src/streamlit_app.py

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}")