Update app.py

app.py

@@ -33,7 +33,7 @@ def fit_transform(model, docs):
     topics, probs = model.fit_transform(docs)
     return topics, probs
 
-#topics, probs = fit_transform(topic_model, tiktok)
+topics, probs = fit_transform(topic_model, tiktok)
 
 #topics_over_times = topic_model.topics_over_time(tiktok, topics, timestamps, nr_bins=20)
 #topic_model.visualize_topics_over_time(topics_over_times, top_n_topics=30)
@@ -46,35 +46,35 @@ form = st.sidebar.form("Main Settings")
 
 form.header("Main Settings")
 
-ebay_topic= form.selectbox("eBay Products Topic Selection", ["Motor", "Bicycle", "Beauty", "Basketball", "Fitness"])
-top_n = form.number_input("What's the max length of the text?", value = 10)
+ebay_topic = form.selectbox("eBay Products Topic Selection", ["Motor", "Bicycle", "Beauty", "Basketball", "Fitness"])
+num = form.number_input("What's the max length of the text?", value = 10)
 
 form.form_submit_button("Run")
 
 if ebay_topic == "Motor":
-    topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
-    topics, probs = fit_transform(topic_model, tiktok)
-    similar_topics, similarity = topic_model.find_topics("Motor", top_n=top_n)
+    #topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
+    #topics, probs = fit_transform(topic_model, tiktok)
+    similar_topics, similarity = topic_model.find_topics("Motor", top_n=num)
 elif ebay_topic == "Bicycle":
-    topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
-    topics, probs = fit_transform(topic_model, tiktok)
-    similar_topics, similarity = topic_model.find_topics("Bicycle", top_n=top_n)
+    #topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
+    #topics, probs = fit_transform(topic_model, tiktok)
+    similar_topics, similarity = topic_model.find_topics("Bicycle", top_n=num)
 elif ebay_topic == "Beauty":
-    topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
-    topics, probs = fit_transform(topic_model, tiktok)
-    similar_topics, similarity = topic_model.find_topics("Beauty", top_n=top_n)
+    #topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
+    #topics, probs = fit_transform(topic_model, tiktok)
+    similar_topics, similarity = topic_model.find_topics("Beauty", top_n=num)
 elif ebay_topic == "Basketball":
-    topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
-    topics, probs = fit_transform(topic_model, tiktok)
-    similar_topics, similarity = topic_model.find_topics("Basketball", top_n=top_n)
+    #topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
+    #topics, probs = fit_transform(topic_model, tiktok)
+    similar_topics, similarity = topic_model.find_topics("Basketball", top_n=num)
 elif ebay_topic == "Fitness":
-    topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
-    topics, probs = fit_transform(topic_model, tiktok)
-    similar_topics, similarity = topic_model.find_topics("Fitness", top_n=top_n)
+    #topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
+    #topics, probs = fit_transform(topic_model, tiktok)
+    similar_topics, similarity = topic_model.find_topics("Fitness", top_n=num)
 else:
-    topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
-    topics, probs = fit_transform(topic_model, tiktok)
-    similar_topics, similarity = topic_model.find_topics("Motor", top_n=top_n)
+    #topic_model = BERTopic(verbose=True,vectorizer_model=vectorizer_model)
+    #topics, probs = fit_transform(topic_model, tiktok)
+    similar_topics, similarity = topic_model.find_topics("Motor", top_n=num)
 
 if similar_topics != []:
     most_similar = similar_topics[0]