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import requests
import streamlit as st
import pandas as pd
st.title("SuperKart Sales Prediction")
# Batch Prediction
st.subheader("Online Prediction")
# Input fields for product data
Product_Id = st.text_input("Product_Id")
Product_Weight = st.number_input("Product_Weight (Product's weight in KG)", min_value=0.0, max_value=900.0, value=2.0)
Product_Sugar_Content = st.selectbox("Product_Sugar_Content (Product's sugar content)", ["No Sugar", "Low Sugar", "Regular"])
Product_Allocated_Area = st.number_input("Product_Allocated_Area (Fraction of total store area allocated to this product)", min_value=0.0, max_value=1.0, value=0.29)
product_type_values = [
'Meat',
'Snack Foods',
'Hard Drinks',
'Dairy',
'Canned',
'Soft Drinks',
'Health and Hygiene',
'Baking Goods',
'Bread',
'Breakfast',
'Frozen Foods',
'Fruits and Vegetables',
'Household',
'Seafood',
'Starchy Foods',
'Others'
]
Product_Type = st.selectbox("Product_Type (Type of the product)", product_type_values)
Product_MRP = st.number_input("Product_MRP (Max Retail Price of the product in dollars)", min_value=0.0, value=119.0)
Store_Size = st.selectbox("Store_Size (Size category of the store)", ["Small","Medium","High"])
Store_Location_City_Type = st.selectbox("Store_Location_City_Type(Type of city in which store is located)", ["Tier 3", "Tier 2", "Tier 1"])
store_type_values = [
'Departmental Store',
'Supermarket Type1',
'Supermarket Type2',
'Food Mart'
]
Store_Type = st.selectbox("Store_Type (Type of store depending on the products that are being sold there)", store_type_values)
product_data = {
'Product_Id': Product_Id,
'Product_Weight': Product_Weight,
'Product_Sugar_Content': Product_Sugar_Content,
'Product_Allocated_Area': Product_Allocated_Area,
'Product_Type': Product_Type,
'Product_MRP': Product_MRP,
'Store_Size': Store_Size,
'Store_Location_City_Type': Store_Location_City_Type,
'Store_Type': Store_Type
}
if st.button("Predict", type='primary'):
response = requests.post("https://AdarshRL-SalesPredictionBackend.hf.space/v1/product", json=product_data) # enter user name and space name before running the cell
if response.status_code == 200:
result = response.json()
prediction = result["Prediction"]["Sales"] # Extract only the value
st.write(f"Based on the information provided, the product with ID {Product_Id} is likely to generate sales of: {prediction}.")
else:
st.error("Error in API request")
# Batch Prediction
st.subheader("Batch Prediction")
file = st.file_uploader("Upload CSV file", type=["csv"])
if file is not None:
if st.button("Predict for Batch", type='primary'):
response = requests.post("https://AdarshRL-SalesPredictionBackend.hf.space/v1/productbatch", files={"file": file}) # enter user name and space name before running the cell
if response.status_code == 200:
result = response.json()
st.header("Batch Prediction Results")
st.write(result)
else:
st.error("Error in API request")
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