Update app.py

app.py

@@ -412,54 +412,56 @@ that can be as accurate as many blackbox models. The MIT licensed source code
 can be downloaded from github.com/microsoft/interpret.
 
 ''', height=200)
+
+
 #Get user input for the number of dictionary components
 n_components = st.slider('Number of dictionary components', 1, 20, 10)
 if st.button('Analyze'):
-# Perform text preprocessing
-vectorizer = CountVectorizer(stop_words='english')
-X = vectorizer.fit_transform([text_input])
-
-# Perform dictionary learning
-dl = DictionaryLearning(n_components=n_components, transform_algorithm='lasso_lars', random_state=0)
-X_transformed = dl.fit_transform(X)
-dictionary = dl.components_
-
-# Get the feature names (terms)
-feature_names = vectorizer.get_feature_names_out()
-
-# Create a DataFrame with dictionary components and their corresponding terms
-df_components = pd.DataFrame(dictionary, columns=feature_names)
-df_components['Component'] = ['Component ' + str(i+1) for i in range(n_components)]
-df_components = df_components.set_index('Component')
-
-# Display the DataFrame
-st.markdown("### Dictionary Components")
-st.dataframe(df_components)
-
-# Create a graph of terms and their connections
-G = nx.Graph()
-
-# Add nodes to the graph
-for term in feature_names:
-    G.add_node(term)
-
-# Add edges to the graph based on co-occurrence in dictionary components
-for i in range(n_components):
-    terms = df_components.columns[df_components.iloc[i] > 0]
-    for term1 in terms:
-        for term2 in terms:
-            if term1 != term2:
-                G.add_edge(term1, term2)
-
-# Plot the graph
-fig, ax = plt.subplots(figsize=(8, 8))
-pos = nx.spring_layout(G, k=0.3)
-nx.draw_networkx_nodes(G, pos, node_size=100, node_color='lightblue', alpha=0.8)
-nx.draw_networkx_edges(G, pos, edge_color='gray', alpha=0.5)
-nx.draw_networkx_labels(G, pos, font_size=8)
-ax.axis('off')
-st.pyplot(fig)
-
+    # Perform text preprocessing
+    vectorizer = CountVectorizer(stop_words='english')
+    X = vectorizer.fit_transform([text_input])
+    
+    # Perform dictionary learning
+    dl = DictionaryLearning(n_components=n_components, transform_algorithm='lasso_lars', random_state=0)
+    X_transformed = dl.fit_transform(X)
+    dictionary = dl.components_
+    
+    # Get the feature names (terms)
+    feature_names = vectorizer.get_feature_names_out()
+    
+    # Create a DataFrame with dictionary components and their corresponding terms
+    df_components = pd.DataFrame(dictionary, columns=feature_names)
+    df_components['Component'] = ['Component ' + str(i+1) for i in range(n_components)]
+    df_components = df_components.set_index('Component')
+    
+    # Display the DataFrame
+    st.markdown("### Dictionary Components")
+    st.dataframe(df_components)
+    
+    # Create a graph of terms and their connections
+    G = nx.Graph()
+    
+    # Add nodes to the graph
+    for term in feature_names:
+        G.add_node(term)
+    
+    # Add edges to the graph based on co-occurrence in dictionary components
+    for i in range(n_components):
+        terms = df_components.columns[df_components.iloc[i] > 0]
+        for term1 in terms:
+            for term2 in terms:
+                if term1 != term2:
+                    G.add_edge(term1, term2)
+    
+    # Plot the graph
+    fig, ax = plt.subplots(figsize=(8, 8))
+    pos = nx.spring_layout(G, k=0.3)
+    nx.draw_networkx_nodes(G, pos, node_size=100, node_color='lightblue', alpha=0.8)
+    nx.draw_networkx_edges(G, pos, edge_color='gray', alpha=0.5)
+    nx.draw_networkx_labels(G, pos, font_size=8)
+    ax.axis('off')
+    st.pyplot(fig)
+