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import streamlit as st
import pandas as pd
import datetime
import pickle
# Step 1
def extract_week(df):
try:
df['Date'] = pd.to_datetime(df['Date'])
df['Week'] = df['Date'].dt.isocalendar().week
df['Year'] = pd.DatetimeIndex(df['Date']).year
df['Month'] = pd.DatetimeIndex(df['Date']).month
except Exception as e:
st.error(f"Error during date extraction: {str(e)}")
return df
# Step 2
def maping_type(df):
try:
df['Type'] = df['Type'].map({'A': 1, 'B': 2, 'C': 3}) # Convert to integer directly
except Exception as e:
st.error(f"Error during type mapping: {str(e)}")
return df
# Step 3
def convert_to_int(df):
try:
df['Type'] = df['Type'].astype(int)
df['IsHoliday'] = df['IsHoliday'].astype(int)
except Exception as e:
st.error(f"Error during data type conversion: {str(e)}")
return df
# Step 4
def input_col_sel(df):
input_col = ['Store', 'Dept', 'IsHoliday', 'Type', 'Size', 'Temperature',
'Fuel_Price', 'CPI', 'Unemployment', 'Week', 'Year', 'Month',
'Fuel_Price_Cat']
return df[input_col]
def load_model():
try:
# Load the pre-trained model from the pickle file
model = pickle.load(open('model.pkl', 'rb'))
return model
except Exception as e:
st.error(f"Error loading the model: {str(e)}")
return None
def predict_sales(model, input_data):
try:
# Make predictions using the loaded model
# Replace this line with the appropriate prediction logic based on your model
prediction = model.predict(input_data)
return prediction
except Exception as e:
st.error(f"Error during prediction: {str(e)}")
return None
def main():
st.title("Walmart Sales Forecasting App")
# Create input fields for manual input
st.sidebar.header("Inputs")
store = st.sidebar.number_input("Store", min_value=1, max_value=45, step=1, value=1)
dept = st.sidebar.number_input("Dept", min_value=1, max_value=99, step=1, value=1)
is_holiday = st.sidebar.checkbox("Is Holiday")
type_input = st.sidebar.selectbox("Type", ['A', 'B', 'C'])
size = st.sidebar.number_input("Size", min_value=34875, max_value=219622, step=1, value=34875)
temperature = st.sidebar.number_input("Temperature", min_value=-2, max_value=100)
fuel_price = st.sidebar.number_input("Fuel Price", min_value=2.5, max_value=4.4)
cpi = st.sidebar.number_input("CPI", min_value=126, max_value=227)
unemployment = st.sidebar.number_input("Unemployment", min_value=3.879, max_value=14.00, value=3.879)
min_date = datetime.date(2010, 3, 1) # Minimum allowed date
input_date = st.sidebar.date_input("Date", min_value=min_date, value=datetime.date.today())
fuel_price_cat = st.sidebar.number_input("Fuel Price Category", min_value=2.00, max_value=4.25)
# Create a dataframe from manual input
input_data = pd.DataFrame({
'Store': [store],
'Dept': [dept],
'IsHoliday': [int(is_holiday)],
'Type': [type_input],
'Size': [size],
'Temperature': [temperature],
'Fuel_Price': [fuel_price],
'CPI': [cpi],
'Unemployment': [unemployment],
'Date': [input_date], # Use the manually input date
'Fuel_Price_Cat': [fuel_price_cat]
})
# Load the model
model = load_model()
if model is not None and st.button("Predict Sales Amount"):
# Apply the preprocessing steps to the manual input
input_data = extract_week(input_data)
input_data = maping_type(input_data)
input_data = convert_to_int(input_data)
input_data = input_col_sel(input_data)
# Make predictions
prediction = predict_sales(model, input_data)
if prediction is not None:
st.success(f"Prediction: {prediction}")
if __name__ == "__main__":
main()
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