Update app.py

app.py

@@ -38,22 +38,16 @@ with col1:
 with col2:
     input_data['day'] = st.slider("Day", 1, 31)
     input_data['month'] = st.slider("Month", 1, 12, value=6)
-    input_data['year'] = st.number_input("Year", 2018, 2020, value=2020)
+    input_data['year'] = st.number_input("Year", 2018, 2020, value=2020, step=1)
 
-# Create a button to predict
+# Create a button to make a prediction
 if st.button("Predict"):
     # Feature Scaling
     numerical_cols = ['day', 'month', 'year', 'shop_id', 'item_id', 'item_price', 'item_category_id']
-    scaler.fit(input_data)
-    input_df = pd.DataFrame(input_data, index=[0])
-    input_df_scaled = scaler.transform(input_df[numerical_cols])
-    input_df_scaled = pd.DataFrame(input_df_scaled, columns=numerical_cols)
-    
-    # Fit the model
-    rf_model.fit(input_df_scaled)
+    input_df = pd.DataFrame(input_data, index=[0])[numerical_cols]
     
     # Make predictions using the trained model
-    predictions = rf_model.predict(input_df_scaled)
+    predictions = rf_model.predict(input_df)
     
     # Display the predicted sales value to the user
     st.write("The predicted sales are:", predictions[0])