Upload 3 files

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+
+with open('model_iterative.pkl', 'rb') as model_file:
+    model = joblib.load(model_file)
+
+def predict_total_order(week_of_month, day_of_week, order_type_a, order_type_b, order_type_c):
+
+    input_data = {
+        'Week of the month': week_of_month,
+        'Day of the week': day_of_week,
+        'Order type A': order_type_a,
+        'Order type B': order_type_b,
+        'Order type C': order_type_c,
+    }
+
+    input_df = pd.DataFrame([input_data])
+    total_order_prediction = model.predict(input_df)
+    return total_order_prediction
+
+def run():
+    st.title('Total Order Prediction')
+    st.subheader('Input Features')
+
+    week_of_month = st.number_input('Week of the month', min_value=0, max_value=4, step=1)
+    day_of_week = st.number_input('Day of the week', min_value=0, max_value=7, step=1)
+    order_type_a = st.number_input('Order type A', value=0.0)
+    order_type_b = st.number_input('Order type B', value=0.0)
+    order_type_c = st.number_input('Order type C', value=0.0)
+
+    if st.button('Predict Total Order'):
+        total_order_prediction = predict_total_order(week_of_month, day_of_week, order_type_a, order_type_b, order_type_c)
+        st.success(f'Predicted Total Order: {total_order_prediction[0]:.2f}')
+
+if __name__ == '__main__':
+    run()

model_iterative.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:22bf91838360774db64478c9ea3524d34e6919e44e1cfc02b69d9a84658e9351
+size 1008

requirements.txt

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+streamlit
+pandas
+scikit-learn