inference_example.py

"""
Simple inference examples for English to Kannada translation
Quick start guide for using the model
"""

import torch
from tokenizers import Tokenizer
from main import Transformer, greedy_decode


def simple_translate(english_sentence):
    """
    Translate a single English sentence to Kannada
    
    Args:
        english_sentence: English text to translate
        
    Returns:
        Kannada translation
    """
    # Setup device
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    
    # Load model
    checkpoint = torch.load('best_model.pt', map_location=device)
    vocab_info = checkpoint['vocab_info']
    
    # Initialize model
    model = Transformer(
        d_model=384,
        ffn_hidden=1536,
        num_heads=6,
        drop_prob=0.1,
        num_layers=4,
        max_sequence_length=75,
        src_vocab_size=vocab_info['source_vocab_size'],
        tgt_vocab_size=vocab_info['target_vocab_size']
    )
    
    # Load weights
    model.load_state_dict(checkpoint['model_state_dict'])
    model.to(device)
    model.eval()
    
    # Load tokenizers
    src_tokenizer = Tokenizer.from_file('source_tokenizer.json')
    tgt_tokenizer = Tokenizer.from_file('target_tokenizer.json')
    
    # Translate
    translation = greedy_decode(
        model=model,
        src_sentence=english_sentence,
        source_tokenizer=src_tokenizer,
        target_tokenizer=tgt_tokenizer,
        vocab_info=vocab_info,
        device=device,
        max_length=75
    )
    
    return translation


class TranslationPipeline:
    """
    Reusable translation pipeline for multiple translations
    Loads model once and reuses it
    """
    
    def __init__(self, model_path='best_model.pt', 
                 src_tokenizer_path='source_tokenizer.json',
                 tgt_tokenizer_path='target_tokenizer.json'):
        """
        Initialize the translation pipeline
        
        Args:
            model_path: Path to model checkpoint
            src_tokenizer_path: Path to source tokenizer
            tgt_tokenizer_path: Path to target tokenizer
        """
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        print(f"Initializing translation pipeline on {self.device}...")
        
        # Load model
        checkpoint = torch.load(model_path, map_location=self.device)
        self.vocab_info = checkpoint['vocab_info']
        
        self.model = Transformer(
            d_model=384,
            ffn_hidden=1536,
            num_heads=6,
            drop_prob=0.1,
            num_layers=4,
            max_sequence_length=75,
            src_vocab_size=self.vocab_info['source_vocab_size'],
            tgt_vocab_size=self.vocab_info['target_vocab_size']
        )
        
        self.model.load_state_dict(checkpoint['model_state_dict'])
        self.model.to(self.device)
        self.model.eval()
        
        # Load tokenizers
        self.src_tokenizer = Tokenizer.from_file(src_tokenizer_path)
        self.tgt_tokenizer = Tokenizer.from_file(tgt_tokenizer_path)
        
        print("Pipeline ready!")
    
    def translate(self, english_text):
        """
        Translate English text to Kannada
        
        Args:
            english_text: English sentence or text
            
        Returns:
            Kannada translation
        """
        return greedy_decode(
            model=self.model,
            src_sentence=english_text,
            source_tokenizer=self.src_tokenizer,
            target_tokenizer=self.tgt_tokenizer,
            vocab_info=self.vocab_info,
            device=self.device,
            max_length=75
        )
    
    def translate_batch(self, english_texts):
        """
        Translate multiple English texts
        
        Args:
            english_texts: List of English sentences
            
        Returns:
            List of Kannada translations
        """
        return [self.translate(text) for text in english_texts]


# Example 1: Simple one-time translation
def example_1_simple():
    """Example: Translate a single sentence"""
    print("Example 1: Simple Translation")
    print("-" * 50)
    
    sentence = "Good morning, have a nice day!"
    translation = simple_translate(sentence)
    
    print(f"English: {sentence}")
    print(f"Kannada: {translation}")
    print()


# Example 2: Using the pipeline for multiple translations
def example_2_pipeline():
    """Example: Translate multiple sentences efficiently"""
    print("Example 2: Pipeline Translation")
    print("-" * 50)
    
    # Initialize pipeline once
    pipeline = TranslationPipeline()
    
    # Translate multiple sentences
    sentences = [
        "Hello world!",
        "How are you today?",
        "What is your name?",
        "I am learning Kannada.",
        "Thank you for your help."
    ]
    
    print("Translating sentences:\n")
    for eng in sentences:
        kan = pipeline.translate(eng)
        print(f"  EN: {eng}")
        print(f"  KN: {kan}")
        print()


# Example 3: Batch translation
def example_3_batch():
    """Example: Batch translation"""
    print("Example 3: Batch Translation")
    print("-" * 50)
    
    pipeline = TranslationPipeline()
    
    sentences = [
        "The weather is beautiful.",
        "Where is the nearest hospital?",
        "Can you help me please?",
        "I love this city.",
        "See you tomorrow!"
    ]
    
    translations = pipeline.translate_batch(sentences)
    
    print("Batch translation results:\n")
    for eng, kan in zip(sentences, translations):
        print(f"EN: {eng}")
        print(f"KN: {kan}")
        print()


# Example 4: Custom usage with error handling
def example_4_with_error_handling():
    """Example: Translation with error handling"""
    print("Example 4: Translation with Error Handling")
    print("-" * 50)
    
    try:
        pipeline = TranslationPipeline()
        
        test_cases = [
            "Hello!",
            "",  # Empty string
            "This is a very long sentence that might exceed the maximum token length and we need to see how the model handles it when processing.",
            "Short.",
        ]
        
        for text in test_cases:
            try:
                if not text.strip():
                    print("Skipping empty input")
                    continue
                    
                translation = pipeline.translate(text)
                print(f"✓ EN: {text}")
                print(f"  KN: {translation}")
                print()
            except Exception as e:
                print(f"✗ Error translating '{text}': {e}")
                print()
                
    except Exception as e:
        print(f"Failed to initialize pipeline: {e}")


if __name__ == "__main__":
    print("="*70)
    print("English to Kannada Translation - Inference Examples")
    print("="*70)
    print()
    
    # Run examples
    example_1_simple()
    print("\n" + "="*70 + "\n")
    
    example_2_pipeline()
    print("\n" + "="*70 + "\n")
    
    example_3_batch()
    print("\n" + "="*70 + "\n")
    
    example_4_with_error_handling()
    print("\n" + "="*70)
    print("All examples completed!")