File size: 3,179 Bytes
766645e 24a44b0 766645e 24a44b0 766645e 24a44b0 766645e 24a44b0 766645e 24a44b0 | 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 | import streamlit as st
import requests
# --- Streamlit UI config ---
st.set_page_config(page_title="SuperKart Sales Prediction", layout="centered")
st.title("🛒 SuperKart Sales Prediction")
st.write("Enter product and store features below to get a sales forecast.")
# --- INPUT FIELDS ---
product_weight = st.number_input("Product Weight (kg)", min_value=0.0, step=0.1, value=12.0)
product_sugar = st.selectbox("Product Sugar Content", [0, 1])
product_alloc_area = st.number_input("Allocated Display Area (sq. m)", min_value=0.0, step=0.01, value=0.05)
product_mrp = st.number_input("Product MRP", min_value=1.0, step=0.5, value=150.0)
store_size = st.selectbox("Store Size", [0, 1, 2])
store_city_type = st.selectbox("Store Location City Type", [0, 1, 2])
store_type = st.selectbox("Store Type", [0, 1, 2, 3])
store_age = st.slider("Store Age (Years)", 0, 30, 10)
product_type = st.selectbox("Product Category", [
"Breads", "Breakfast", "Canned", "Dairy", "Frozen Foods", "Fruits and Vegetables",
"Hard Drinks", "Health and Hygiene", "Household", "Meat", "Others", "Seafood",
"Snack Foods", "Soft Drinks", "Starchy Foods"
])
# --- One-hot encode the product type ---
product_type_features = {
f"Product_Type_{pt}": int(pt == product_type)
for pt in [
"Breads", "Breakfast", "Canned", "Dairy", "Frozen Foods", "Fruits and Vegetables",
"Hard Drinks", "Health and Hygiene", "Household", "Meat", "Others", "Seafood",
"Snack Foods", "Soft Drinks", "Starchy Foods"
]
}
# --- Create input JSON ---
input_data = {
"Product_Weight": product_weight,
"Product_Sugar_Content": product_sugar,
"Product_Allocated_Area": product_alloc_area,
"Product_MRP": product_mrp,
"Store_Size": store_size,
"Store_Location_City_Type": store_city_type,
"Store_Type": store_type,
"Store_Age": store_age,
**product_type_features
}
if st.button("Predict Sales"):
with st.spinner("Fetching prediction from backend..."):
try:
response = requests.post(
"https://lokiiparihar-SuperkartBackendModalDeploy-XGBoost.hf.space/predict",
json=input_data
)
if response.status_code == 200:
try:
result = response.json()
st.subheader("Raw Backend Response")
#st.json(result) # SHOW FULL JSON RETURNED
prediction = result.get("Predicted_Sales", None)
except ValueError:
prediction = response.text
st.warning("⚠ Backend did not return JSON, showing raw text:")
st.code(prediction)
try:
prediction = float(prediction)
st.success(f"Predicted Sales: **{prediction:.2f} units**")
except (ValueError, TypeError):
st.error(f" Could not convert prediction to number: {prediction}")
else:
st.error(f"API Error: Status code {response.status_code}")
st.text(response.text)
except Exception as e:
st.error(f" Request failed: {e}") |