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.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']>4]
      #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)