Update app.py

app.py

@@ -197,44 +197,45 @@ if fetch_data:
             incomplete_mask = metadata_df.map(is_incomplete).any(axis=1)
             incomplete_records = metadata_df[incomplete_mask]
 
-            st.subheader("Records with Incomplete Metadata")
-            if not incomplete_records.empty:
-                st.dataframe(incomplete_records.astype(str))
-            else:
-                st.success("All metadata fields are complete in this collection!")
-
-            st.subheader("Identifiers of Items Needing Metadata Updates")
-            if not incomplete_records.empty:
-                st.write(incomplete_records[['id', 'title']])
-            else:
-                st.success("All records are complete!")
+                st.subheader("✨ Suggested Metadata Enhancements")
 
-            st.subheader("Suggested Metadata Enhancements")
-            incomplete_with_desc = incomplete_records[incomplete_records['description'].notnull()]
-            reference_df = metadata_df[metadata_df['subject'].notnull() & metadata_df['description'].notnull()]
-            tfidf = TfidfVectorizer(stop_words='english')
-            try:
-                suggestions = []
-                tfidf_matrix = tfidf.fit_transform(reference_df['description'])
+                incomplete_with_desc = incomplete_records[incomplete_records['description'].notnull()]
+                reference_df = metadata_df[metadata_df['subject'].notnull() & metadata_df['description'].notnull()]
+                tfidf = TfidfVectorizer(stop_words='english')
+            
+                if len(incomplete_with_desc) > 1 and len(reference_df) > 1:
+                    try:
+                        suggestions = []
+                        tfidf_matrix = tfidf.fit_transform(reference_df['description'])
             
-                for idx, row in incomplete_with_desc.iterrows():
-                    if pd.isna(row['subject']) and pd.notna(row['description']):
-                        desc_vec = tfidf.transform([str(row['description'])])
-                        sims = cosine_similarity(desc_vec, tfidf_matrix).flatten()
-                        top_idx = sims.argmax()
-                        suggested_subject = metadata_df.iloc[top_idx]['subject']
-                        if pd.notna(suggested_subject) and suggested_subject:
-                            suggestions.append((row['title'], suggested_subject))
+                        for idx, row in incomplete_with_desc.iterrows():
+                            if pd.isna(row['subject']) and pd.notna(row['description']):
+                                desc_vec = tfidf.transform([str(row['description'])])
+                                sims = cosine_similarity(desc_vec, tfidf_matrix).flatten()
+                                top_idx = sims.argmax()
+                                suggested_subject = reference_df.iloc[top_idx]['subject']
+                                if pd.notna(suggested_subject) and suggested_subject:
+                                    suggestions.append((row['title'], suggested_subject))
             
-                if suggestions:
-                    suggestions_df = pd.DataFrame(suggestions, columns=["Title", "Suggested Subject"])
-                    st.table(suggestions_df)
-                else:
-                    st.markdown("""
-                    <div class='custom-table'>
-                    <b>ℹ️ No metadata enhancement suggestions available.</b>
-                    </div>
-                    """, unsafe_allow_html=True)
+                        if suggestions:
+                            suggestions_df = pd.DataFrame(suggestions, columns=["Title", "Suggested Subject"])
+                            st.markdown("<div class='custom-table'>" + suggestions_df.to_markdown(index=False) + "</div>", unsafe_allow_html=True)
+                        else:
+                            st.markdown("""
+                            <div class='custom-table'>
+                            <b>ℹ️ No metadata enhancement suggestions available.</b>
+                            </div>
+                            """, unsafe_allow_html=True)
+
+        except Exception as e:
+            st.error(f"Error generating metadata suggestions: {e}")
+    else:
+        st.markdown("""
+        <div class='custom-table'>
+        <b>ℹ️ Not enough descriptive data to generate metadata suggestions.</b>
+        </div>
+        """, unsafe_allow_html=True)
+
 
             except Exception as e:
                 st.error(f"Error generating metadata suggestions: {e}")