Update app.py

app.py

@@ -16,24 +16,24 @@ text_input = placeholder.text_area("Paste or write text", height=300)
 button = st.button("Extract Keywords")
 top_n = st.sidebar.slider("Select a number of keywords", 1, 10, 50,20)
 min_ngram = st.sidebar.number_input("Minimum number of words in each keyword", 1)
-max_ngram = st.sidebar.number_input("Maximum number of words in each keyword", 2,3,4, step=1)
-st.sidebar.code(f"ngram_range=({min_ngram}, {max_ngram})")
+max_ngram = st.sidebar.number_input("Maximum number of words in each keyword", 3)
+#st.sidebar.code(f"ngram_range=({min_ngram}, {max_ngram})")
 
 params = {"docs": text_input, "top_n": top_n, "keyphrase_ngram_range": (min_ngram, max_ngram), "stop_words": 'english',"vectorizer":KeyphraseCountVectorizer()}
 
-add_diversity = st.sidebar.checkbox("Adjust diversity of keywords")
+#add_diversity = st.sidebar.checkbox("Adjust diversity of keywords")
 
-if add_diversity:
-  method = st.sidebar.selectbox("Select a method", ("Max Sum Similarity", "Maximal Marginal Relevance"))
-  if method == "Max Sum Similarity":
-        nr_candidates = st.sidebar.slider("nr_candidates", 20, 50, 20, 2)
-        params["use_maxsum"] = True
-        params["nr_candidates"] = nr_candidates
+#if add_diversity:
+  #method = st.sidebar.selectbox("Select a method", ("Max Sum Similarity", "Maximal Marginal Relevance"))
+  #if method == "Max Sum Similarity":
+        #nr_candidates = st.sidebar.slider("nr_candidates", 20, 50, 20, 2)
+        #params["use_maxsum"] = True
+        #params["nr_candidates"] = nr_candidates
 
-  elif method == "Maximal Marginal Relevance":
-        diversity = st.sidebar.slider("diversity", 0.1, 1.0, 0.6, 0.01)
-        params["use_mmr"] = True
-        params["diversity"] = diversity
+  #elif method == "Maximal Marginal Relevance":
+        #diversity = st.sidebar.slider("diversity", 0.1, 1.0, 0.6, 0.01)
+        #params["use_mmr"] = True
+        #params["diversity"] = diversity
 
 keywords = model.extract_keywords(**params)