Create new file

apps/sdg.py

@@ -0,0 +1,63 @@
+import plotly.express as px
+import streamlit as st
+from sentence_transformers import SentenceTransformer
+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)