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")     
    
def predict(input, history=[]):    
    new_user_input_ids = tokenizer.encode(input, return_tensors="pt")    
    
    bot_input_ids = torch.cat([torch.tensor(history), new_user_input_ids], dim=-1) if history else new_user_input_ids    
    
    chat_history_ids = model.generate(bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id)    
    
    response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)    
        
    return response    
    
iface = gr.Interface(    
    fn=predict,    
    title=title,    
    description=description,    
    examples=examples,    
    inputs="text",    
    outputs="text",    
    theme="ParityError/Anime",    
)    
    
iface.launch()