Create app.py

app.py

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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)   
+@st.cache_data               
+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()
+