app.py

from transformers import AutoModelForCausalLM, AutoTokenizer  
import gradio as gr  
import torch  
  
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 anyother model on 🤗HuggingFace."  
examples = [["How are you?"]]  
  
# Set the padding token to be used and initialize the model  
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
tokenizer.padding_side = 'left'  

from transformers import AutoModelForCausalLM, AutoTokenizer    
import gradio as gr    
import torch    
    
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?"]]    
    
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")  
tokenizer.padding_side = 'left'    
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")  
  
class ChatBot:  
    def __init__(self):  
        self.history = []  
  
    def predict(self, input):  
        new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors="pt")  
        bot_input_ids = torch.cat([torch.tensor(self.history), new_user_input_ids], dim=-1) if self.history else new_user_input_ids  
        chat_history_ids = model.generate(bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id)  
        self.history.append(chat_history_ids[:, bot_input_ids.shape[-1]:].tolist())  
        response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)  
        return response  
  
bot = ChatBot()  
  
iface = gr.Interface(  
    fn=bot.predict,  
    title=title,  
    description=description,  
    examples=examples,  
    inputs="text",  
    outputs="text",  
    theme="ParityError/Anime",  
)  
  
iface.launch()