Update app.py

app.py

@@ -73,6 +73,13 @@ form.caption("A complete list of all available metrics supported by UMAP can be
 form.form_submit_button("Submit")
 
 #BerTopic_model = BERTopic.load("my_topics_model")
+hdbscan_model = HDBSCAN(min_cluster_size=hdbscan_min_cluster_size, min_samples = hdbscan_min_samples, metric=hdbscan_metric, prediction_data=True)
+if use_random_seed:
+    umap_model = UMAP(n_neighbors=umap_n_neighbors, n_components=umap_n_components, min_dist=umap_min_dist, metric=umap_metric, random_state = 42)
+else:
+    umap_model = UMAP(n_neighbors=umap_n_neighbors, n_components=umap_n_components, min_dist=umap_min_dist, metric=umap_metric)
+vectorizer_model = CountVectorizer(lowercase = cv_lowercase, ngram_range=(cv_ngram_min, cv_ngram_max), analyzer=cv_analyzer, max_df=cv_max_df, min_df=cv_min_df, stop_words="english")
+
 @st.cache(allow_output_mutation=True)
 def load_model(model_name, hdbscan_model=hdbscan_model, umap_model=umap_model, vectorizer_model=vectorizer_model, use_topic_reduction = use_topic_reduction, number_of_topics = number_of_topics):
     sentence_model = SentenceTransformer(model_name)
@@ -84,12 +91,7 @@ def load_model(model_name, hdbscan_model=hdbscan_model, umap_model=umap_model, v
         kw_model = BERTopic(embedding_model=sentence_model, umap_model = umap_model, hdbscan_model = hdbscan_model, vectorizer_model = vectorizer_model, calculate_probabilities = True)
     return kw_model
    
-hdbscan_model = HDBSCAN(min_cluster_size=hdbscan_min_cluster_size, min_samples = hdbscan_min_samples, metric=hdbscan_metric, prediction_data=True)
-if use_random_seed:
-    umap_model = UMAP(n_neighbors=umap_n_neighbors, n_components=umap_n_components, min_dist=umap_min_dist, metric=umap_metric, random_state = 42)
-else:
-    umap_model = UMAP(n_neighbors=umap_n_neighbors, n_components=umap_n_components, min_dist=umap_min_dist, metric=umap_metric)
-vectorizer_model = CountVectorizer(lowercase = cv_lowercase, ngram_range=(cv_ngram_min, cv_ngram_max), analyzer=cv_analyzer, max_df=cv_max_df, min_df=cv_min_df, stop_words="english")
+
 
 
 BerTopic_model = load_model(model_name=model_name)