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app.py

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+import pandas as pd
+import streamlit as st
+import numpy as np
+from matplotlib import pyplot as plt
+import pickle
+import sklearn 
+from PIL import Image
+
+
+
+# Load the saved components
+with open("dt_model.pkl", "rb") as f:
+    components = pickle.load(f)
+
+# Extract the individual components
+num_imputer = components["num_imputer"]
+cat_imputer = components["cat_imputer"]
+encoder = components["encoder"]
+scaler = components["scaler"]
+dt_model = components["models"]
+
+# Create the app
+
+st.set_page_config(
+    layout="wide"
+)
+
+
+# Add an image or logo to the app
+image = Image.open('grocery_store.jpg')
+
+# Open the image file
+st.image(image)
+
+
+#add app title
+st.title(":moneybag: SALES PREDICTION MACHINE LEARNING APP :moneybag:")
+
+
+# Add some text
+st.write("Enter some data for Prediction.")
+
+ # Create the input fields
+input_data = {}
+col1,col2,col3 = st.columns(3)
+with col1:
+    input_data['store_nbr'] = st.slider("store_nbr",0,54)
+    input_data['products'] = st.selectbox("products", ['AUTOMOTIVE', 'CLEANING', 'BEAUTY', 'FOODS', 'STATIONERY',
+       'CELEBRATION', 'GROCERY', 'HARDWARE', 'HOME', 'LADIESWEAR',
+       'LAWN AND GARDEN', 'CLOTHING', 'LIQUOR,WINE,BEER', 'PET SUPPLIES'])
+    input_data['onpromotion'] =st.number_input("onpromotion",step=1)
+    input_data['state'] = st.selectbox("state", ['Pichincha', 'Cotopaxi', 'Chimborazo', 'Imbabura',
+       'Santo Domingo de los Tsachilas', 'Bolivar', 'Pastaza',
+       'Tungurahua', 'Guayas', 'Santa Elena', 'Los Rios', 'Azuay', 'Loja',
+       'El Oro', 'Esmeraldas', 'Manabi'])
+with col2:    
+    input_data['store_type'] = st.selectbox("store_type",['D', 'C', 'B', 'E', 'A'])
+    input_data['cluster'] = st.number_input("cluster",step=1)
+    input_data['dcoilwtico'] = st.number_input("dcoilwtico",step=1)
+    input_data['year'] = st.number_input("year",step=1)
+with col3:    
+    input_data['month'] = st.slider("month",1,12)
+    input_data['day'] = st.slider("day",1,31)
+    input_data['dayofweek'] = st.number_input("dayofweek,0=Sun and 6=Sat",step=1)
+    input_data['end_month'] = st.selectbox("end_month",['True','False'])
+
+
+  # Create a button to make a prediction
+    
+if st.button("Predict"):
+    # 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])
+
+        
+ # Encode the categorical columns
+        input_encoded_df = pd.DataFrame(encoder.transform(input_df_imputed_cat).toarray(),
+                                   columns=encoder.get_feature_names(cat_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)
+
+#joining the cat encoded and num scaled
+        final_df = pd.concat([input_encoded_df, input_scaled_df], axis=1)
+
+# Make a prediction
+        prediction =dt_model.predict(final_df)[0]
+
+        
+# Display the prediction
+        st.write(f"The predicted sales are: {prediction}.")
+        input_df.to_csv("data.csv", index=False)
+        st.table(input_df)
+
+
+
+

dt_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4c48c2b830b10599782588fb9850ab1fffc099a4af0bf970e341cfa6710b1f81
+size 130142917

requirements.txt

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+streamlit ==1.24.1
+pandas ==2.0.3
+numpy ==1.25.1
+matplotlib ==3.7.2
+scikit-learn ==1.2.2
+pickleshare==0.7.5
+cycler == 0.11.0
+decorator == 5.1.1