Updated app.py

Dockerfile

@@ -10,4 +10,4 @@ COPY . .
 ENV HF_MODEL_REPO="manoj112025/SuperKartSalesModel"
 EXPOSE 7860
 
-CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
+CMD ["streamlit", "run", "deployment/app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

@@ -1,35 +0,0 @@
-import streamlit as st
-import pandas as pd
-from predict import predict_one
-
-st.set_page_config(page_title="SuperKart Sales Predictor", page_icon="🛒", layout="centered")
-st.title("🛒 SuperKart Sales Prediction App")
-
-st.markdown("Enter product & store details to predict **Product_Store_Sales_Total**")
-
-# Input UI
-inputs = {
-    "Product_Weight": st.number_input("Product Weight", min_value=0.0, value=12.0, step=0.1),
-    "Product_Sugar_Content": st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"]),
-    "Product_Allocated_Area": st.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, value=0.05),
-    "Product_Type": st.selectbox("Product Type", [
-        "Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene", "Snack Foods",
-        "Meat", "Household", "Hard Drinks", "Fruits and Vegetables", "Breads",
-        "Breakfast", "Seafood", "Starchy Foods", "Soft Drinks", "Others",
-        "Food Mart", "Departmental Store", "Supermarket Type1", "Supermarket Type2"
-    ]),
-    "Product_MRP": st.number_input("Product MRP", min_value=0.0, value=150.0),
-    "Store_Id": st.selectbox("Store ID", ["OUT001", "OUT002", "OUT003", "OUT004"]),
-    "Store_Establishment_Year": st.number_input("Store Establishment Year", value=2000, step=1),
-    "Store_Size": st.selectbox("Store Size", ["Small", "Medium", "High"]),
-    "Store_Location_City_Type": st.selectbox("Store City Type", ["Tier 1", "Tier 2", "Tier 3"]),
-    "Store_Type": st.selectbox("Store Type", ["Departmental Store", "Supermarket Type1", "Supermarket Type2", "Food Mart"])
-}
-
-if st.button("Predict Sales"):
-    df = pd.DataFrame([inputs])
-    try:
-        prediction = predict_one(df)
-        st.success(f"Predicted Sales: **{float(prediction[0]):,.2f}**")
-    except Exception as e:
-        st.error(f"Prediction Failed: {e}")

deployment/app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import pandas as pd
-from predict import predict_one
+from deployment.predict import predict_one
 
 st.set_page_config(page_title="SuperKart Sales Predictor", page_icon="🛒", layout="centered")
 st.title("🛒 SuperKart Sales Prediction App")

predict.py

@@ -1,23 +0,0 @@
-import os
-import joblib
-import pandas as pd
-from huggingface_hub import hf_hub_download
-
-MODEL_REPO = os.getenv("HF_MODEL_REPO", "manoj112025/SuperKartSalesModel")
-MODEL_FILE = "model.joblib"
-PREPROCESSOR_FILE = "preprocessor.joblib"
-
-def load_artifacts():
-    model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE, repo_type="model")
-    pre_path = hf_hub_download(repo_id=MODEL_REPO, filename=PREPROCESSOR_FILE, repo_type="model")
-
-    model = joblib.load(model_path)
-    preprocessor = joblib.load(pre_path)
-
-    return preprocessor, model
-
-def predict_one(df: pd.DataFrame):
-    preprocessor, model = load_artifacts()
-    X = preprocessor.transform(df)
-    y = model.predict(X)
-    return y