Add inference.py

inference.py

@@ -0,0 +1,35 @@
+import joblib
+import numpy as np
+import requests
+from io import BytesIO
+
+# urls to both model and scaler
+model_url = "https://huggingface.co/mahaqj/ml_assignment_3/resolve/main/best_model.joblib"
+scaler_url = "https://huggingface.co/mahaqj/ml_assignment_3/resolve/main/scaler.joblib"
+
+# download and load model
+model_bytes = BytesIO(requests.get(model_url).content)
+model = joblib.load(model_bytes)
+
+# download and load scaler
+scaler_bytes = BytesIO(requests.get(scaler_url).content)
+scaler = joblib.load(scaler_bytes)
+
+# feature names
+features = ["MedInc", "HouseAge", "AveRooms", "AveBedrms", "Population", "AveOccup", "Latitude", "Longitude"]
+
+# collect user input
+print("Enter feature values!")
+user_input = []
+for feature in features:
+    val = float(input(f"Enter value for {feature}: "))
+    user_input.append(val)
+
+# convert to array and scale
+user_input = np.array(user_input).reshape(1, -1)
+user_input_scaled = scaler.transform(user_input)
+
+# predict and display
+prediction = model.predict(user_input_scaled)[0]
+predicted_price = prediction * 100000  # target is in 100000s (hundreds of thousands of dollars)
+print(f"\nPredicted median house value: ${predicted_price:,.5f}")