load model

app.py

@@ -13,12 +13,6 @@ from transformers import pipeline
 
 from sentence_transformers import SentenceTransformer, util
 
-classifier_model = pipeline(
-    "zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1"
-)
-
-embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
-
 # get db info from env vars
 db_host = os.environ.get("DB_HOST")
 db_user = os.environ.get("DB_USER")
@@ -77,6 +71,9 @@ potential_labels = get_potential_labels()
 # Function to handle the classification
 def classify_email_and_generate_response(representative_email, constituent_email):
     potential_labels = get_potential_labels()
+    classifier_model = pipeline(
+        "zero-shot-classification", model="MoritzLaurer/deberta-v3-large-zeroshot-v1"
+    )
     print("classifying email")
     model_out = classifier_model(constituent_email, potential_labels, multi_label=True)
     print("classification complete")
@@ -148,6 +145,7 @@ def get_similar_messages(constituent_email):
     )
 
     messages_for_category = db_cursor.fetchall()
+    embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
 
     all_message_chains = []