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23af5ff | 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 77 78 79 80 81 82 83 84 85 86 87 88 | import streamlit as st
import requests
import json
# Page configuration
st.set_page_config(
page_title="SuperKart Sales Predictor",
page_icon="🛒",
layout="centered"
)
# Debug: Print to logs
print("Streamlit app starting...")
# Title and description
st.title("🛒 SuperKart Sales Predictor")
st.markdown("Predict product sales using your tuned Random Forest model. Enter details below!")
# Input fields matching SuperKart dataset
col1, col2 = st.columns(2)
with col1:
st.subheader("Product Information")
product_weight = st.number_input("Product Weight", min_value=0.0, max_value=50.0, value=12.0, step=0.1)
product_mrp = st.number_input("Product MRP ($)", min_value=0.0, max_value=10000.0, value=150.0, step=0.01)
product_sugar = st.selectbox("Product Sugar Content", ['Low Fat', 'Regular', 'Low Sugar', 'LF'])
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', 'Canned', 'Breads',
'Starchy Foods', 'Others'])
with col2:
st.subheader("Store Information")
store_size = st.selectbox("Store Size", ['Small', 'Medium', 'High'])
store_location = st.selectbox("Store Location Type", ['Tier 1', 'Tier 2', 'Tier 3'])
store_type = st.selectbox("Store Type",
['Grocery Store', 'Supermarket Type1', 'Supermarket Type2', 'Supermarket Type3'])
# Prediction button
if st.button("Predict Sales"):
# Prepare data for your backend API
data = {
"Product_Weight": product_weight,
"Product_MRP": product_mrp,
"Product_Sugar_Content": product_sugar,
"Product_Type": product_type,
"Store_Size": store_size,
"Store_Location_City_Type": store_location,
"Store_Type": store_type
}
# Debug: Print data being sent
print(f"Sending data: {data}")
# Call your deployed backend API
# REPLACE YOUR_USERNAME with your actual Hugging Face username
api_url = "https://toddmattingly-superkart-backend.hf.space/predict"
try:
response = requests.post(api_url, json=data, timeout=10)
print(f"API response status: {response.status_code}")
if response.status_code == 200:
# API returns a list directly (based on your testing)
predictions = response.json()
prediction = predictions[0] if isinstance(predictions, list) and len(predictions) > 0 else 0
st.success(f"🎯 Predicted Sales Total: ${prediction:,.2f}")
st.info(f"📊 Based on: {product_type} at ${product_mrp:,.2f} MRP in a {store_type}")
else:
st.error(f"API Error: {response.status_code} - {response.text}")
print(f"API Error: {response.status_code} - {response.text}")
except requests.exceptions.RequestException as e:
st.error(f"Connection Error: {str(e)}")
print(f"Connection Error: {str(e)}")
except Exception as e:
st.error(f"Unexpected Error: {str(e)}")
print(f"Unexpected Error: {str(e)}")
# Footer
st.markdown("---")
st.markdown("*Powered by Streamlit & Hugging Face Spaces*")
st.markdown("*Using your tuned Random Forest model*")
print("Streamlit app loaded successfully.")
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