Create app.py

app.py

@@ -0,0 +1,43 @@
+import pandas as pd
+import streamlit as st
+from keybert import KeyBERT
+from samples import texts
+
+@st.cache(allow_output_mutation=True, suppress_st_warning=True, show_spinner=True)
+def load_model():
+  model = KeyBERT("sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1")
+  return model
+  
+model = load_model()
+
+placeholder = st.empty()
+text_input = placeholder.text_area("Paste or write text", height=300)
+
+top_n = st.sidebar.slider("Select a number of keywords", 1, 10, 5, 1)
+min_ngram = st.sidebar.slider.number_input("Minimum number of words in each keyword", 1, 5, 1, 1)
+max_ngram = st.sidebar.slider.number_input("Maximum number of words in each keyword", min_ngram, 5, 3, step=1)
+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'}
+
+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
+
+    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)
+
+if keywords != []:
+    st.info("Extracted keywords")
+    keywords = pd.DataFrame(keywords, columns=["keyword", "relevance"])
+    st.table(keywords)
+