Separate model loading.

model_loading.py

@@ -0,0 +1,4 @@
+import tensorflow_hub as hub
+
+
+EMBEDDING_MODEL = hub.load("https://tfhub.dev/google/universal-sentence-encoder-large/5")

utils.py

@@ -7,10 +7,7 @@
 from typing import Dict, Any, List
 from sklearn.metrics.pairwise import cosine_similarity
 from sklearn.cluster import KMeans
-import tensorflow_hub as hub
-
-
-embedding_model = hub.load("https://tfhub.dev/google/universal-sentence-encoder-large/5")
+from model_loading import EMBEDDING_MODEL
 
 
 def open_file(filepath):
@@ -44,7 +41,7 @@ def save_message(root_folder, message: Dict[str, Any]):
 def search_tree(root_folder, query):
     # TODO add a "forks" parameter to allow for branching relevance
     # TODO add a "fuzziness" parameter that can generate a random vector to modify the search query
-    query_embedding = embedding_model([query]).numpy()
+    query_embedding = EMBEDDING_MODEL([query]).numpy()
     level = 6
     taxonomy = []
 
@@ -147,7 +144,7 @@ def process_missing_messages(root_folder: str):
         timestamp = file2_data['timestamp']
 
         combined_text = context + " --- " + response
-        embedding = embedding_model([combined_text]).numpy().tolist()
+        embedding = EMBEDDING_MODEL([combined_text]).numpy().tolist()
 
         message_pair_data = {
             'content': combined_text,
@@ -179,7 +176,7 @@ def create_summaries(root_folder: str, clusters: List[List[str]], target_folder:
         summary = quick_summarize(combined_content)
 
         # Create embedding for summary
-        summary_embedding = embedding_model([summary]).numpy().tolist()
+        summary_embedding = EMBEDDING_MODEL([summary]).numpy().tolist()
 
         # Save summary in target folder
         summary_data = {
@@ -275,7 +272,7 @@ def integrate_new_elements(root_folder: str, target_folder: str, new_elements: L
 
             combined_content = closest_file_data["content"] + " --- " + new_element_data["content"]
             updated_summary = quick_summarize(combined_content)
-            updated_summary_embedding = embedding_model([updated_summary]).numpy().tolist()
+            updated_summary_embedding = EMBEDDING_MODEL([updated_summary]).numpy().tolist()
 
             closest_file_data["content"] = updated_summary
             closest_file_data["vector"] = updated_summary_embedding
@@ -286,7 +283,7 @@ def integrate_new_elements(root_folder: str, target_folder: str, new_elements: L
             # Create a new summary for the new_element
             combined_content = new_element_data["content"]
             new_summary = quick_summarize(combined_content)
-            new_summary_embedding = embedding_model([new_summary]).numpy().tolist()
+            new_summary_embedding = EMBEDDING_MODEL([new_summary]).numpy().tolist()
 
             new_summary_data = {
                 "content": new_summary,