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| # here are some examples for sadness, joy, anger, and optimism. | |
| import gradio as gr | |
| # Load model directly | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from transformers import pipeline | |
| tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model") | |
| model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model") | |
| # We can now use the model in the pipeline. | |
| classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) | |
| def predict(prompt): | |
| completion = classifier(prompt) | |
| return completion[0]["label"], completion[0]["score"] | |
| examples = [ | |
| ["The movie was a bummer."], | |
| ["I cannot wait to watch all these movies!"], | |
| ["The ending of the movie really irks me, gives me the ick fr."], | |
| ["The protagonist seems to have a lot of hope...."] | |
| ] | |
| gr.Interface.load("models/barbieheimer/MND_TweetEvalBert_model", fn=predict, title="Sentiment Analysis", examples=examples, | |
| inputs=gr.inputs.Textbox(lines=5, label="Paste an Article here."), | |
| outputs=[gr.outputs.Textbox(label="Label"),gr.outputs.Textbox(label="Score")],).launch() |