Update app.py

app.py

@@ -1,23 +1,56 @@
-import joblib
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import joblib
 from huggingface_hub import hf_hub_download
 
-MODEL_REPO = "atsh2846/supercart-model"   
-MODEL_FILE = "superkart_model.joblib"     
+st.set_page_config(page_title="SuperKart Sales Predictor", layout="centered")
+st.title("SuperKart Sales Prediction")
+st.write("Predict **Product_Store_Sales_Total** using the trained SuperKart model hosted on Hugging Face.")
+
+MODEL_REPO = "atsh2846/superkart_model"
+MODEL_FILE = "superkart_model.joblib"
+
+@st.cache_resource
+def load_model():
+    model_path = hf_hub_download(
+        repo_id=MODEL_REPO,
+        filename=MODEL_FILE,
+        repo_type="model"
+    )
+    return joblib.load(model_path)
 
-st.title("SuperKart Sales Forecast")
+model = load_model()
 
-model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE, repo_type="model")
-model = joblib.load(model_path)
+st.subheader("Enter product & store details")
 
-st.success("Model loaded successfully")
+# Inputs (match your dataset columns)
+Product_Id = st.text_input("Product_Id", value="NC6218")
+Product_Weight = st.number_input("Product_Weight", value=10.0)
+Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar"])
+Product_Allocated_Area = st.number_input("Product_Allocated_Area", value=1200.0)
+Product_Type = st.text_input("Product_Type", value="Snack Foods")
+Product_MRP = st.number_input("Product_MRP", value=150.0)
 
-st.write("Upload a CSV of numeric feature columns (same columns used in training).")
+Store_Id = st.text_input("Store_Id", value="OUT049")
+Store_Establishment_Year = st.number_input("Store_Establishment_Year", value=2005)
+Store_Size = st.selectbox("Store_Size", ["Small", "Medium", "High"])
+Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])
+Store_Type = st.selectbox("Store_Type", ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"])
 
-uploaded = st.file_uploader("Upload CSV", type=["csv"])
-if uploaded is not None:
-    X = pd.read_csv(uploaded)
-    preds = model.predict(X)
-    st.write("Predictions (first 10):")
-    st.write(preds[:10])
+if st.button("Predict Sales"):
+    row = {
+        "Product_Id": Product_Id,
+        "Product_Weight": Product_Weight,
+        "Product_Sugar_Content": Product_Sugar_Content,
+        "Product_Allocated_Area": Product_Allocated_Area,
+        "Product_Type": Product_Type,
+        "Product_MRP": Product_MRP,
+        "Store_Id": Store_Id,
+        "Store_Establishment_Year": Store_Establishment_Year,
+        "Store_Size": Store_Size,
+        "Store_Location_City_Type": Store_Location_City_Type,
+        "Store_Type": Store_Type,
+    }
+    X = pd.DataFrame([row])
+    pred = model.predict(X)[0]
+    st.success(f"Predicted Product_Store_Sales_Total: {pred:.2f}")