File size: 3,366 Bytes
8990fc3 9b18b71 8990fc3 c412bca 8990fc3 cecb4ce 8990fc3 8e2fdb8 8990fc3 cecb4ce 8990fc3 8e2fdb8 3f877c6 | 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 71 72 73 74 75 76 | import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("SuperKart Sales Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for product and store features
product_type = st.selectbox("Product Type", [
"Dairy", "Soft Drinks", "Meat", "Fruits and Vegetables", "Household",
"Baking Goods", "Snack Foods", "Frozen Foods", "Breakfast", "Health and Hygiene",
"Hard Drinks", "Others", "Seafood", "Starchy Foods", "Canned", "Breads"
])
store_id = st.selectbox("Store ID", ["OUT004", "OUT001", "OUT003", "OUT002"]) # Update with actual store IDs
product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
product_weight = st.number_input("Product Weight (grams)", min_value=0.0, step=0.1, value=500.0)
product_mrp = st.number_input("Product MRP (₹)", min_value=0.0, step=0.1, value=100.0)
product_allocated_area = st.number_input("Allocated Shelf Area (sq cm)", min_value=0.0, step=1.0, value=100.0)
store_type = st.selectbox("Store Type", ["Supermarket Type2", "Supermarket Type1", "Departmental Store", "Food Mart"])
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location_city_type = st.selectbox("Store Location", ["Tier 2", "Tier 1", "Tier 3"])
store_establishment_year = st.number_input("Year of Store Establishment", min_value=1900, max_value=2025, step=1, value=2015)
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'Product_Type': product_type,
'Product_Sugar_Content': product_sugar_content,
'Product_Weight': product_weight,
'Product_MRP': product_mrp,
'Product_Allocated_Area': product_allocated_area,
'Store_Id': store_id,
'Store_Type': store_type,
'Store_Size': store_size,
'Store_Location_City_Type': store_location_city_type,
'Store_Establishment_Year': store_establishment_year
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post("https://Swetha2031-SuperKartSalesPredictionBackend.hf.space/v1/salestotal", 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 CSV file for batch prediction", type=["csv"])
if st.button("Predict Batch"):
response = requests.post("https://Swetha2031-SuperKartSalesPredictionBackend.hf.space/v1/salesbatch", files={"file": uploaded_file})
if response.status_code == 200:
predictions = response.json()
# Convert dictionary to DataFrame
df_predictions = pd.DataFrame(list(predictions.items()), columns=["Store ID", "Predicted Sales (₹)"])
# Optional: check for overflow
if df_predictions["Predicted Sales (₹)"].gt(1e10).any():
st.warning("Some predictions are extremely large. This may indicate overflow or input scaling issues.")
st.success("Batch predictions completed!")
st.write(df_predictions)
else:
st.error("Error making batch prediction.")
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