Update app.py

app.py

@@ -219,44 +219,44 @@ if file_text:
     with st.expander("📑 Context Table"):
         display_context_table(context_words)
 
-    with st.expander("📝 Sentence Clustering", expanded=True):
-        sentences = [line.strip() for line in file_text.split('\n') if len(line.strip()) > 10]
+    #with st.expander("📝 Sentence Clustering", expanded=True):
+    sentences = [line.strip() for line in file_text.split('\n') if len(line.strip()) > 10]
+
+    num_sentences = len(sentences)
+    st.write(f"Total Sentences: {num_sentences}")
+
+    num_clusters = st.slider("Number of Clusters", min_value=2, max_value=10, value=5)
+    clustered_sentences = cluster_sentences(sentences, num_clusters)
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.subheader("Original Text")
+        original_text = "\n".join(sentences)
+        st.text_area("Original Sentences", value=original_text, height=400)
+
+    with col2:
+        st.subheader("Clustered Text")
+        clusters = ""
+        clustered_text = ""
+        cluster_high_info_words = get_high_info_words_per_cluster(clustered_sentences)
+
+        for i, cluster in enumerate(clustered_sentences):
+            cluster_text = "\n".join(cluster)
+            high_info_words = ", ".join(cluster_high_info_words[i])
+            clusters += f"Cluster {i+1} (High Info Words: {high_info_words})\n"
+            clustered_text += f"Cluster {i+1} (High Info Words: {high_info_words}):\n{cluster_text}\n\n"
+
+        st.text_area("Clusters", value=clusters, height=200)
+        st.text_area("Clustered Sentences", value=clustered_text, height=200)
+
+        # Verify that all sentences are accounted for in the clustered output
+        clustered_sentences_flat = [sentence for cluster in clustered_sentences for sentence in cluster]
+        if set(sentences) == set(clustered_sentences_flat):
+            st.write("✅ All sentences are accounted for in the clustered output.")
+        else:
+            st.write("❌ Some sentences are missing in the clustered output.")
     
-        num_sentences = len(sentences)
-        st.write(f"Total Sentences: {num_sentences}")
-    
-        num_clusters = st.slider("Number of Clusters", min_value=2, max_value=10, value=5)
-        clustered_sentences = cluster_sentences(sentences, num_clusters)
-    
-        col1, col2 = st.columns(2)
-    
-        with col1:
-            st.subheader("Original Text")
-            original_text = "\n".join(sentences)
-            st.text_area("Original Sentences", value=original_text, height=400)
-    
-        with col2:
-            st.subheader("Clustered Text")
-            clusters = ""
-            clustered_text = ""
-            cluster_high_info_words = get_high_info_words_per_cluster(clustered_sentences)
-    
-            for i, cluster in enumerate(clustered_sentences):
-                cluster_text = "\n".join(cluster)
-                high_info_words = ", ".join(cluster_high_info_words[i])
-                clusters += f"Cluster {i+1} (High Info Words: {high_info_words})\n"
-                clustered_text += f"Cluster {i+1} (High Info Words: {high_info_words}):\n{cluster_text}\n\n"
-    
-            st.text_area("Clusters", value=clusters, height=200)
-            st.text_area("Clustered Sentences", value=clustered_text, height=200)
-    
-            # Verify that all sentences are accounted for in the clustered output
-            clustered_sentences_flat = [sentence for cluster in clustered_sentences for sentence in cluster]
-            if set(sentences) == set(clustered_sentences_flat):
-                st.write("✅ All sentences are accounted for in the clustered output.")
-            else:
-                st.write("❌ Some sentences are missing in the clustered output.")
-        
-        plot_cluster_words(clustered_sentences)
+    plot_cluster_words(clustered_sentences)
 
 st.markdown("For more information and updates, visit our [help page](https://huggingface.co/awacke1).")