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| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| import re | |
| model_name = 'armandnlp/gpt2-TOD_finetuned_SGD' | |
| tokenizer_TOD = AutoTokenizer.from_pretrained(model_name) | |
| model_TOD = AutoModelForCausalLM.from_pretrained(model_name) | |
| def generate_response(prompt): | |
| input_ids = tokenizer_TOD(prompt, return_tensors="pt").input_ids | |
| outputs = model_TOD.generate(input_ids, | |
| do_sample=False, | |
| max_length=1024, | |
| eos_token_id=50262) | |
| return tokenizer_TOD.batch_decode(outputs)[0] | |
| #<|context|> <|user|> I want to go to the restaurant.<|endofcontext|> | |
| import gradio as gr | |
| iface = gr.Interface(fn=generate_response, | |
| inputs="text", | |
| outputs="text", | |
| title="gpt2-TOD", | |
| examples=[["<|context|> <|user|> I'm super hungry ! I want to go to the restaurant.<|endofcontext|>"], | |
| ["<|context|> <|user|> I want to go to the restaurant. <|system|> What food would you like to eat ? <|user|> Italian sounds good. <|endofcontext|>"]], | |
| description="Passing in a task-oriented dialogue context generates a belief state, actions to take and a response based on those actions", | |
| ) | |
| iface.launch() | |
| """ | |
| ## Work in progress | |
| ## https://gradio.app/creating_a_chatbot/ | |
| ## make chatbot interface | |
| ## can get input and responses for now | |
| ## would like to add belief state and actions to history response | |
| ## means modifying the history when appending input during next turn | |
| ## ie. keeping only the response and adding <|system|> token | |
| ckpt = 'armandnlp/gpt2-TOD_finetuned_SGD' | |
| tokenizer = AutoTokenizer.from_pretrained(ckpt) | |
| model = AutoModelForCausalLM.from_pretrained(ckpt) | |
| def predict(input, history=[]): | |
| # history: list of all token ids | |
| # response: list of tuples of strings corresponding to dialogue turns | |
| #model input and output with extra formatting | |
| new_user_input_ids = tokenizer.encode(' <|user|> '+input, return_tensors='pt') | |
| context = tokenizer.encode('<|context|>', return_tensors='pt') | |
| endofcontext = tokenizer.encode(' <|endofcontext|>', return_tensors='pt') | |
| model_input = torch.cat([context, torch.LongTensor(history), new_user_input_ids, endofcontext], dim=-1) | |
| out = model.generate(model_input, max_length=1024, eos_token_id=50262).tolist()[0] | |
| #history : format for next dialogue turn | |
| history = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1) | |
| string_out = tokenizer.decode(out) | |
| response_only = string_out.split('<|response|>')[1].replace('<|endofresponse|>', '') | |
| resp_tokenized = tokenizer.encode(' <|system|> '+response_only, return_tensors='pt') | |
| history = torch.cat([history, resp_tokenized], dim=-1).tolist() | |
| # history with belief + action | |
| # output with belief + action + response | |
| #response: format printed output | |
| turns = tokenizer.decode(history[0]) | |
| #turns = "<|user|> I want to go to the restaurant. <|system|> What food would you like to eat ? <|user|> Italian sounds good. <|system|> Okay then !" | |
| turns = re.split('<\|system\|>|<\|user\|>', turns)[1:] | |
| #print(turns) | |
| response = [(turns[i], turns[i+1]) for i in range(0, len(turns)-1, 2)] | |
| #print(response) | |
| return response, history | |
| #predict("I want to go to the restaurant.") | |
| import gradio as gr | |
| gr.Interface(fn=predict, | |
| inputs=["text", "state"], | |
| outputs=["chatbot", "state"]).launch() | |
| """ | |