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import streamlit as st # type: ignore
import numpy as np
import pandas as pd
import seaborn as sns # type: ignore
import matplotlib.pyplot as plt
import base64
import pickle
import time
@st.cache_data
def load_data(dataset):
df = pd.read_csv(dataset)
return df
def filedownload(df):
csv = df.to_csv(index=False)
b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions
href = f'<a href="data:file/csv;base64,{b64}" download="diabete_predictions.csv">Download CSV File</a>'
href2 = f'<a href="https://huggingface.co/spaces/Jasonntone/Brain_Stroke"></a>'
return href,href2
data = load_data('dataset/brain_stroke.csv')
meanGlucose = data['avg_glucose_level'].mean()
meanBmi = data['bmi'].mean()
meanAge = data['age'].mean()
st.sidebar.image('images/ahat.png',width=280)
def main():
st.markdown("<h1 style='text-align:center;color: red;'>Streamlit Brain Stroke Prediction App</h1>",unsafe_allow_html=True)
st.markdown("<h2 style='text-align:center;color: grey;'>Brain Stroke study in USA</h2>",unsafe_allow_html=True)
menu = ['Home','Analysis','Data Visualization','Machine Learning']
choice = st.sidebar.selectbox('Select Menu', menu)
if choice == 'Home':
left,middle,right = st.columns((2,3,2))
with middle:
col4, col5, col6 = st.columns(3)
with col4:
temp = st.metric(label="Average Glucose Level", value=meanGlucose, delta="From 5010 To 2012")
with col5:
temp = st.metric(label="Average BMI", value=meanBmi, delta="From 2010 To 2012")
with col6:
temp = st.metric(label="Average Age", value=meanAge, delta="From 2010 To 2012")
st.image('images/ahat.png',width=280)
if choice == 'Analysis':
st.subheader('Brain Stroke Dataset')
st.write(data.head())
if st.checkbox('Summary'):
st.write(data.describe())
elif st.checkbox('Correlation'):
fig = plt.figure(figsize=(15,5))
st.write(sns.heatmap(data.corr(),annot=True))
st.pyplot(fig)
elif choice == 'Data Visualization':
if st.checkbox('Countplot'):
fig = plt.figure(figsize=(15,5))
sns.countplot(x='age',data=data)
st.pyplot(fig)
elif st.checkbox('Scatterplot'):
fig = plt.figure(figsize=(15,5))
sns.scatterplot(x='avg_glucose_level',y='age',data=data,hue='stroke')
st.pyplot(fig)
elif choice == 'Machine Learning':
tab1, tab2, tab3 = st.tabs([":clipboard: Data",":bar_chart:✅ Visualisation", "📈🎯 Prediction"])
uploaded_file = st.sidebar.file_uploader('Upload your Dataset(.csv file)',
type=['csv'])
if uploaded_file:
df = load_data(uploaded_file)
with tab1:
st.subheader('Loaded Dataset')
st.write(df)
with tab2:
st.subheader("Heart Disease's Histogram")
fig = plt.figure(figsize=(8,8))
sns.histplot(x='heart_disease',data=data)
st.pyplot(fig)
with tab3:
model = pickle.load(open('./models/gbc_dump.pkl', 'rb')) # Load the trained model from disk
prediction = model.predict(df)
pp = pd.DataFrame(prediction, columns=['Prediction'])
ndf = pd.concat([df, pp], axis=1)
ndf['Prediction'].replace(0, 'No Stroke Risk', inplace=True)
ndf['Prediction'].replace(1, 'Stroke Risk', inplace=True)
st.header("📈🎯 Stroke Risk Prediction")
st.subheader("Predictions")
st.write(ndf)
if st.button(' 💾 Download csv file'):
st.markdown(filedownload(ndf), unsafe_allow_html=True)
if __name__ == '__main__':
main() |