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e7404b9 d76bedd e7404b9 | 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 | import streamlit as st
import requests
import pandas as pd
st.set_page_config(page_title="SuperKart Sales Forecaster", page_icon="🛒", layout="wide")
# Backend API URL pointing to deployed Flask backend
BACKEND_URL = "https://codedfortamara-superkart-backend.hf.space/predict"
st.title("🛒 SuperKart Sales Revenue Forecaster")
st.markdown("Predict sales revenue for any product-store combination using our trained ML model.")
st.divider()
col1, col2 = st.columns(2)
with col1:
st.subheader("📦 Product Details")
product_weight = st.number_input("Product Weight (kg)", min_value=1.0, max_value=25.0, value=12.0, step=0.1)
product_sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
product_allocated_area = st.slider("Allocated Display Area (ratio)", min_value=0.0, max_value=0.30, value=0.05, step=0.005, format="%.3f")
product_type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy",
"Household", "Baking Goods", "Canned", "Health and Hygiene", "Meat", "Soft Drinks",
"Breads", "Hard Drinks", "Others", "Starchy Foods", "Breakfast", "Seafood"])
product_mrp = st.number_input("Product MRP", min_value=30.0, max_value=270.0, value=140.0, step=1.0)
product_category = st.selectbox("Product Category", ["FD", "NC", "DR"],
format_func=lambda x: {"FD": "FD – Food", "NC": "NC – Non-Consumable", "DR": "DR – Drinks"}[x])
with col2:
st.subheader("🏬 Store Details")
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location = st.selectbox("City Tier", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
store_age = st.number_input("Store Age (years)", min_value=1, max_value=50, value=20, step=1)
st.divider()
if st.button("🔮 Predict Sales Revenue", type="primary", use_container_width=True):
payload = {
"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,
"Store_Type": store_type,
"Store_Age": store_age,
"Product_Category": product_category,
}
try:
response = requests.post(BACKEND_URL, json=payload, timeout=30)
if response.status_code == 200:
prediction = response.json()["predictions"][0]
st.success(f"### 💰 Predicted Sales Revenue: ₹{prediction:,.2f}")
st.markdown("#### Input Summary")
st.dataframe(pd.DataFrame([payload]), use_container_width=True)
else:
st.error(f"API Error: {response.json().get('error', 'Unknown error')}")
except requests.exceptions.ConnectionError:
st.error("Could not connect to the backend. Check if the backend space is running.")
except Exception as e:
st.error(f"Error: {str(e)}")
st.divider()
st.subheader("📊 Batch Prediction")
uploaded_file = st.file_uploader("Upload CSV for batch predictions", type=["csv"])
if uploaded_file is not None:
batch_df = pd.read_csv(uploaded_file)
st.dataframe(batch_df.head(), use_container_width=True)
if st.button("Run Batch Prediction", use_container_width=True):
try:
response = requests.post(BACKEND_URL, json=batch_df.to_dict(orient='records'), timeout=60)
if response.status_code == 200:
batch_df["Predicted_Sales"] = response.json()["predictions"]
st.success(f"Predictions generated for {len(batch_df)} records!")
st.dataframe(batch_df, use_container_width=True)
st.download_button("Download Predictions", batch_df.to_csv(index=False),
"superkart_predictions.csv", "text/csv")
else:
st.error(f"API Error: {response.json().get('error', 'Unknown error')}")
except Exception as e:
st.error(f"Error: {str(e)}")
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