Spaces:
Runtime error
Runtime error
| import discord | |
| # from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # from transformers import pipeline | |
| import os | |
| from dotenv import load_dotenv | |
| # Lite mode | |
| # from transformers import AutoTokenizer, AutoModelForQuestionAnswering | |
| # model_name = "deepset/roberta-base-squad2" | |
| # nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) | |
| # tokenizer = AutoTokenizer.from_pretrained(name,) | |
| # model = AutoModelForQuestionAnswering.from_pretrained(name) | |
| # nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) | |
| #load_dotenv() | |
| # TODO | |
| # Add chat history to DialoGPT for context | |
| ## Initialize models | |
| # Question Answering | |
| # model_name = "deepset/roberta-base-squad2" | |
| # nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) | |
| # Conversational | |
| convo_tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large") | |
| convo_model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") | |
| # Summarizer | |
| # summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| print('Models and tokenizers successfully loaded') | |
| # Get Discord client | |
| client = discord.Client() | |
| ## Event Handlers | |
| async def on_ready(): | |
| print('We have logged in as {0.user}'.format(client)) | |
| status = "We have logged in" | |
| async def on_message(message): | |
| if message.author == client.user: | |
| return | |
| if message.content.startswith('$ping'): | |
| await message.channel.send('pong') | |
| print(message.author) | |
| print(message.author.name) | |
| print(message.author.avatar) | |
| if message.content.startswith('$help'): | |
| await message.channel.send('List of stable commands: ') | |
| await message.channel.send('COMMANDS') | |
| await message.channel.send('List of nightly commands: ') | |
| await message.channel.send('COMMANDS') | |
| if message.content.startswith('$q'): | |
| question = message.content[4::] | |
| if(len(question) == 0): | |
| await message.channel.send("I didn't get that.") | |
| return | |
| else: | |
| print(question) | |
| QA_input = { | |
| 'question': 'Why is model conversion important?', | |
| 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' | |
| } | |
| res = nlp(QA_input) | |
| ans = res['answer'] | |
| score = res['score'] | |
| await message.channel.send(ans) | |
| await message.channel.send('score: ' + score) | |
| # if message.content.startswith('$q'): | |
| # question = message.content[4::] | |
| # if(len(question) == 0): | |
| # await message.channel.send("I didn't get that.") | |
| # return | |
| # else: | |
| # print(question) | |
| # QA_input = { | |
| # 'question': 'Why is model conversion important?', | |
| # 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' | |
| # } | |
| # res = nlp(QA_input) | |
| # ans = res['answer'] | |
| # score = res['score'] | |
| # await message.channel.send(ans) | |
| # await message.channel.send('score: ' + score) | |
| # if message.content.startswith('$TLDR'): | |
| # query = message.content[6::] | |
| # if(len(query) < 2): | |
| # messages = [message.content async for message in message.channel.history(limit=30)] | |
| # messages_str = ".".join(messages) | |
| # summary = summarizer(messages_str, max_length=130, min_length=30, do_sample=False) | |
| # await message.channel.send('Summary: ' + summary[0]['summary_text']) | |
| # elif(len(query) < 35): | |
| # await message.channel.send('Too short to summarize.') | |
| # else: | |
| # summary = summarizer(query, max_length=130, min_length=30, do_sample=False) | |
| # await message.channel.send('Summary: ' + summary[0]['summary_text']) | |
| # if message.content.startswith('$talk'): | |
| # chat = message.content[6::] | |
| # if(len(chat) == 0): | |
| # await message.channel.send("I didn't get that.") | |
| # return | |
| # else: | |
| # print(chat) | |
| # input_ids = convo_tokenizer.encode(chat + convo_tokenizer.eos_token, return_tensors='pt') | |
| # return_ids = convo_model.generate(input_ids, max_length=1000, pad_token_id=convo_tokenizer.eos_token_id) | |
| # response = "{}".format(convo_tokenizer.decode(return_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)) | |
| # await message.channel.send(response) | |
| client.run(str(os.environ.get('TOKEN'))) | |
| # client.run(str(os.getenv('TOKEN'))) |