bugs fixed

app.py

@@ -250,27 +250,28 @@ if submit_planning:
         cluster_box, z = st.columns(2, gap="medium")
         with cluster_box:
             num_clusters = st.slider('Number of clusters to display', min_value=2, max_value=10, value=2)
-        # submit_clustering = st.button('Start clustering')
 
-        # if submit_clustering:
-        st.subheader("Examples of clusters")
-        super_cgrs_dict = reassign_nums(tree)
+        submit_clustering = st.button('Start clustering')
 
-        reduced_super_cgrs_dict = process_all_rs_cgrs(super_cgrs_dict)
+        if submit_clustering:
+            st.subheader("Examples of clusters")
+            super_cgrs_dict = reassign_nums(tree)
 
-        mfp = MorganFingerprint()
+            reduced_super_cgrs_dict = process_all_rs_cgrs(super_cgrs_dict)
 
-        results = cluster_molecules(reduced_super_cgrs_dict, mfp, max_clusters=num_clusters)
+            mfp = MorganFingerprint()
 
-        # Access results
-        clusters = results['clusters_dict']
+            results = cluster_molecules(reduced_super_cgrs_dict, mfp, max_clusters=num_clusters)
 
-        for cluster_num, node_id_list in clusters.items():
-            st.markdown(f"Cluster's number: ``{cluster_num}``")
-            node_id = node_id_list[0]
-            num_steps = len(tree.synthesis_route(node_id))
-            route_score = round(tree.route_score(node_id), 3)
-            st.image(get_route_svg(tree, node_id), caption=f"Route {node_id}; {num_steps} steps; Route score: {route_score}")
+            # Access results
+            clusters = results['clusters_dict']
+
+            for cluster_num, node_id_list in clusters.items():
+                st.markdown(f"Cluster's number: ``{cluster_num}``")
+                node_id = node_id_list[0]
+                num_steps = len(tree.synthesis_route(node_id))
+                route_score = round(tree.route_score(node_id), 3)
+                st.image(get_route_svg(tree, node_id), caption=f"Route {node_id}; {num_steps} steps; Route score: {route_score}")
 
         stat_col, download_col = st.columns(2, gap="medium")
 

cluster/clustering.py

@@ -95,7 +95,7 @@ def optimal_cluster_num(Z, distance_matrix, max_clusters=10):
     
     return cluster_range[np.argmax(silhouette_scores)]
 
-def perform_clustering(Z, threshold=0.0, max_clusters=10):
+def perform_clustering(Z, similarity_df, threshold=0.0, max_clusters=10):
     """Perform hierarchical clustering with automatic cluster number optimization.
     
     Args:
@@ -158,7 +158,7 @@ def cluster_molecules(cgrs, fingerprint_method, threshold=0.0, max_clusters=10,
     Z = calculate_linkage(similarity_df, method=linkage_method)
     
     # Perform clustering
-    cluster_labels = perform_clustering(Z, threshold, max_clusters)
+    cluster_labels = perform_clustering(Z, similarity_df, threshold, max_clusters)
     
     # Create clusters dictionary
     clusters_dict = create_clusters_dict(cluster_labels, np.array(labels))

cluster/rs_cgr.py

@@ -1,3 +1,5 @@
+from CGRtools.containers.bonds import DynamicBond
+
 def s_cgr2rs_cgr(s_cgr):
     cgr_prods = [s_cgr.substructure(c) for c in s_cgr.connected_components]
     target_cgr = cgr_prods[0]