Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import pickle
+
+# Charger le jeu de données initial
+data = pd.read_csv('concrete.csv')
+
+# Créer un dictionnaire de correspondance entre les noms de colonnes et les noms de variables
+column_name_mapping = {
+    'cement': 'cement',
+    'slag': 'slag',
+    'ash': 'ash',
+    'water': 'water',
+    'superplastic': 'superplastic',
+    'fineagg': 'fineagg',
+    'age': 'age'
+}
+
+# Charger le modèle de régression enregistré avec pickle
+with open('best_model.pkl', 'rb') as f:
+    best_model = pickle.load(f)
+
+# Configuration de la mise en page
+st.set_page_config(layout='wide', initial_sidebar_state='expanded')
+
+# Définir le style CSS
+with open('style.css') as f:
+    st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+# Barre latérale avec les paramètres de visualisation et de prédiction
+with st.sidebar:
+    st.title('Concrete Data APP')
+    st.image('concrete.jpg')
+
+    st.subheader('Heat map parameter')
+    cement_value = st.selectbox('Age', data['age'].unique())
+
+    st.subheader('Donut chart parameter')
+    slag_value = st.selectbox('Select data', data['slag'].unique())
+
+    st.subheader('Line chart parameters')
+    plot_data = st.multiselect('Select data', ['cement', 'slag', 'ash'], ['cement', 'slag', 'ash'])
+    plot_height = st.slider('Specify plot height', 200, 500, 250)
+
+    st.subheader('Prediction')
+    do_prediction = st.checkbox('Perform prediction')
+
+    st.subheader('Upload Dataset')
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    if uploaded_file is not None:
+        try:
+            data = pd.read_csv(uploaded_file)
+        except pd.errors.EmptyDataError:
+            st.error("Error: The uploaded CSV file is empty or could not be read.")
+        except Exception as e:
+            st.error(f"An error occurred: {str(e)}")
+
+# Row A
+st.markdown('### Metrics')
+col1, col2, col3 = st.columns(3)
+
+# Nombre d'échantillons
+num_samples = 905
+col1.metric("Number of Samples", 905)
+
+# strength moyen
+average_strength = data['superplastic'].mean()
+col2.metric("Average superplastic", f"{average_strength:.2f}")
+
+# cement moyen
+average_cement = data['cement'].mean()
+col3.metric("Average Cement", f"{average_cement:.2f}")
+
+# Row B
+st.markdown('### Donut chart')
+top_10_ash = data['ash'].value_counts().head(10).index.tolist()
+top_10_data = data[data['ash'].isin(top_10_ash)]
+donut_fig = px.pie(top_10_data, names='ash', hole=0.5)
+st.plotly_chart(donut_fig, use_container_width=True)
+
+# Row C
+st.markdown('### Line chart')
+st.line_chart(data[plot_data], height=plot_height)
+
+# Prediction Section
+if do_prediction:
+    st.markdown('### Predicted strength')
+    cement = st.number_input('Cement', min_value=100.0, max_value=600.0, value=300.0, step=1.0, key='cement')
+    slag = st.number_input('Slag', min_value=0.0, max_value=300.0, value=100.0, step=1.0, key='slag')
+    ash = st.number_input('ash', min_value=0.0, max_value=200.0, value=50.0, step=1.0, key='ash')
+    water = st.number_input('Water', min_value=100.0, max_value=300.0, value=200.0, step=1.0, key='water')
+    superplastic = st.number_input('Superplastic', min_value=0.0, max_value=20.0, value=5.0, step=0.1, key='superplastic')
+    coarseagg = st.number_input('Coarse Aggregate', min_value=600.0, max_value=1200.0, value=1000.0, step=1.0, key='coarseagg')
+    fineagg = st.number_input('Fine Aggregate', min_value=500.0, max_value=1000.0, value=600.0, step=1.0, key='fineagg')
+    age = st.number_input('Age', min_value=1, max_value=365, value=28, step=1, key='age')
+
+    input_data = pd.DataFrame({
+        'cement': [cement],
+        'slag': [slag],
+        'ash': [ash],
+        'water': [water],
+        'superplastic': [superplastic],
+        'coarseagg': [coarseagg],
+        'fineagg': [fineagg],
+        'age': [age]
+    })
+
+    if st.button('Predict'):
+        predicted_strength = best_model.predict(input_data)
+        st.write(f'Predicted strength: {predicted_strength[0]:.2f}')