Refactor generate_embedding function to remove unneeded variables

app.py

@@ -43,7 +43,7 @@ def generate_embedding(text_data, image_data):
 
     # Embed text data
     text_embeddings = []
-    empty_text_indices = []
+    empty_data_indices = []
     if text_data:
         # If text_data is a string, convert to list of strings
         if isinstance(text_data, str):
@@ -54,7 +54,7 @@ def generate_embedding(text_data, image_data):
             text_data = list(text_data)
 
         # Keep track of indices of empty text strings
-        empty_text_indices = [i for i, text in enumerate(text_data) if text == ""]
+        empty_data_indices = [i for i, text in enumerate(text_data) if text == ""]
 
         # Remove empty text strings
         text_data = [text for text in text_data if text != ""]
@@ -71,12 +71,12 @@ def generate_embedding(text_data, image_data):
             text_embeddings = [embedding.detach().cpu().numpy().tolist() for embedding in text_embeddings]
 
         # Insert empty strings at indices of empty text strings
-        for i in empty_text_indices:
+        for i in empty_data_indices:
             text_embeddings.insert(i, "")
 
     # Embed image data
     image_embeddings = []
-    empty_image_indices = []
+    empty_data_indices = []
     if image_data:
         # If image_data is a single PIL image, convert to list of PIL images
         if isinstance(image_data, PIL.Image.Image):
@@ -87,7 +87,7 @@ def generate_embedding(text_data, image_data):
             image_data = list(image_data)
 
         # Keep track of indices of None images
-        empty_image_indices = [i for i, img in enumerate(image_data) if img is None]
+        empty_data_indices = [i for i, img in enumerate(image_data) if img is None]
 
         # Remove None images
         image_data = [img for img in image_data if img is not None]
@@ -105,12 +105,12 @@ def generate_embedding(text_data, image_data):
             image_embeddings = [embedding.detach().cpu().numpy().tolist() for embedding in image_embeddings]
 
         # Insert empty strings at indices of empty images
-        for i in empty_image_indices:
+        for i in empty_data_indices:
             image_embeddings.insert(i, "")
 
     # Calculate cosine similarity between text and image embeddings
     similarity = []
-    empty_similarity_indices = []
+    empty_data_indices = []
     if text_embeddings and image_embeddings:
         # Filter out embedding pairs with either empty text or image embeddings, tracking indices of empty embeddings
         text_embeddings_filtered = []
@@ -120,7 +120,7 @@ def generate_embedding(text_data, image_data):
                 text_embeddings_filtered.append(text_embedding)
                 image_embeddings_filtered.append(image_embedding)
             else:
-                empty_similarity_indices.append(i)
+                empty_data_indices.append(i)
 
         # Calculate cosine similarity if there are any non-empty embedding pairs
         if image_embeddings_filtered and text_embeddings_filtered:
@@ -138,7 +138,7 @@ def generate_embedding(text_data, image_data):
             similarity = [f"{sim.item() * 100:.2f}%" for sim in similarity]
 
         # Insert empty text strings in similarity
-        for i in empty_similarity_indices:
+        for i in empty_data_indices:
             similarity.insert(i, "")
 
     return (text_embeddings, image_embeddings, similarity)