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| import streamlit as st | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import base64 | |
| import pickle | |
| st.set_option('deprecation.showPyplotGlobalUse', False) | |
| def load_data(dataset): | |
| df = pd.read_csv(dataset) | |
| return df | |
| st.sidebar.image('photo_house.jpg',width=300) | |
| def main(): | |
| st.markdown("<h1 style='text-align:center;color: brown;'>Streamlit Housing App</h1>",unsafe_allow_html=True) | |
| st.markdown("<h2 style='text-align:center;color: black;'>Housing study in Cameroon</h2>",unsafe_allow_html=True) | |
| menu = ['Home','Data Analysis','Data Visualisation','Machine Learning'] | |
| choice = st.sidebar.selectbox('Select Menu',menu) | |
| if choice == 'Home': | |
| left,middle,right = st.columns((2,3,2)) | |
| with middle: | |
| st.image('photo_house.jpg',width=300) | |
| st.write('This is an app that will analyse value of house with some python tools that can optimize decisions') | |
| st.subheader('house value Informations') | |
| st.write('') | |
| if choice == 'Data Analysis': | |
| st.subheader('Dataset') | |
| data = load_data('housing.csv') | |
| st.write(data.head(5)) | |
| if st.checkbox('Summary'): | |
| st.write(data.describe().head()) | |
| elif st.checkbox('Correlation'): | |
| plt.figure(figsize=(15,15)) | |
| st.write(sns.heatmap(data.corr(),annot=True)) | |
| st.pyplot() | |
| if choice == 'Data Visualisation': | |
| if st.checkbox('Pairplot'): | |
| fig = plt.figure(figsize=(5,5)) | |
| data = load_data('housing.csv') | |
| sns.pairplot(data=data) | |
| st.pyplot(fig) | |
| if choice == 'Machine Learning': | |
| tab1, tab2, tab3 = st.tabs([":clipboard: Data",":bar_chart: Visualisation", ":mask: :smile: Prediction"]) | |
| uploaded_files = st.sidebar.file_uploader('Upload your input CSV file',type=['csv']) | |
| if uploaded_files: | |
| dfs = load_data(uploaded_files) | |
| with tab1: | |
| st.subheader('Loaded dataset') | |
| st.write(dfs) | |
| with tab2: | |
| model = pickle.load(open('model.pkl', 'rb')) | |
| prediction = model.predict() | |
| st.subheader('prediction') | |
| st.write(prediction) | |
| 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>' | |
| return href | |
| button = st.button('Download') | |
| if button : | |
| st.markdown(filedownload(ndf), unsafe_allow_html=True) | |
| # If the file was imported as a module, the code would not run. | |
| if __name__ == '__main__': | |
| main() | |