Create app.py

app.py

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+import streamlit as st
+import streamlit.components.v1 as components
+import pandas as pd
+import os
+import re
+import numpy as np
+from glob import glob
+import lightgbm as lgb
+import pickle
+
+os.environ['S3_BUCKET'] = 'seriouslyusers'
+os.environ['S3_BUCKET'] = "seriouslytestfaces"
+
+import io
+
+def get_s3_url(key):
+    url = 'https://s3.amazonaws.com/%s/%s' % (os.environ['S3_BUCKET'],key.replace(' ','+'))
+    return url
+
+embeddings = pd.read_parquet('./embeddings.parquet')
+
+def create_dir(directory):
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+def setup_user():
+    create_dir(f'./users/{st.session_state.name}')
+    create_dir(f'./users/{st.session_state.name}/likes')
+    create_dir(f'./users/{st.session_state.name}/models')
+
+def get_filename():
+    if 'preds' in st.session_state:
+        p = st.session_state.preds**4
+        p /= sum(p)
+        choice = np.random.choice(range(len(p)),p=p)
+        st.session_state.pred = st.session_state.preds[choice]
+        return embeddings.index[choice]
+    st.toast('Random for now')
+    return np.random.choice(embeddings.index)
+
+st.title('What does attractive mean to you?')
+st.session_state.name = st.text_input(label='Invent a unique alias (and remember it)')
+
+
+def liked(filename,like):
+    filename = f'./users/{st.session_state.name}/likes/' + filename.split('/')[-1] + '.' + str(like)[:1]
+    open(filename, 'a').close()
+
+def get_train_data():
+    clean = lambda file : file.replace('\\','/').split('/')[-1][:-2]
+    true_files = list(map(clean,glob(f'./users/{st.session_state.name}/likes/*.T')))
+    false_files = list(map(clean,glob(f'./users/{st.session_state.name}/likes/*.F')))
+    true_embeddings = embeddings.loc[true_files].values
+    false_embeddings = embeddings.loc[false_files].values
+    st.toast(f'Found {len(true_files)} positives and {len(false_files)} negatives')
+    labels = np.array([1 for _ in true_embeddings] + [0 for _ in false_embeddings])
+    st.session_state.labels = pd.Series(labels,index=true_files+false_files).rename('label')
+    X = np.vstack([true_embeddings,false_embeddings])
+    return X,labels
+
+def train_model(X,labels):
+    if len(labels) < 30:
+        st.toast('Not enough data')
+        return
+    if labels.mean() > 0.9:
+        st.toast('Not enough negatives')
+        return
+    if labels.mean() < 0.1:
+        st.toast('Not enough positives')
+        return
+    train_data = lgb.Dataset(X, label=labels)
+    num_round = 10
+    param = {'num_leaves':10, 'objective': 'binary', 'metric' : 'auc'}
+    bst = lgb.train(param, train_data, num_round)
+    in_sample_preds = bst.predict(X)
+    in_sample_score = np.corrcoef([in_sample_preds,np.array(labels)])[0][1]
+    st.session_state.score = in_sample_score
+    st.toast(f'Score = {in_sample_score:.1%}')
+    return bst
+    
+def rank_candidates(bst):
+    return bst.predict(embeddings.values)
+
+
+def train():
+    X,labels = get_train_data()
+    bst = train_model(X,labels)
+    if bst is None:
+        return
+    filename = f'./users/{st.session_state.name}/models/model.txt'
+    bst.save_model(filename)
+    preds = rank_candidates(bst)
+    st.session_state.preds = preds
+
+
+def cleanup():
+    files = glob(f'./users/{st.session_state.name}/likes/*')
+    for f in files:
+       os.remove(f)
+    if 'preds' in st.session_state:
+        del st.session_state.preds
+        del st.session_state.pred
+        
+    
+def get_extremes(n=4):
+    if 'preds' in st.session_state:
+        preds = pd.Series(st.session_state.preds,index=embeddings.index).sort_values(ascending=False)
+        return preds.iloc[:n].to_dict(),preds.iloc[-n:].to_dict()
+        
+def get_strange(n=4):
+    if 'labels' in st.session_state:
+        labels = st.session_state.labels
+        preds = pd.Series(st.session_state.preds,index=embeddings.index).loc[labels.index].rename('pred')
+        data = pd.concat([labels, preds],axis=1)
+        st.toast(data.columns)
+        
+        data['diff'] = data['pred'] - data['label']
+        data = data.sort_values('diff')['diff']
+        return data.iloc[:n].to_dict(),data.iloc[-n:].to_dict() 
+        
+        
+
+
+if st.session_state.name:
+    st.session_state.name = re.sub(r'[^A-Za-z0-9 ]+', '', st.session_state.name)[:100]
+    setup_user()
+    st.subheader(f"Let's start {st.session_state.name}")
+    filename = get_filename()
+    
+    
+    cc1, cc2 = st.columns(2)
+    c1,c2 = cc1.columns(2)
+    c1.button('Why not', on_click=liked, args=[filename,True])
+    c2.button('Nope', on_click=liked, args=[filename,False])
+    key = get_s3_url(filename)
+    cc1.image(key, width = 400)
+    c1,c2 = cc2.columns(2)
+    c1.button('Train',on_click=train,args=[])
+    c2.button('Start over',on_click=cleanup,args=[])
+    if 'preds' in st.session_state:
+        
+        cc1.write('Here is our guess')
+        cc1.metric("Probability you will like", f'{st.session_state.pred:.1%}')
+    
+        best,worst = get_extremes()
+        
+        cc2.subheader('Predicted best')
+        cs = cc2.columns(len(best))
+        for c,(file,pred) in zip(cs,best.items()):
+            c.metric("", f'{pred:.0%}')
+            c.image(get_s3_url(file), width = 100)
+            
+        cc2.subheader('Predicted worst')
+        cs = cc2.columns(len(worst))
+        for c,(file,pred) in zip(cs,worst.items()):
+            c.metric("", f'{pred:.0%}')
+            c.image(get_s3_url(file), width = 100)
+
+        cc1.metric("Overall model accuracy", f'{st.session_state.score:.0%}')
+
+        cc1.subheader('Where you confused me')
+
+        best,worst = get_strange()
+        
+        cc1.write("You didn't like my picks")
+        cs = cc1.columns(len(best))
+        for c,(file,pred) in zip(cs,best.items()):
+            c.metric("", "",f'{pred:.0%}')
+            c.image(get_s3_url(file), width = 100)
+
+        cc1.write("You liked these more than I thought")
+        cs = cc1.columns(len(worst))
+        for c,(file,pred) in zip(cs,worst.items()):
+            c.metric("","", f'{pred:.0%}')
+            c.image(get_s3_url(file), width = 100)
+        
+        
+
+        
+