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import streamlit as st
import requests
API_URL = "https://lokiiparihar-superkart-api.hf.space/predict" # use the working API
st.set_page_config(page_title="Superkart Sales Prediction", layout="centered")
st.title("Sales Prediction App")
st.write("This tool predicts Superkart sales. Enter the required information below.")
# Model Choice
model_choice = st.selectbox(
"Select Model",
options=["dt", "xgb"],
format_func=lambda x: {
"dt": "Decision Tree",
"xgb": "XGBoost",
"rf": "Random Forest",
"lr": "Linear Regression",
}.get(x, x),
)
# Inputs (set defaults so the API call has valid values)
col1, col2 = st.columns(2)
with col1:
product_weight = st.number_input("Product Weight", min_value=0.0, value=12.5, step=0.1)
sugar = st.selectbox("Sugar Content", [0, 1, 2], index=0)
area = st.number_input("Allocated Area", min_value=0.0, value=0.08, step=0.01)
product_type = st.number_input("Product Type Code", min_value=0, value=0, step=1)
with col2:
mrp = st.number_input("Product MRP", min_value=0.0, value=249.99, step=1.0)
store_size = st.selectbox("Store Size Code", [0, 1, 2], index=1)
city = st.selectbox("City Type Code", [0, 1, 2], index=0)
store_type = st.number_input("Store Type Code", min_value=0, value=1, step=1)
store_age = st.number_input("Store Age", min_value=0, value=15, step=1)
# Build payload EXACTLY as your working notebook request expects
sample = {
"Product_Weight": float(product_weight),
"Product_Sugar_Content": float(sugar),
"Product_Allocated_Area": float(area),
"Product_Type": int(product_type),
"Product_MRP": float(mrp),
"Store_Size": int(store_size),
"Store_Location_City_Type": int(city),
"Store_Type": int(store_type),
"Store_Age": int(store_age),
"model": model_choice,
}
st.subheader("Payload being sent")
st.json(sample)
if st.button("Predict", type="primary"):
try:
headers = {"Content-Type": "application/json"}
with st.spinner("Calling prediction API..."):
response = requests.post(API_URL, json=sample, headers=headers, timeout=30)
st.write("Status Code:", response.status_code)
# Show raw response for debugging
st.write("Raw Response:")
st.code(response.text)
if response.headers.get("content-type", "").startswith("application/json"):
result = response.json()
st.write("Parsed JSON:")
st.json(result)
# Try common keys (your API might return a different one)
pred_key = next((k for k in ["Prediction", "prediction", "pred", "result", "output"] if k in result), None)
if pred_key:
st.success(f"Prediction: {result[pred_key]}")
else:
st.info("Prediction key not found. See JSON above.")
else:
st.error("API did not return JSON. See raw response above.")
except requests.exceptions.RequestException as e:
st.error(f"Request failed: {e}")
# IMPORTANT:
# Streamlit apps do NOT use app.run(). Remove any Flask-related code.
# if __name__ == '__main__':
# app.run(debug=True) |