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barbieheimer
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893b93a
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906b41b
Create app.py
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app.py
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# here are some examples for sadness, joy, anger, and optimism.
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import gradio as gr
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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from transformers import pipeline
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tokenizer = AutoTokenizer.from_pretrained("barbieheimer/MND_TweetEvalBert_model")
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model = AutoModelForSequenceClassification.from_pretrained("barbieheimer/MND_TweetEvalBert_model")
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# We can now use the model in the pipeline.
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classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
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def predict(prompt):
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completion = classifier(prompt)
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return completion[0]["label"], completion[0]["score"]
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examples = [
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["The movie was a bummer."],
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["I cannot wait to watch all these movies!"],
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["The ending of the movie really irks me, gives me the ick fr."],
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["The protagonist seems to have a lot of hope...."]
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]
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gr.Interface.load("models/barbieheimer/MND_TweetEvalBert_model", fn=predict, title="Sentiment Analysis", examples=examples,
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inputs=gr.inputs.Textbox(lines=5, label="Paste an Article here."),
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outputs=[gr.outputs.Textbox(label="Label"),gr.outputs.Textbox(label="Score")],).launch()
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