app.py

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

access_token = "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"

#set up the model
tokenizer = AutoTokenizer.from_pretrained("mental/mental-bert-base-uncased", use_auth_token = access_token )

model = AutoModel.from_pretrained("mental/mental-bert-base-uncased", use_auth_token = access_token )

#Defining a predict function
def predict(input, history=[]):
    # tokenize the new input sentence
    new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')

    # append the new user input tokens to the chat history
    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)

    # generate a response 
    history = model.generate(bot_input_ids, max_length=1000, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, pad_token_id=tokenizer.eos_token_id).tolist()

    # convert the tokens to text, and then split the responses into lines
    response = tokenizer.decode(history[0]).split("<|endoftext|>")
    #print('decoded_response-->>'+str(response))
    response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
    #print('response-->>'+str(response))
    return response, history

description = "This is a chatbot application based on the DialoGPT model of Microsoft domain focused on mental health. Type a Hi or Hello to get started with chatting."
title = "MentalChatGpt 🦚"
examples = [["I feel anxious"]]
gr.Interface(fn=predict,
             title=title,
             description=description,
             examples=examples,
             inputs=["text", "state"],
             outputs=["chatbot", "state"]).launch()