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50549b7 1772671 50549b7 9c2d4d3 50549b7 9c2d4d3 50549b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | %%writefile frontend_files/app.py
import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("SuperKart Product Sales Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for product/store features
Product_Weight = st.number_input("Product Weight (kg)", min_value=0.0, value=12.5)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular", "reg"])
Product_Allocated_Area = st.number_input("Allocated Shelf Area", min_value=0.0, value=0.05)
Product_Type = st.selectbox("Product Type", [
"Fruits and Vegetables", "Dairy", "Canned", "Baking Goods",
"Snack Foods", "Health and Hygiene", "Household", "Frozen Foods", "Meat", "Soft Drinks", "Breads", "Hard Drinks", "Starchy Foods", "Breakfast", "Seafood"
])
Product_MRP = st.number_input("Product MRP (₹)", min_value=0.0, value=150.0)
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
Store_Location_City_Type = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Food Mart", "Departmental Store"])
Store_Establishment_Year = st.slider("Store Establishment Year", min_value=1987, max_value=2025, value=2005)
Store_Age = 2025 - Store_Establishment_Year
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'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,
'Store_Age': Store_Age
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post(
"https://PStark-SuperKartSalesPrediction-backend.hf.space/v1/sales",
json=input_data.to_dict(orient='records')[0]
)
if response.status_code == 200:
prediction = response.json()['Predicted Sales (₹)']
st.success(f"🧾 Predicted Product Sales: ₹{prediction}")
else:
st.error("Error making prediction.")
# Section for batch prediction
st.subheader("Batch Prediction")
# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload a CSV file for batch sales prediction", type=["csv"])
if uploaded_file is not None:
if st.button("Predict Batch"):
response = requests.post(
"https://PStark-SuperKartSalesPrediction.hf.space/v1/salesbatch",
files={"file": uploaded_file}
)
if response.status_code == 200:
predictions = response.json()
st.success("Batch predictions completed!")
st.write(predictions)
else:
st.error("Error making batch prediction.")
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