Update main.py

main.py

@@ -13,7 +13,55 @@ def read_root():
     return {"Hello": "World!"}
 
 @app.put("/")
-def update_item(item: Item):
-    return item
+def predict(Item):
+    # Load the OpenAI CLIP Model
+    print('Loading CLIP Model...')
+    model = SentenceTransformer('clip-ViT-B-32')
+    
+    # Next we compute the embeddings
+    # To encode an image, you can use the following code:
+    # from PIL import Image
+    # encoded_image = model.encode(Image.open(filepath))
+    #image_names = list(glob.glob('./*.jpg'))
+    #print("Images:", len(image_names))
+    encoded_image = model.encode(imageArray, batch_size=128, convert_to_tensor=True, show_progress_bar=True)
+    
+    # Now we run the clustering algorithm. This function compares images aganist 
+    # all other images and returns a list with the pairs that have the highest 
+    # cosine similarity score
+    processed_images = util.paraphrase_mining_embeddings(encoded_image)
+    NUM_SIMILAR_IMAGES = 10 
+    
+    # =================
+    # DUPLICATES
+    # =================
+    print('Finding duplicate images...')
+    # Filter list for duplicates. Results are triplets (score, image_id1, image_id2) and is scorted in decreasing order
+    # A duplicate image will have a score of 1.00
+    # It may be 0.9999 due to lossy image compression (.jpg)
+    duplicates = [image for image in processed_images if image[0] >= 0.999]
+    
+    # Output the top X duplicate images
+    for score, image_id1, image_id2 in duplicates[0:NUM_SIMILAR_IMAGES]:
+        print("\nScore: {:.3f}%".format(score * 100))
+    
+    # =================
+    # NEAR DUPLICATES
+    # =================
+    print('Finding near duplicate images...')
+    # Use a threshold parameter to identify two images as similar. By setting the threshold lower, 
+    # you will get larger clusters which have less similar images in it. Threshold 0 - 1.00
+    # A threshold of 1.00 means the two images are exactly the same. Since we are finding near 
+    # duplicate images, we can set it at 0.99 or any number 0 < X < 1.00.
+    threshold = 0.99
+    near_duplicates = [image for image in processed_images if image[0] < threshold]
+    
+    for score, image_id1, image_id2 in near_duplicates[0:NUM_SIMILAR_IMAGES]:
+        print("\nScore: {:.3f}%".format(score * 100))
+
+    return "Score: {:.3f}%".format(score * 100)
+
+#def update_item(item: Item):
+#   return item
 #def put_root():
 #   return {"Hello": "Put!"}