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app.py

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+import streamlit as st
+from PIL import Image
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pickle
+import base64
+from streamlit_option_menu import option_menu # Import the option_menu
+
+def load_data(data):
+    df = pd.read_csv(data)
+    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>'
+    return href
+
+st.sidebar.image('images/photo_2025-05-23_18-50-58.jpg')
+
+def main():
+    st.markdown("<h1 style='text-align: center; color:brown;'>Diabete Prediction</h1>", unsafe_allow_html=True)
+    st.markdown("<h3 style='text-align: center; color: white;'>Diabete Study in Cameroun</h3>", unsafe_allow_html=True)
+
+    # Replace st.sidebar.selectbox with option_menu
+    with st.sidebar: # Use a 'with' block for the sidebar to ensure the menu is placed there
+        selected = option_menu(
+            menu_title=None,  # No title for the menu
+            options=["Home", "Analysis", "Data Visualisation", "Machine Learning", "About"], # Your menu options
+            icons=["house", "clipboard-data", "bar-chart", "robot", "info-circle"], # Optional: icons for each option
+            menu_icon="cast", # Optional: icon for the menu itself
+            default_index=0, # Default selected option
+            styles={
+                "container": {"padding": "5px!important", "background-color": "#fafafa"},
+                "icon": {"color": "brown", "font-size": "20px"},
+                "nav-link": {"font-size": "16px", "text-align": "left", "margin":"0px", "--hover-color": "#eee"},
+                "nav-link-selected": {"background-color": "brown"},
+            }
+        )
+    
+    data = load_data("diabetes.csv")
+
+    if selected == "Home": # Use 'selected' instead of 'choice'
+        left, middle, right = st.columns((2,3,2))
+        with middle:
+            st.image("images/photo_2025-05-23_18-49-29.jpg", width=400)
+        st.write('This is an app that will analyse diabetes Datas with some python tools that can optimize decisions')
+        st.subheader('Diabetis Information')
+        st.write('In Cameroon, the prevalence of diabetes in adults in urban areas is currently estimated at 6 – 8%, with as much as 80% of people living with diabetes who are currently undiagnosed in the population. Further, according to data from Cameroon in 2002, only about a quarter of people with known diabetes actually had adequate control of their blood glucose levels. The burden of diabetes in Cameroon is not only high but is also rising rapidly. Data in Cameroonian adults based on three cross-sectional surveys over a 10-year period (1994–2004) showed an almost 10-fold increase in diabetes prevalence.')
+
+    elif selected == "Analysis":
+        st.subheader("Diabetes Analysis")
+        st.dataframe(data.head())
+
+        if st.checkbox("Summary"):
+            st.write(data.describe())
+
+        if st.checkbox("Correlation"):
+            fig = plt.figure(figsize=(15,15))
+            sns.heatmap(data.corr(), annot=True) # Removed st.write around sns.heatmap
+            st.pyplot(fig)
+
+        if st.checkbox("Column Names"):
+            st.write(data.columns)
+        st.markdown(filedownload(data), unsafe_allow_html=True)
+
+    elif selected == "Data Visualisation": # Corrected to "Data Visualisation" as in the options
+        st.subheader("Data Visualisation")
+        if st.checkbox("Countplot"):
+            fig = plt.figure(figsize=(15,15))
+            sns.countplot(x=data['Age']) # Removed st.write around sns.countplot
+            st.pyplot(fig)
+
+        if st.checkbox("Caterplot"):
+            fig = plt.figure(figsize=(15,15))
+            sns.scatterplot(x='Glucose', y='Age', data=data, hue='Outcome') # Removed st.write around sns.scatterplot
+            st.pyplot(fig)
+        
+    elif selected == "Machine Learning":
+        st.subheader("Machine Learning")
+        tab1,tab2,tab3=st.tabs([":clipboard: data",":bar_chart: Visualisation",":mask: Prediction"])
+        uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"])
+
+        if uploaded_file:
+            df = load_data(uploaded_file)
+            with tab1:
+                st.subheader("Loaded Data")
+                st.write(df)
+            with tab2:
+                st.subheader("Histogram glucose")
+                fig = plt.figure(figsize=(8,8))
+                sns.histplot(data=df, x='Glucose')
+                st.pyplot(fig)
+            with tab3:
+                model = pickle.load(open('model_dump.pkl','rb'))
+                prediction = model.predict(df) 
+                st.subheader("Prediction")
+                #transformation de l'arrey predict en datafram
+                pp = pd.DataFrame(prediction, columns=['prediction'])
+                #concatenation avec le df de depart
+
+                ndf = pd.concat([df,pp],axis=1)
+                #ndf.Prediction = ndf.prediction.map({0:'NO diabete',1:'diabete'})
+                ndf.prediction.replace(0,'NO diabete Risk', inplace=True)
+                ndf.prediction.replace(1,'diabete Risk', inplace=True)
+                st.write(ndf)
+
+                button = st.button("Download")
+                if button:
+                    st.markdown(filedownload(ndf), unsafe_allow_html=True)
+                    st.write("Downloading..")
+    elif selected == "About": # Added the "About" section
+        st.subheader("About This Application")
+        st.write("This application was developed to analyze diabetes data, provide insights through visualizations, and predict diabetes risk using machine learning models.")
+        st.write("It serves as a tool to help understand and manage diabetes prevalence in regions like Cameroon.")
+
+
+if __name__ == '__main__':
+    main()

model_dump.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4e2309032256d1b72706608262ecbe9bc7db9f1681b2a9112f2e4623004ea315
+size 1883055