modules/chatbot.py

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
import torch

class MultilingualChatbot:
    def __init__(self):
        self.models = {
            'en': GPT2LMHeadModel.from_pretrained("microsoft/DialoGPT-medium"),
            'es': GPT2LMHeadModel.from_pretrained("DeepESP/gpt2-spanish"),
            'fr': GPT2LMHeadModel.from_pretrained("asi/gpt-fr-cased-small")
        }
        self.tokenizers = {
            'en': GPT2Tokenizer.from_pretrained("microsoft/DialoGPT-medium"),
            'es': GPT2Tokenizer.from_pretrained("DeepESP/gpt2-spanish"),
            'fr': GPT2Tokenizer.from_pretrained("asi/gpt-fr-cased-small")
        }
        for tokenizer in self.tokenizers.values():
            tokenizer.pad_token = tokenizer.eos_token

    def generate_response(self, prompt, src_lang):
        # Default to English if the language is not supported
        model = self.models.get(src_lang, self.models['en'])
        tokenizer = self.tokenizers.get(src_lang, self.tokenizers['en'])
        
        input_ids = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors='pt')
        
        # Move input to the same device as the model
        input_ids = input_ids.to(model.device)
        
        chat_history_ids = model.generate(
            input_ids,
            max_length=1000,
            pad_token_id=tokenizer.eos_token_id,
            no_repeat_ngram_size=3,
            do_sample=True,
            top_k=50,
            top_p=0.95,
            temperature=0.7,
            num_return_sequences=1,
            length_penalty=1.0,
            repetition_penalty=1.2
        )
        return tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)

def initialize_chatbot():
    return MultilingualChatbot()

def get_chatbot_response(chatbot, prompt, src_lang):
    return chatbot.generate_response(prompt, src_lang)

def initialize_chatbot():
    return MultilingualChatbot()

def get_chatbot_response(chatbot, prompt, src_lang):
    return chatbot.generate_response(prompt, src_lang)