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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 | |
| 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)") |