Replace default demo with SuperKart UI

streamlit_app.py

@@ -0,0 +1,78 @@
+import os, json, requests, streamlit as st
+API_URL = os.getenv("API_URL","https://angadsi-superkart-sales-forecast-api.hf.space/predict")
+
+st.set_page_config(page_title="SuperKart Sales Forecast", page_icon="🛒", layout="centered")
+st.title("🛒 SuperKart Sales Forecast")
+st.caption("Backend: Flask on HF Spaces • Frontend: Streamlit")
+
+with st.form("predict_form"):
+    c1, c2 = st.columns(2)
+    with c1:
+        product_id = st.text_input("Product_Id", "ME1234")
+        product_weight = st.number_input("Product_Weight", 0.0, value=0.75, step=0.01)
+        sugar = st.selectbox("Product_Sugar_Content", ["low sugar","regular","no sugar"], index=1)
+        alloc_area = st.number_input("Product_Allocated_Area (0-1)", 0.0, 1.0, value=0.012, step=0.001)
+        product_type = st.selectbox("Product_Type", [
+            "meat","snack foods","hard drinks","dairy","canned","soft drinks","health and hygiene",
+            "baking goods","bread","breakfast","frozen foods","fruits and vegetables","household",
+            "seafood","starchy foods","others"
+        ])
+    with c2:
+        mrp = st.number_input("Product_MRP", 0.0, value=199.0, step=1.0)
+        store_id = st.text_input("Store_Id", "S104")
+        est_year = st.number_input("Store_Establishment_Year", 1900, 2100, value=2012, step=1)
+        store_size = st.selectbox("Store_Size", ["High","Medium","Low"])
+        city_tier = st.selectbox("Store_Location_City_Type", ["Tier 1","Tier 2","Tier 3"])
+        store_type = st.selectbox("Store_Type", ["Departmental Store","Supermarket Type 1","Supermarket Type 2","Food Mart"])
+    submitted = st.form_submit_button("Predict")
+
+st.markdown("### Or paste JSON for batch prediction")
+default_batch = [
+    {"Product_Id":"ME1234","Product_Weight":0.75,"Product_Sugar_Content":"regular",
+     "Product_Allocated_Area":0.012,"Product_Type":"meat","Product_MRP":199.0,
+     "Store_Id":"S104","Store_Establishment_Year":2012,"Store_Size":"High",
+     "Store_Location_City_Type":"Tier 1","Store_Type":"Supermarket Type 1"}
+]
+raw = st.text_area("JSON list (optional)", value=json.dumps(default_batch, indent=2), height=180)
+go_batch = st.button("Predict (batch JSON)")
+
+def call_api(payload):
+    r = requests.post(API_URL, json=payload, timeout=30)
+    try:
+        return r.status_code, r.json()
+    except Exception:
+        return r.status_code, {"error": r.text[:300]}
+
+if submitted:
+    payload = {
+        "Product_Id": product_id,
+        "Product_Weight": float(product_weight),
+        "Product_Sugar_Content": sugar,
+        "Product_Allocated_Area": float(alloc_area),
+        "Product_Type": product_type,
+        "Product_MRP": float(mrp),
+        "Store_Id": store_id,
+        "Store_Establishment_Year": int(est_year),
+        "Store_Size": store_size,
+        "Store_Location_City_Type": city_tier,
+        "Store_Type": store_type
+    }
+    with st.spinner("Calling backend..."):
+        code, resp = call_api(payload)
+    if code == 200 and "predictions" in resp:
+        st.success(f"Prediction: **{resp['predictions'][0]:,.2f}**")
+    else:
+        st.error(f"{code}: {resp}")
+
+if go_batch:
+    try:
+        payload = json.loads(raw)
+        with st.spinner("Calling backend..."):
+            code, resp = call_api(payload)
+        if code == 200 and "predictions" in resp:
+            st.success("Predictions:")
+            st.write(resp["predictions"])
+        else:
+            st.error(f"{code}: {resp}")
+    except Exception as e:
+        st.error(f"Invalid JSON: {e}")