Update app.py

app.py

@@ -1,21 +1,69 @@
-import flask
-from flask import Flask, request, jsonify
+# Import necessary libraries
+import os
 import joblib
 import pandas as pd
-import numpy as np
+from flask import Flask, request, jsonify
+
+# Initialize Flask app
+superkart_api = Flask("SuperKart")
+
+# Global model variable (lazy loading)
+model = None
+MODEL_PATH = "superkart_model.joblib"
+
+
+def load_model():
+    """
+    Load the model only once when first needed.
+    Prevents Hugging Face startup crashes.
+    """
+    global model
+    if model is None:
+        if not os.path.exists(MODEL_PATH):
+            raise FileNotFoundError(f"Model file not found: {MODEL_PATH}")
+        model = joblib.load(MODEL_PATH)
+
+
+# Health check endpoint (IMPORTANT for Hugging Face Spaces)
+@superkart_api.get("/")
+def home():
+    return "SuperKart API is running"
+
+
+# Prediction endpoint
+@superkart_api.post("/v1/predict")
+def predict_sales():
+    try:
+        load_model()
+
+        data = request.get_json(force=True)
+
+        # Enforce correct feature schema
+        sample = {
+            "Product_Weight": data["Product_Weight"],
+            "Product_Sugar_Content": data["Product_Sugar_Content"],
+            "Product_Allocated_Area": data["Product_Allocated_Area"],
+            "Product_MRP": data["Product_MRP"],
+            "Store_Size": data["Store_Size"],
+            "Store_Location_City_Type": data["Store_Location_City_Type"],
+            "Store_Type": data["Store_Type"],
+            "Product_Id_char": data["Product_Id_char"],
+            "Store_Age_Years": data["Store_Age_Years"],
+            "Product_Type_Category": data["Product_Type_Category"],
+        }
+
+        input_df = pd.DataFrame([sample])
 
-app = Flask(__name__)
+        prediction = model.predict(input_df)[0]
 
-model = joblib.load("superkart_model.joblib")
+        return jsonify({"Sales": float(prediction)})
 
-@app.route("/")
-def health():
-    return "OK"
+    except KeyError as e:
+        return jsonify({"error": f"Missing field: {str(e)}"}), 400
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
 
-@app.route("/v1/predict", methods=["POST"])
-def predict():
-    data = request.get_json()
-    df = pd.DataFrame([data])
-    return jsonify({"Sales": model.predict(df).tolist()[0]})
 
-app.run(host="0.0.0.0", port=7860)
+# Run app (Hugging Face requires port 7860)
+if __name__ == "__main__":
+    superkart_api.run(host="0.0.0.0", port=7860)