advanced_nlu.py

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

class AdvancedNLU:
    def __init__(self, model_name="facebook/bart-large-mnli"):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(self.device)

    def understand(self, text: str, context: List[Dict[str, str]] = None) -> Dict[str, Any]:
        # Prepare input
        if context:
            context_text = " ".join([f"{item['role']}: {item['content']}" for item in context])
            input_text = f"{context_text}\nHuman: {text}\nAI:"
        else:
            input_text = f"Human: {text}\nAI:"

        # Tokenize and generate
        inputs = self.tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True).to(self.device)
        outputs = self.model.generate(**inputs, max_length=100, num_return_sequences=1, do_sample=True)

        # Decode and parse the output
        decoded_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Here, implement more sophisticated parsing based on the expected structure of the output
        # For this example, we'll use a simple approach
        intent, entities = self._parse_output(decoded_output)

        return {
            "intent": intent,
            "entities": entities,
            "raw_output": decoded_output
        }

    def _parse_output(self, output: str) -> Tuple[str, List[Dict[str, str]]]:
        # Implement more sophisticated parsing here
        # This is a placeholder implementation
        parts = output.split(", ")
        intent = parts[0] if parts else "unknown"
        entities = [{"type": "entity", "value": entity} for entity in parts[1:]]
        return intent, entities

# Usage
nlu = AdvancedNLU()
result = nlu.understand("Book a flight to New York for tomorrow", 
                        context=[{"role": "Human", "content": "I need to travel soon."},
                                 {"role": "AI", "content": "Certainly! Where would you like to go?"}])
print(result)