Update app.py

app.py

@@ -1,41 +1,9 @@
-import pandas as pd
-import streamlit as st
+import gradio as gr
 from keybert import KeyBERT
 
-@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()
+def keywords(text):
+    model = KeyBERT("sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1")
+    keywords = model.extract_keywords("The quick brown fox jumps over the lazy dog.")
+    return keywords
 
-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.number_input("Minimum number of words in each keyword", 1, 5, 1, 1)
-max_ngram = st.sidebar.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)
+gr.Interface(keywords, "text", "text",title="Keyword Extractor").launch()