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()