from transformers import AutoModelForCausalLM, AutoTokenizer      
import gradio as gr      
import torch      

# Create the ChatBot class
class ChatBot:
    def __init__(self):
        # Initialize the history
        self.history = []

        # Initialize the tokenizer with SentencePiece support
        self.tokenizer = AutoTokenizer.from_pretrained("Open-Orca/Mistral-7B-OpenOrca", use_fast=True)

        # Initialize the model
        self.model = AutoModelForCausalLM.from_pretrained("Open-Orca/Mistral-7B-OpenOrca")

    def predict(self, input):
        # Encode user input
        new_user_input_ids = self.tokenizer.encode(input + self.tokenizer.eos_token, return_tensors="pt")

        # Flatten the conversation history
        flat_history = [item for sublist in self.history for item in sublist]
        flat_history_tensor = torch.tensor(flat_history).unsqueeze(dim=0)

        # Create the input tensor for the model
        bot_input_ids = torch.cat([flat_history_tensor, new_user_input_ids], dim=-1) if self.history else new_user_input_ids

        # Generate a response from the model
        chat_history_ids = self.model.generate(bot_input_ids, max_length=2000, pad_token_id=self.tokenizer.eos_token_id)

        # Update the history with the generated response
        self.history.append(chat_history_ids[:, bot_input_ids.shape[-1]:].tolist()[0])

        # Decode the response
        response = self.tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)

        return response

# Example usage
chatbot = ChatBot()
user_input = "Hello, chatbot!"
response = chatbot.predict(user_input)
print(response)

  
title = "👋🏻Welcome to Tonic's EZ Chat🚀"    
description = "You can use this Space to test out the current model (DialoGPT-medium) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on [Discord](https://discord.gg/fpEPNZGsbt) to build together."    
examples = [["How are you?"]]    
  
iface = gr.Interface(    
    fn=bot.predict,    
    title=title,    
    description=description,    
    examples=examples,    
    inputs="text",    
    outputs="text", 
    theme="ParityError/Anime"
)    
  
iface.launch()