Create app.py

app.py

@@ -0,0 +1,58 @@
+import streamlit as st
+import joblib
+import numpy as np
+
+# Load the trained model, scaler, and PCA
+regressor = joblib.load('slump_regressor.pkl')
+scaler = joblib.load('scaler.pkl')
+
+def preprocess_data(X):
+    # Load the trained scaler and PCA
+    scaler = joblib.load('scaler.pkl')
+    pca = joblib.load('pca.pkl')
+
+    # Check if the input has 8 features
+    if X.shape[1] != 8:
+        raise ValueError("Input data should have 8 features.")
+
+    # Scale the input data using the loaded scaler
+    X_scaled = scaler.transform(X)
+
+    # Apply PCA using the loaded PCA transformer
+    X_pca = pca.transform(X_scaled)
+
+    return X_pca
+
+def predict_slump_app():
+    # Get the input values from the user
+    cement = st.number_input("Cement (kg/m^3)", min_value=0.0, step=0.1)
+    blast_furnace_slag = st.number_input("Blast Furnace Slag (kg/m^3)", min_value=0.0, step=0.1)
+    fly_ash = st.number_input("Fly Ash (kg/m^3)", min_value=0.0, step=0.1)
+    water = st.number_input("Water (kg/m^3)", min_value=0.0, step=0.1)
+    superplasticizer = st.number_input("Superplasticizer (kg/m^3)", min_value=0.0, step=0.1)
+    coarse_aggregate = st.number_input("Coarse Aggregate (kg/m^3)", min_value=0.0, step=0.1)
+    fine_aggregate = st.number_input("Fine Aggregate (kg/m^3)", min_value=0.0, step=0.1)
+    flow = st.number_input("FLOW (cm)", min_value=0.0, step=0.1)
+
+    # Prepare the input data
+    X = np.array([[cement, blast_furnace_slag, fly_ash, water, superplasticizer, coarse_aggregate, fine_aggregate, flow]])
+
+    # Preprocess the data
+    X_preprocessed = preprocess_data(X)
+
+    # Make the prediction
+    slump_prediction = regressor.predict(X_preprocessed)[0]
+
+    return slump_prediction
+
+def main():
+    st.set_page_config(page_title="Concrete Slump Strength Prediction")
+    st.title("Concrete Slump Strength Prediction")
+    st.write("Enter the concrete mix parameters to predict the slump.")
+
+    slump_prediction = predict_slump_app()
+    st.subheader("Predicted Slump Strength:")
+    st.write(f"{slump_prediction:.2f} MPA")
+
+if __name__ == '__main__':
+    main()