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| 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() |