# nlp_integration.py

import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer

class NLPIntegration:
    def __init__(self, model_name='bert-base-uncased'):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)

    def encode_text(self, text):
        inputs = self.tokenizer(text, return_tensors='pt', padding=True, truncation=True)
        with torch.no_grad():
            outputs = self.model(**inputs)
        return outputs.last_hidden_state.mean(dim=1)

    def similarity(self, text1, text2):
        encoding1 = self.encode_text(text1)
        encoding2 = self.encode_text(text2)
        return nn.functional.cosine_similarity(encoding1, encoding2)

# Placeholder for future NLP-related functions
def process_text(text):
    # TODO: Implement text processing logic
    pass

def generate_text(prompt, max_length=100):
    # TODO: Implement text generation logic
    pass

def summarize_text(text):
    # TODO: Implement text summarization logic
    pass

# Add more NLP-related functions as needed
