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import streamlit as st
import pandas as pd
import joblib
import requests
# Streamlit UI for Product Sales Prediction
st.title("Product Sales Prediction")
st.subheader("Please fill details below for sales prediction")
# Collect user input
ProductWeight = st.number_input("Product Weight (weight of the product)", min_value=4.0, max_value=22.0, value=10.0)
ProductSugarContent = st.selectbox("Product Sugar Content(sugar content of the product)", ["Low Sugar", "Regular", "No sugar"])
ProductAllocatedArea = st.number_input("Product Allocated Area (ratio of the allocated display area of each product to the total display area )", min_value=0.00, max_value=0.29, value=0.06)
ProductType = st.selectbox("Product Type", ['Baking Goods', 'Breads', 'Breakfast', 'Canned',
'Dairy', 'Frozen Food', 'Fruits and Vegetables',
'Hard Drinks', 'Health and Hygiene', 'Household',
'Meat', 'Others', 'Seafood', 'Snack foods',
'Soft Drinks', 'Starchy Foods'])
ProductMRP = st.number_input("Product MRP(maximum retail price of each product)", min_value=31.0, max_value=266.0, value=120.0)
StoreID = st.selectbox("Store Id",['OUT001', 'OUT002', 'OUT003', 'OUT004'] )
StoreEstablishmentYear = st.number_input("Store Establishment Year (the year in which the store was established)", min_value=1987, max_value=2009, value=2000)
StoreSize= st.selectbox("Store Size", ['Small','Medium', 'High'])
StoreLocationCityType = st.selectbox("Store Location City Type", ['Tier 1', 'Tier 2', 'Tier 3'])
StoreType = st.selectbox("Store Type",['Departmental Store', 'Food Mart', 'Supermarket Type 1', 'Supermarket Type 2'])
# Convert categorical inputs to match model training
input_data = pd.DataFrame([{
'Product_Weight': ProductWeight,
'Product_Sugar_Content': ProductSugarContent,
'Product_Allocated_Area': ProductAllocatedArea,
'Product_Type': ProductType,
'Product_MRP': ProductMRP,
'Store_Id': StoreID,
'Store_Establishment_Year': StoreEstablishmentYear,
'Store_Size': StoreSize,
'Store_Location_City_Type': StoreLocationCityType,
'Store_Type': StoreType
}])
if st.button("Predict", type='primary'):
headers = {'Content-Type': 'application/json'}
response = requests.post("https://csankaran3-backend.hf.space/v1/product", json=input_data.to_dict(orient='records')[0],headers=headers) # enter user name and space name before running the cell
if response.status_code == 200:
result = response.json()
predicted_sales = result['Predicted Sales'] # Extract only the value
st.success(f"Sales predictions completed!.. The predicted product sale is {predicted_sales}.")
else:
st.error("Error in API request")
# Section for batch prediction
st.subheader("Batch Prediction")
# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload CSV file for batch sales prediction", type=["csv"])
# Make batch prediction when the "Predict Batch" button is clicked
if uploaded_file is not None:
if st.button("Predict Batch", type='primary'):
response = requests.post("https://csankaran3-backend.hf.space/v1/productbatch", files={"file": uploaded_file}) # Send file to Flask API
if response.status_code == 200:
predictions = response.json()
st.success("Batch predictions completed!")
st.write(predictions) # Display the predictions
else:
st.error("Error making batch prediction.")
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