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| import os | |
| import gradio as gr | |
| HF_TOKEN = os.getenv('HF_TOKEN') | |
| hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "Rick-bot-flags") | |
| title = "Talk To Me Morty" | |
| description = """ | |
| <p> | |
| <center> | |
| The bot was trained on Rick and Morty dialogues Kaggle Dataset using DialoGPT. | |
| <img src="https://huggingface.co/spaces/kingabzpro/Rick_and_Morty_Bot/resolve/main/img/rick.png" alt="rick" width="200"/> | |
| </center> | |
| </p> | |
| """ | |
| article = "<p style='text-align: center'><a href='https://medium.com/geekculture/discord-bot-using-dailogpt-and-huggingface-api-c71983422701' target='_blank'>Complete Tutorial</a></p><p style='text-align: center'><a href='https://dagshub.com/kingabzpro/DailoGPT-RickBot' target='_blank'>Project is Available at DAGsHub</a></p></center><center><img src='https://visitor-badge.glitch.me/badge?page_id=kingabzpro/Rick_and_Morty_Bot' alt='visitor badge'></center></p>" | |
| examples = [["How are you Rick?"]] | |
| 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, 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, | |
| flagging_callback = hf_writer, | |
| allow_flagging = "manual", | |
| inputs=["text", "state"], | |
| outputs=["chatbot", "state"], | |
| theme='gradio/seafoam').launch() | |
| #theme ="grass", | |
| #title = title, | |
| #flagging_callback=hf_writer, | |
| #description = description, | |
| #article = article |