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import streamlit as st |
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import numpy as np |
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import pickle |
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from sklearn.linear_model import LinearRegression |
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from sklearn.preprocessing import StandardScaler |
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from sklearn.decomposition import PCA |
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def load_data(): |
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X_train = np.random.rand(100, 5) |
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y_train = np.random.rand(100) |
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return X_train, y_train |
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def load_model_artifacts(): |
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with open('slump_regressor.pkl', 'rb') as f: |
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regressor = pickle.load(f) |
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with open('scaler.pkl', 'rb') as f: |
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scaler = pickle.load(f) |
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with open('pca.pkl', 'rb') as f: |
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pca = pickle.load(f) |
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return regressor, scaler, pca |
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def save_model_artifacts(regressor, scaler, pca): |
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with open('slump_regressor.pkl', 'wb') as f: |
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pickle.dump(regressor, f, protocol=pickle.HIGHEST_PROTOCOL) |
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with open('scaler.pkl', 'wb') as f: |
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pickle.dump(scaler, f, protocol=pickle.HIGHEST_PROTOCOL) |
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with open('pca.pkl', 'wb') as f: |
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pickle.dump(pca, f, protocol=pickle.HIGHEST_PROTOCOL) |
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def predict_slump_app(regressor, scaler, pca): |
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st.subheader("Enter Concrete Mix Parameters") |
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cement = st.number_input("Cement (kg/m³)", min_value=0.0, step=1.0) |
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slag = st.number_input("Slag (kg/m³)", min_value=0.0, step=1.0) |
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flyash = st.number_input("Fly Ash (kg/m³)", min_value=0.0, step=1.0) |
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water = st.number_input("Water (kg/m³)", min_value=0.0, step=1.0) |
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superplasticizer = st.number_input("Superplasticizer (kg/m³)", min_value=0.0, step=0.1) |
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coarseaggregate = st.number_input("Coarse Aggregate (kg/m³)", min_value=0.0, step=1.0) |
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fineaggregate = st.number_input("Fine Aggregate (kg/m³)", min_value=0.0, step=1.0) |
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if st.button("Predict Slump Strength"): |
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X_new = np.array([cement, slag, flyash, water, superplasticizer, coarseaggregate, fineaggregate]) |
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X_new = scaler.transform([X_new]) |
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X_new = pca.transform(X_new) |
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slump_prediction = regressor.predict(X_new)[0] |
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return slump_prediction |
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else: |
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return None |
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def main(): |
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st.set_page_config(page_title="Concrete Slump Strength Prediction") |
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st.title("Concrete Slump Strength Prediction") |
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st.write("Enter the concrete mix parameters to predict the slump.") |
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try: |
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regressor, scaler, pca = load_model_artifacts() |
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except (FileNotFoundError, UnpicklingError): |
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X_train, y_train = load_data() |
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regressor = LinearRegression() |
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regressor.fit(X_train, y_train) |
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scaler = StandardScaler() |
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scaler.fit(X_train) |
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pca = PCA(n_components=4) |
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pca.fit(scaler.transform(X_train)) |
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save_model_artifacts(regressor, scaler, pca) |
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slump_prediction = predict_slump_app(regressor, scaler, pca) |
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if slump_prediction is not None: |
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st.subheader("Predicted Slump Strength") |
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st.markdown(f"The predicted slump strength is **{slump_prediction:.2f} MPa**.") |
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if __name__ == '__main__': |
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main() |
