64, text_input, api_name="/predict")
+
+        # Append the API result and similarity to the list
+        results_with_similarity.append({"api_result": api_result, "similarity": similarity})
+
+    # Print the top 6 results
+    for result in results_with_similarity:
         print(result)
     
     # Return the segmented images in descending order of similarity
-    return segmented_regions
+    return results_with_similarity
 
 # Create Gradio components
 input_image = gr.Textbox(label="Base64 Image", lines=8)