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| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import gradio as gr | |
| import torch | |
| title = "🤖AI ChatBot" | |
| description = "Building open-domain chatbots is a challenging area for machine learning research." | |
| examples = [["How are you?"]] | |
| tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") | |
| model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") | |
| 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=4000, 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 | |
| gr.Interface( | |
| fn=predict, | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| inputs=["text", "state"], | |
| outputs=["chatbot", "state"], | |
| theme="finlaymacklon/boxy_violet", | |
| ).launch() | |