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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: 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=185)
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: 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)") |