update app.py

app.py

@@ -1,113 +1,11 @@
 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
+from predict_page import show_predict_page
+from explore_page import show_explore_page
 
 
+page = st.sidebar.selectbox("Explore Or Predict", ("Predict", "Explore"))
 
-# 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)
+if page == "Predict":
+    show_predict_page()
+else:
+    show_explore_page()