create app.py

app.py

@@ -0,0 +1,113 @@
+import streamlit as st
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.impute import SimpleImputer
+from xgboost import XGBRegressor
+from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import StandardScaler
+import joblib
+
+
+
+# Load the numerical imputer
+#num_imputer = joblib.load("numerical_imputer.joblib")
+
+# Load the categorical imputer
+#cat_imputer = joblib.load("categorical_imputer.joblib")
+
+# Load the scaler
+#scaler = joblib.load("scaler.joblib")
+
+# Load the label encoder for 'family' feature
+#le_family = joblib.load("le_family.joblib")
+
+# Load the label encoder for 'holiday_type' feature
+#le_holiday_type = joblib.load("le_holiday_type.joblib")
+
+# Load the label encoder for 'city' feature
+#le_city = joblib.load("le_city.joblib")
+
+# Load the final model
+regressor = joblib.load("Best_model.joblib")
+
+
+
+#@st.cache_resource()
+def show_predict_page():
+     # Add a title and subtitle
+    st.write("<center><h1>Predicting Sales App</h1></center>", unsafe_allow_html=True)
+
+
+    # Add a subtitle or description
+    st.write("This app predict sales by the using machine learning, based on certain input parameters. Simply enter the required information and click 'Predict' to get a sales prediction!")
+
+    st.subheader("Enter the following details to predict sales")
+
+    input_data = {
+        'store_nbr': st.slider("store_nbr", step=1, min_value=0, max_value=54),
+        'onpromotion': st.number_input("onpromotion, 0 - 800", min_value=0, max_value=800),
+        'transactions': st.number_input("Number of Transactions, 0 - 10000", min_value=0, max_value=10000),
+        'oil_price': st.number_input("oil_price, 1 - 200", step=1, min_value=0, max_value=200),
+        'cluster': st.slider("cluster", step=1, min_value=0, max_value=17),
+        'day': st.slider("day", 1, 31, 1),
+        'year': st.selectbox("year", [1970]),
+        'month': st.slider("month", 1, 12, 1),
+        #'dayofmonth': st.slider("dayofmonth", 1, 31, 1),
+        #'dayofweek': st.slider("dayofweek, 0=Sun and 6=Sat", step=1, min_value=1, max_value=6),
+        'family': st.selectbox("products", ['AUTOMOTIVE', 'Personal Care', 'Beverages', 'STATIONERY', 'Food', 'CLEANING', 'HARDWARE', 'Home and Kitchen', 'Clothing', 'PET SUPPLIES', 'ELECTRONICS']),
+        'holiday_type': st.selectbox("holiday_type", ['Workday', 'holiday']),
+        'city': st.selectbox("City", ['Salinas', 'Quito', 'Cayambe', 'Latacunga', 'Riobamba', 'Ibarra', 'Santo Domingo', 'Guaranda', 'Ambato', 'Guayaquil', 'Daule', 'Babahoyo', 'Quevedo', 'Playas', 'Cuenca', 'Loja', 'Machala', 'Esmeraldas', 'El Carmen', 'Libertad', 'Manta', 'Puyo'])
+    }
+
+# Create a button to make a prediction
+
+    if st.button("Predict", key="predict_button", help="Click to make a prediction."):
+        # Convert the input data to a pandas DataFrame
+        input_df = pd.DataFrame([input_data])
+
+
+# Selecting categorical and numerical columns separately
+#        cat_columns = [col for col in input_df.columns if input_df[col].dtype == 'object']
+#        num_columns = [col for col in input_df.columns if input_df[col].dtype != 'object']
+
+
+# Apply the imputers
+#        input_df_imputed_cat = cat_imputer.transform(input_df[cat_columns])
+#        input_df_imputed_num = num_imputer.transform(input_df[num_columns])
+
+# Convert the NumPy arrays to DataFrames
+#        input_df_imputed_cat = pd.DataFrame(input_df_imputed_cat, columns=cat_columns)
+#        input_df_imputed_num = pd.DataFrame(input_df_imputed_num, columns=num_columns)
+        
+
+# Scale the numerical columns
+#        input_df_scaled = scaler.transform(input_df_imputed_num)
+#        input_scaled_df = pd.DataFrame(input_df_scaled , columns = num_columns)
+
+#        input_df_imputed  = pd.concat([input_df_imputed_cat, input_scaled_df], axis=1)
+        
+ # Encode the categorical columns
+        # Encode the categorical columns
+#        input_df_imputed['family'] = le_family.transform(input_df_imputed['family'])
+#        input_df_imputed['holiday_type'] = le_holiday_type.transform(input_df_imputed['holiday_type'])
+#        input_df_imputed['city'] = le_city.transform(input_df_imputed['city'])
+
+
+        #input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat))
+        #input_encoded_df.columns = input_encoded_df.columns.astype(str)
+        
+
+#joining the cat encoded and num scaled
+#        final_df = input_df_imputed 
+
+# Make a prediction
+        prediction = round(regressor.predict(input_df)[0], 2)
+
+
+# Display the prediction
+     #st.write(f"The predicted sales are: {prediction}.")
+
+# Display the prediction
+        st.subheader("Sales Prediction")
+        st.write("The predicted sales for the company is:", prediction)