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inference.py

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+"""
+Inference script for Linear Regression Text Classification Model
+This script demonstrates how to load and use the trained model for predictions.
+"""
+
+import joblib
+import numpy as np
+from typing import Union, List
+
+
+class TextClassifier:
+    """Wrapper class for the Linear Regression text classification model."""
+    
+    def __init__(self, model_path: str):
+        """
+        Initialize the classifier by loading the model.
+        
+        Args:
+            model_path: Path to the saved joblib model file
+        """
+        self.model = joblib.load(model_path)
+        self.class_names = {
+            0: "Strictly Necessary",
+            1: "Functionality", 
+            2: "Analytics",
+            3: "Advertising/Tracking"
+        }
+    
+    def predict(self, features: np.ndarray) -> np.ndarray:
+        """
+        Make predictions on input features.
+        
+        Args:
+            features: Preprocessed TF-IDF features (numpy array)
+            
+        Returns:
+            Array of predicted class labels
+        """
+        predictions = self.model.predict(features)
+        return predictions.astype(int)
+    
+    def predict_single(self, features: np.ndarray) -> int:
+        """
+        Make a prediction for a single sample.
+        
+        Args:
+            features: Preprocessed TF-IDF features for one sample
+            
+        Returns:
+            Predicted class label
+        """
+        if len(features.shape) == 1:
+            features = features.reshape(1, -1)
+        prediction = self.model.predict(features)[0]
+        return int(prediction)
+    
+    def get_class_name(self, class_id: int) -> str:
+        """
+        Get the name of a class given its ID.
+        
+        Args:
+            class_id: The numeric class identifier
+            
+        Returns:
+            The name/description of the class
+        """
+        return self.class_names.get(class_id, f"Unknown Class {class_id}")
+
+
+def load_model(model_path: str = "Linear Regression/LR_TFIDF+NAME.joblib") -> TextClassifier:
+    """
+    Load the trained model from disk.
+    
+    Args:
+        model_path: Path to the model file
+        
+    Returns:
+        TextClassifier instance
+    """
+    return TextClassifier(model_path)
+
+
+def main():
+    """Example usage of the model."""
+    # Load the model
+    print("Loading model...")
+    classifier = load_model()
+    print("Model loaded successfully!")
+    
+    # Example: Create dummy features for demonstration
+    # In practice, you would use your TF-IDF vectorizer to transform text
+    print("\nExample prediction (using random features for demonstration):")
+    dummy_features = np.random.randn(1, 100)  # Replace with actual TF-IDF features
+    
+    prediction = classifier.predict_single(dummy_features)
+    class_name = classifier.get_class_name(prediction)
+    
+    print(f"Predicted class: {prediction}")
+    print(f"Class name: {class_name}")
+    
+    # Batch prediction example
+    print("\nBatch prediction example:")
+    batch_features = np.random.randn(5, 100)  # 5 samples
+    batch_predictions = classifier.predict(batch_features)
+    
+    print(f"Predictions for {len(batch_predictions)} samples:")
+    for i, pred in enumerate(batch_predictions):
+        print(f"  Sample {i+1}: Class {pred} ({classifier.get_class_name(pred)})")
+
+
+if __name__ == "__main__":
+    main()