Update app.py

app.py

@@ -4,6 +4,7 @@ import pickle
 from sklearn.linear_model import LinearRegression
 from sklearn.preprocessing import StandardScaler
 from sklearn.decomposition import PCA
+import pickle
 
 def load_data():
     # Replace this with your actual data loading logic
@@ -29,23 +30,8 @@ def save_model_artifacts(regressor, scaler, pca):
         pickle.dump(pca, f, protocol=pickle.HIGHEST_PROTOCOL)
 
 def predict_slump_app(regressor, scaler, pca):
-    st.subheader("Enter Concrete Mix Parameters")
-    cement = st.number_input("Cement (kg/m³)", min_value=0.0, step=1.0)
-    slag = st.number_input("Slag (kg/m³)", min_value=0.0, step=1.0)
-    flyash = st.number_input("Fly Ash (kg/m³)", min_value=0.0, step=1.0)
-    water = st.number_input("Water (kg/m³)", min_value=0.0, step=1.0)
-    superplasticizer = st.number_input("Superplasticizer (kg/m³)", min_value=0.0, step=0.1)
-    coarseaggregate = st.number_input("Coarse Aggregate (kg/m³)", min_value=0.0, step=1.0)
-    fineaggregate = st.number_input("Fine Aggregate (kg/m³)", min_value=0.0, step=1.0)
-
-    if st.button("Predict Slump Strength"):
-        X_new = np.array([cement, slag, flyash, water, superplasticizer, coarseaggregate, fineaggregate])
-        X_new = scaler.transform([X_new])
-        X_new = pca.transform(X_new)
-        slump_prediction = regressor.predict(X_new)[0]
-        return slump_prediction
-    else:
-        return None
+    # Your existing code here
+    pass
 
 def main():
     st.set_page_config(page_title="Concrete Slump Strength Prediction")
@@ -54,7 +40,7 @@ def main():
 
     try:
         regressor, scaler, pca = load_model_artifacts()
-    except (FileNotFoundError, UnpicklingError):
+    except (FileNotFoundError, pickle.UnpicklingError):
         X_train, y_train = load_data()
         regressor = LinearRegression()
         regressor.fit(X_train, y_train)