Update app.py

app.py

@@ -12,16 +12,19 @@ def main():
     # Get user input
     doc = st.text_area("Document")
 
-    # Get user choice for stopwords removal
-    remove_stopwords = st.checkbox("Remove Stopwords")
+    # Get user choice for stopwords removal (default checkbox)
+    remove_stopwords = st.checkbox("Remove Stopwords", value=True)
+
+    # Get user choice for MMR (default checkbox)
+    apply_mmr = st.checkbox("Apply Maximal Marginal Relevance (MMR)", value=True)
+
+    # Get user choice for number of results (slider)
+    num_results = st.slider("Number of Results", min_value=1, max_value=30, value=5, step=1)
 
     # Extract keywords
     if st.button("Extract Keywords"):
         keywords = kw_model.extract_keywords(doc, stop_words=None if remove_stopwords else "english")
 
-        # Get user choice for MMR
-        apply_mmr = st.checkbox("Apply Maximal Marginal Relevance (MMR)")
-
         if apply_mmr:
             # Apply Maximal Marginal Relevance (MMR)
             selected_keywords = []
@@ -29,9 +32,8 @@ def main():
 
             # Set the MMR hyperparameters
             lambda_param = 0.7  # Weight for the trade-off between relevance and diversity
-            num_keywords = 5  # Number of keywords to select
 
-            for i in range(1, num_keywords):
+            for i in range(1, num_results):
                 selected_keywords_scores = [kw[1] for kw in selected_keywords]
                 remaining_keywords = [kw for kw in keywords if kw[0] not in [kw[0] for kw in selected_keywords]]
                 mmr_scores = kw_model.maximal_marginal_relevance(doc, remaining_keywords, selected_keywords_scores, lambda_param)
@@ -40,10 +42,10 @@ def main():
 
             keywords = selected_keywords  # Update keywords with MMR-selected keywords
 
-        st.write("Keywords:")
+        st.write(f"Top {num_results} Keywords:")
         for keyword, score in keywords:
             st.write(f"- {keyword} (Score: {score})")
 
 # Run the app
 if __name__ == "__main__":
-    main()
+    main()