Spaces:
Runtime error
Runtime error
| import os | |
| import gradio as gr | |
| title = "Ask Rick a Question" | |
| description = """ | |
| <center> | |
| The bot was trained to answer questions based on Rick and Morty dialogues. Ask Rick anything! | |
| <img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px> | |
| </center> | |
| """ | |
| article = "Check out [the original Rick and Morty Bot](https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot) that this demo is based off of." | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("ericzhou/DialoGPT-Medium-Rick_v2") | |
| model = AutoModelForCausalLM.from_pretrained("ericzhou/DialoGPT-Medium-Rick_v2") | |
| def predict(input): | |
| # tokenize the new input sentence | |
| new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt') | |
| # generate a response | |
| history = model.generate(new_user_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist() | |
| # convert the tokens to text, and then split the responses into the right format | |
| response = tokenizer.decode(history[0]).split("<|endoftext|>") | |
| return response[1] | |
| gr.Interface(fn = predict, inputs = ["textbox"], outputs = ["text"], title = title, description = description, article = article).launch() | |