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| 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 | |
| def app(): | |
| st.title("SDG Embedding Visualisation") | |
| with st.expander("ℹ️ - About this app", expanded=True): | |
| st.write( | |
| """ | |
| Information cartography - Get your word/phrase/sentence/paragraph embedded and visualized. | |
| The (English) sentence-transformers model "all-MiniLM-L6-v2" maps sentences & paragraphs to a 384 dimensional dense vector space This is normally used for tasks like clustering or semantic search, but in this case, we use it to place your text to a 3D map. Before plotting, the dimension needs to be reduced to three so we can actually plot it, but preserve as much information as possible. For this, we use a technology called umap. | |
| On this page, you find thousands of text excerpts that were labelled by the community volunteers with respect to Sustainable Development Goals, a project by OSDG.ai, embedded as described. Ready to explore. | |
| """) | |
| with st.spinner("👑 load data"): | |
| df_osdg = pd.read_csv("sdg_umap.csv", sep = "|") | |
| #labels = [_lab_dict[lab] for lab in df_osdg['label'] ] | |
| keys = list(df_osdg['keys']) | |
| #docs = list(df_osdg['text']) | |
| agree = st.checkbox('add labels') | |
| if agree: | |
| with st.spinner("👑 create visualisation"): | |
| fig = px.scatter_3d( | |
| df_osdg, x='coord_x', y='coord_y', z='coord_z', | |
| 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) | |
| else: | |
| with st.spinner("👑 create visualisation"): | |
| fig = px.scatter_3d( | |
| df_osdg, x='coord_x', y='coord_y', z='coord_z', | |
| 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) |