Spaces:
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| # -*- coding: utf-8 -*- | |
| """chat.ipynb | |
| Automatically generated by Colab. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1FxpG0gxd0Oigj5CAekXbdwgu7NdtI5sF | |
| """ | |
| # !pip install -r requirements.txt | |
| from transformers import pipeline, Conversation | |
| import gradio as gr | |
| # toy example 1 | |
| pipeline(task="sentiment-analysis")("Love this!") | |
| # toy example 2 | |
| pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")("Love this!") | |
| # defining classifier | |
| classifier = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english") | |
| classifier("Hate this.") | |
| # we can also pass in a list to classifier | |
| text_list = ["This is great", \ | |
| "Thanks for nothing", \ | |
| "You've got to work on your face", \ | |
| "You're beautiful, never change!"] | |
| classifier(text_list) | |
| # if there are multiple target labels, we can return them all | |
| classifier = pipeline(task="text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None) | |
| classifier(text_list[0]) | |
| summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| text = """ | |
| Hugging Face is an AI company that has become a major hub for open-source machine learning. | |
| Their platform has 3 major elements which allow users to access and share machine learning resources. | |
| First, is their rapidly growing repository of pre-trained open-source machine learning models for things such as natural language processing (NLP), computer vision, and more. | |
| Second, is their library of datasets for training machine learning models for almost any task. | |
| Third, and finally, is Spaces which is a collection of open-source ML apps. | |
| The power of these resources is that they are community generated, which leverages all the benefits of open source i.e. cost-free, wide diversity of tools, high quality resources, and rapid pace of innovation. | |
| While these make building powerful ML projects more accessible than before, there is another key element of the Hugging Face ecosystem—their Transformers library. | |
| """ | |
| summarized_text = summarizer(text, min_length=5, max_length=140)[0]['summary_text'] | |
| summarized_text | |
| classifier(summarized_text) | |
| chatbot = pipeline(model="facebook/blenderbot-400M-distill") | |
| conversation = chatbot("Hi I'm Shaw, how are you?") | |
| conversation | |
| conversation = chatbot("Where do you work?") | |
| conversation | |
| def top3_text_classes(message, history): | |
| return str(classifier(message)[0][:3]).replace('}, {', '\n').replace('[{', '').replace('}]', '') | |
| demo_sentiment = gr.ChatInterface(top3_text_classes, title="Text Sentiment Chatbot", description="Enter your text, and the chatbot will classify the sentiment.") | |
| demo_sentiment.launch() | |
| def summarizer_bot(message, history): | |
| return summarizer(message, min_length=5, max_length=140)[0]['summary_text'] | |
| demo_summarizer = gr.ChatInterface(summarizer_bot, title="Summarizer Chatbot", description="Enter your text, and the chatbot will return the summarized version.") | |
| demo_summarizer.launch() | |
| message_list = [] | |
| response_list = [] | |
| def vanilla_chatbot(message, history): | |
| conversation = chatbot(message) | |
| return conversation[0]['generated_text'] | |
| demo_chatbot = gr.ChatInterface(vanilla_chatbot, title="Vanilla Chatbot", description="Enter text to start chatting.") | |
| demo_chatbot.launch() |