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| import requests | |
| import gradio as gr | |
| from huggingface_hub import from_pretrained_fastai | |
| import torch | |
| from fastai.text.all import * | |
| from blurr.text.data.all import * | |
| from blurr.text.modeling.all import * | |
| # load model | |
| repo_id = 'matteopilotto/deberta-v3-base-tweet_eval-emotion' | |
| load_learner = from_pretrained_fastai(repo_id) | |
| # define labels | |
| labels_url = 'https://huggingface.co/matteopilotto/deberta-v3-base-tweet_eval-emotion/raw/main/class_names.txt' | |
| labels = requests.get(labels_url).text.splitlines() | |
| # define function to pass to gradio.Interface | |
| def predict(prompt): | |
| out = load_learner.blurr_predict(prompt)[0] | |
| confidences = {label: prob for label, prob in zip(labels, out['probs'])} | |
| return confidences | |
| # define input texbox to pass to gradio.Interface | |
| textbox = gr.Textbox( | |
| label='input', | |
| placeholder=None, | |
| lines=2 | |
| ) | |
| # define exables to pass to gradio.Interface | |
| examples = ["hey @user #fields in #skibbereen give your online delivery service a horrible name. 1.5 hours late on the 1 hour delivery window.", | |
| "when you only meet each other once at an interview and you recognise each other on the streets 🙆 i don't even know what's your name 😂"] | |
| gr.Interface( | |
| fn=predict, | |
| inputs=textbox, | |
| outputs=gr.Label(), | |
| title='Emotion in tweets', | |
| description="""<center><img src="https://huggingface.co/matteopilotto/deberta-v3-base-tweet_eval-emotion/resolve/main/emoji_image.png" width=600px></center>""", | |
| examples = examples, | |
| live=True | |
| ).launch() |