Update apps/sdg.py

apps/sdg.py

@@ -5,59 +5,60 @@ import umap.umap_ as umap
 import pandas as pd
 import os
 
-st.title("SDG Embedding Visualisation")
-
-with st.spinner("👑 load language model (sentence transformer)"):
-  model_name = 'sentence-transformers/all-MiniLM-L6-v2'
-  model = SentenceTransformer(model_name)
-
-with st.spinner("👑 load sdg data"):
-  df_osdg = pd.read_csv('https://zenodo.org/record/5550238/files/osdg-community-dataset-v21-09-30.csv',sep='\t')
-  df_osdg = df_osdg[df_osdg['agreement']>.95]
-  df_osdg = df_osdg[df_osdg['labels_positive']>3]
-  #df_osdg  = df_osdg[:1000]
-  
-  _lab_dict = {0: 'no_cat',
-              1:'SDG 1 - No poverty',
-                2:'SDG 2 - Zero hunger',
-                3:'SDG 3 - Good health and well-being',
-                4:'SDG 4 - Quality education',
-                5:'SDG 5 - Gender equality',
-                6:'SDG 6 - Clean water and sanitation',
-                7:'SDG 7 - Affordable and clean energy',
-                8:'SDG 8 - Decent work and economic growth', 
-               9:'SDG 9 - Industry, Innovation and Infrastructure',
-                10:'SDG 10 - Reduced inequality',
-               11:'SDG 11 - Sustainable cities and communities',
-               12:'SDG 12 - Responsible consumption and production',
-               13:'SDG 13 - Climate action',
-               14:'SDG 14 - Life below water',
-               15:'SDG 15 - Life on land',
-               16:'SDG 16 - Peace, justice and strong institutions',
-               17:'SDG 17 - Partnership for the goals',}
-               
-  labels = [_lab_dict[lab] for lab in df_osdg['sdg'] ]
-  #keys = list(df_osdg['keys'])
-  docs = list(df_osdg['text'])
-  docs_embeddings = model.encode(docs)
-
-with st.spinner("👑 prepare visualisation"):
-  n_neighbors = 15
-  n_components = 3
-  random_state =42
-  umap_model = (umap.UMAP(n_neighbors=n_neighbors, 
-                              n_components=n_components, 
-                              metric='cosine', 
-                              random_state=random_state)
-                          .fit(docs_embeddings))
-                          
-  docs_umap = umap_model.transform(docs_embeddings)
-
-with st.spinner("👑 create visualisation"):  
-  fig = px.scatter_3d(
-      docs_umap, x=0, y=1, z=2,
-      color=labels,
-      opacity = .5)#,    hover_data=[keys])
-  fig.update_scenes(xaxis_visible=False, yaxis_visible=False,zaxis_visible=False )
-  fig.update_traces(marker_size=4)
-  st.plotly_chart(fig)
+    def app():
+    st.title("SDG Embedding Visualisation")
+    
+    with st.spinner("👑 load language model (sentence transformer)"):
+      model_name = 'sentence-transformers/all-MiniLM-L6-v2'
+      model = SentenceTransformer(model_name)
+    
+    with st.spinner("👑 load sdg data"):
+      df_osdg = pd.read_csv('https://zenodo.org/record/5550238/files/osdg-community-dataset-v21-09-30.csv',sep='\t')
+      df_osdg = df_osdg[df_osdg['agreement']>.95]
+      df_osdg = df_osdg[df_osdg['labels_positive']>3]
+      #df_osdg  = df_osdg[:1000]
+      
+      _lab_dict = {0: 'no_cat',
+                  1:'SDG 1 - No poverty',
+                    2:'SDG 2 - Zero hunger',
+                    3:'SDG 3 - Good health and well-being',
+                    4:'SDG 4 - Quality education',
+                    5:'SDG 5 - Gender equality',
+                    6:'SDG 6 - Clean water and sanitation',
+                    7:'SDG 7 - Affordable and clean energy',
+                    8:'SDG 8 - Decent work and economic growth', 
+                   9:'SDG 9 - Industry, Innovation and Infrastructure',
+                    10:'SDG 10 - Reduced inequality',
+                   11:'SDG 11 - Sustainable cities and communities',
+                   12:'SDG 12 - Responsible consumption and production',
+                   13:'SDG 13 - Climate action',
+                   14:'SDG 14 - Life below water',
+                   15:'SDG 15 - Life on land',
+                   16:'SDG 16 - Peace, justice and strong institutions',
+                   17:'SDG 17 - Partnership for the goals',}
+                   
+      labels = [_lab_dict[lab] for lab in df_osdg['sdg'] ]
+      #keys = list(df_osdg['keys'])
+      docs = list(df_osdg['text'])
+      docs_embeddings = model.encode(docs)
+    
+    with st.spinner("👑 prepare visualisation"):
+      n_neighbors = 15
+      n_components = 3
+      random_state =42
+      umap_model = (umap.UMAP(n_neighbors=n_neighbors, 
+                                  n_components=n_components, 
+                                  metric='cosine', 
+                                  random_state=random_state)
+                              .fit(docs_embeddings))
+                              
+      docs_umap = umap_model.transform(docs_embeddings)
+    
+    with st.spinner("👑 create visualisation"):  
+      fig = px.scatter_3d(
+          docs_umap, x=0, y=1, z=2,
+          color=labels,
+          opacity = .5)#,    hover_data=[keys])
+      fig.update_scenes(xaxis_visible=False, yaxis_visible=False,zaxis_visible=False )
+      fig.update_traces(marker_size=4)
+      st.plotly_chart(fig)