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() |