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app.py

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+# -*- coding: utf-8 -*-
+#  File: app.py
+#  Project: 'Homework #2 OTUS.ML.Advanced'
+#  Created by Gennady Matveev (gm@og.ly) on 02-01-2022.
+
+# Import libraries
+import os
+import pandas as pd
+import streamlit as st
+import requests
+
+st.set_page_config(page_title='OTUS.ML.ADV_HW2', page_icon='./sky.ico', layout='centered', initial_sidebar_state='expanded')
+
+padding = 0
+st.markdown(f""" <style>
+    .reportview-container .main .block-container{{
+        padding-top: {padding}rem;
+        padding-right: {padding}rem;
+        padding-left: {padding}rem;
+        padding-bottom: {padding}rem;
+    }} </style> """, unsafe_allow_html=True)
+    
+st.image('./sky.png')
+st.subheader('Homework #2 OTUS.ML.Advanced')
+st.write('Classification model for Heart Disease UCI: &nbsp;&nbsp;https://www.kaggle.com/ronitf/heart-disease-uci')
+st.markdown("""---""")
+
+# Import data, will need it for get requests
+@st.cache(ttl=600)
+def get_data():
+    url = 'https://drive.google.com/uc?export=download&id=1wY3r2MwQoa-jiyzRoEM_eF_EU11vrCs0'
+    return pd.read_csv(url, compression='zip')
+
+df = get_data()
+
+# Main interface
+row_num = st.number_input('Please choose features vector 0-302 or set values in the left sidebar', 
+                          min_value=0, max_value=302, value=42)
+x17 =df.iloc[row_num,:-1].to_frame().T
+st.write('Features, X')
+st.write(x17)
+
+# START Sidebar ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+with st.sidebar.expander("I want to choose my values", expanded=False):
+    age = st.number_input('Age', min_value=25, max_value=80, value=57)
+    sex = st.number_input('Sex', min_value=0, max_value=1, value=1)
+    cp = st.number_input('cp', min_value=0, max_value=4, value=0)
+    trestbps = st.number_input('trestbps', min_value=90, max_value=200, value=125)
+    chol = st.number_input('chol', min_value=125, max_value=550, value=240)
+    fbs = st.number_input('fbs', min_value=0, max_value=1, value=0)
+    restecg = st.number_input('restecg', min_value=0, max_value=2, value=1)
+    thalach = st.number_input('thalach', min_value=70, max_value=200, value=160)
+    exang = st.number_input('exang', min_value=0, max_value=1, value=0)
+    oldpeak = st.number_input('oldpeak', min_value=0, max_value=6, value=2)
+    slope = st.number_input('slope', min_value=0, max_value=2, value=2)
+    ca = st.number_input('ca', min_value=0, max_value=4, value=0)
+    thal = st.number_input('thal', min_value=0, max_value=3, value=2)
+    
+    features = age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal
+    send_req_sidebar = st.button('Get prediction')
+
+# END Sidebar ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+send_req = st.button('Send get request')
+
+backend_address = "https://hw2backend.herokuapp.com/predict/"
+
+# Main page button
+if send_req:
+    prediction = requests.get(backend_address, 
+                              params={"q": tuple(x17.values)})
+    st.code(f'Parameters sent: {x17.values}')
+    col1, col2 = st.columns(2)
+    with col1:
+        st.write('Model predicts')
+        st.success(f'y = {prediction.text}')
+    with col2:
+        st.write('Ground truth')
+        if int(prediction.text) == int(df.iloc[row_num]["target"]):
+            st.success(f'y = {int(df.iloc[row_num]["target"])}')
+        else:
+            st.warning(f'y = {int(df.iloc[row_num]["target"])}')
+
+# Sidebar button        
+
+if send_req_sidebar:
+    prediction = requests.get(backend_address, 
+                              params={"q": features})
+    st.code(f'Parameters sent: {features}')
+    st.write('Model predicts')
+    st.info(f'y = {prediction.text}')
+
+# Show this code
+with st.expander("Show code", expanded=False):
+    show_me = st.checkbox('Show code of this program')
+    if show_me:
+        st.code("""
+    # -*- coding: utf-8 -*-
+#  File: app.py
+#  Project: 'Homework #2 OTUS.ML.Advanced'
+#  Created by Gennady Matveev (gm@og.ly) on 02-01-2022.
+
+# Import libraries
+import pandas as pd
+import streamlit as st
+import requests
+
+st.set_page_config(page_title='OTUS.ML.ADV_HW2', page_icon='./car_at_night.ico',
+                   layout='centered', initial_sidebar_state='expanded')
+
+padding = 0
+st.markdown(f''' <style>
+    .reportview-container .main .block-container{{
+        padding-top: {padding}rem;
+        padding-right: {padding}rem;
+        padding-left: {padding}rem;
+        padding-bottom: {padding}rem;
+    }} </style> ''', unsafe_allow_html=True)
+    
+st.image('./sky.png')
+st.subheader('Homework #2 OTUS.ML.Advanced')
+st.write('Classification model for Heart Disease UCI: &nbsp;&nbsp;https://www.kaggle.com/ronitf/heart-disease-uci')
+st.markdown('''---''')
+
+# Import data, will need it for get requests
+@st.cache(ttl=600)
+def get_data():
+    url = 'https://drive.google.com/uc?export=download&id=1wY3r2MwQoa-jiyzRoEM_eF_EU11vrCs0'
+    return pd.read_csv(url, compression='zip')
+
+df = get_data()
+
+# Main interface
+row_num = st.number_input('Please choose features vector 0-302 or set values in the left sidebar', 
+                          min_value=0, max_value=302, value=42)
+x17 =df.iloc[row_num,:-1].to_frame().T
+st.write('Features, X')
+st.write(x17)
+
+# START Sidebar ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+with st.sidebar.expander("I want to choose my values", expanded=False):
+    age = st.number_input('Age', min_value=25, max_value=80, value=57)
+    sex = st.number_input('Sex', min_value=0, max_value=1, value=1)
+    cp = st.number_input('cp', min_value=0, max_value=4, value=0)
+    trestbps = st.number_input('trestbps', min_value=90, max_value=200, value=125)
+    chol = st.number_input('chol', min_value=125, max_value=550, value=240)
+    fbs = st.number_input('fbs', min_value=0, max_value=1, value=0)
+    restecg = st.number_input('restecg', min_value=0, max_value=2, value=1)
+    thalach = st.number_input('thalach', min_value=70, max_value=200, value=160)
+    exang = st.number_input('exang', min_value=0, max_value=1, value=0)
+    oldpeak = st.number_input('oldpeak', min_value=0, max_value=6, value=2)
+    slope = st.number_input('slope', min_value=0, max_value=2, value=2)
+    ca = st.number_input('ca', min_value=0, max_value=4, value=0)
+    thal = st.number_input('thal', min_value=0, max_value=3, value=2)
+    
+    features = age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal
+    send_req_sidebar = st.button('Get prediction')
+
+# END Sidebar ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+ssend_req = st.button('Send get request')
+
+backend_address = "https://hw2backend.herokuapp.com/predict/"
+
+# Main page button
+if send_req:
+    prediction = requests.get(backend_address, 
+                              params={"q": tuple(x17.values)})
+    st.code(f'Parameters sent: {x17.values}')
+    col1, col2 = st.columns(2)
+    with col1:
+        st.write('Model predicts')
+        st.success(f'y = {prediction.text}')
+    with col2:
+        st.write('Ground truth')
+        if int(prediction.text) == int(df.iloc[row_num]["target"]):
+            st.success(f'y = {int(df.iloc[row_num]["target"])}')
+        else:
+            st.warning(f'y = {int(df.iloc[row_num]["target"])}')
+
+# Sidebar button        
+
+if send_req_sidebar:
+    prediction = requests.get(backend_address, 
+                              params={"q": features})
+    st.code(f'Parameters sent: {features}')
+    st.write('Model predicts')
+    st.info(f'y = {prediction.text}')
+    """
+    )
+    
+    show_api = st.checkbox('Show code of FastAPI backend')
+    if show_api:
+        st.code("""
+        # -*- coding: utf-8 -*-
+#  File: main.py
+#  Project: 'Homework #2 OTUS.ML.Advanced'
+#  Created by Gennady Matveev (gm@og.ly) on 04-01-2022.
+#  Copyright 2022. All rights reserved.
+
+# Import libraries
+import uvicorn
+from atom import ATOMLoader
+from fastapi import FastAPI, Query
+import pandas as pd
+from typing import List, Optional
+
+cols = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',
+       'exang', 'oldpeak', 'slope', 'ca', 'thal']
+
+atom = ATOMLoader("./models/atom20220104-32256", verbose=0)
+
+# Initialize app
+app = FastAPI()
+
+# Routes
+@app.get('/')
+async def index():
+    return {"text": "Hello, fellow ML students"}
+
+
+@app.get('/predict/')
+async def predict(q: Optional[List[float]] = Query(None)):
+    dfx = pd.DataFrame([q], columns = cols)
+    prediction = atom.predict(dfx)
+    return int(prediction[0])
+
+
+if __name__ == '__main__':
+    # port = int(os.environ.get("PORT", 8080))
+    port = int(os.environ.get("PORT", 8080))
+    uvicorn.run("main:app", host="0.0.0.0", port=port)       
+"""
+)
+
+    st.markdown("And, finally, classification model itself on [Colab](https://colab.research.google.com/github/oort77/otusmladvhw2-notebook/blob/main/otus_adv_hw2.ipynb)")