Frontend: clean clone + update Streamlit UI

requirements.txt

@@ -1,4 +1,3 @@
 streamlit==1.29.0
 pandas==2.1.1
-scikit-learn==1.6.1
-joblib==1.3.2
+requests==2.32.3

streamlit_app.py

@@ -0,0 +1,46 @@
+import streamlit as st
+import pandas as pd
+import requests, os
+
+st.set_page_config(page_title="SuperKart Forecast", layout="centered")
+st.title("🛒 SuperKart Quarterly Sales Forecast")
+
+BACKEND_URL = os.getenv(
+    "BACKEND_URL",
+    "https://huggingface.co/spaces/singhina/superkart-forecast"
+)
+
+c1, c2 = st.columns(2)
+with c1:
+    pw  = st.number_input("Product Weight",           0.0,100.0,12.5,0.1)
+    pa  = st.number_input("Allocated Area Ratio",     0.0,  1.0,0.08,0.005)
+    mrp = st.number_input("Product MRP (₹)",          0.0,1000.0,50.0,1.0)
+    year= st.number_input("Store Established Year",1900,2025,2015,1)
+    size= st.selectbox("Store Size", ["low","medium","high"])
+with c2:
+    city = st.selectbox("City Tier", ["Tier 1","Tier 2","Tier 3"])
+    stype= st.selectbox("Store Type", [
+        "Departmental Store","Supermarket Type 1",
+        "Supermarket Type 2","Food Mart"
+    ])
+    prefix=st.text_input("Product Prefix","FD")
+    pnum  =st.number_input("Product Numeric ID", 0,100000,6114,1)
+    age   =st.number_input("Store Age (yrs)",    0,   50,   int(pd.Timestamp.now().year-year),1)
+
+if st.button("🔮 Predict"):
+    payload={"data":[{
+        "Product_Weight":pw,
+        "Product_Allocated_Area":pa,
+        "Product_MRP":mrp,
+        "Store_Establishment_Year":year,
+        "Store_Size":size,
+        "Store_Location_City_Type":city,
+        "Store_Type":stype,
+        "Product_Prefix":prefix,
+        "Product_Num":pnum,
+        "Store_Age":age
+    }]}
+    r = requests.post(f"{BACKEND_URL}/predict", json=payload)
+    r.raise_for_status()
+    pred = r.json()["predictions"][0]
+    st.success(f"🚀 Forecasted Sales: ₹{pred:,.2f}")