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Create app.py

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  1. app.py +53 -0
app.py ADDED
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+ import gradio as gr
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+ from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
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+
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+
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+ class EmotionClassifier:
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+ def __init__(self, model_name: str):
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+ self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
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+ self.tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ self.pipeline = pipeline(
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+ "text-classification",
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+ model=self.model,
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+ tokenizer=self.tokenizer,
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+ return_all_scores=True,
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+ )
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+
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+ def predict(self, input_text: str):
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+ pred = self.pipeline(input_text)[0]
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+ result = {
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+ "Sadness 😭": pred[0]["score"],
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+ "Joy πŸ˜‚": pred[1]["score"],
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+ "Love 😍": pred[2]["score"],
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+ "Anger 😠": pred[3]["score"],
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+ "Fear 😨": pred[4]["score"],
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+ "Surprise 😲": pred[5]["score"],
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+ }
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+ return result
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+
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+
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+ def main():
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+ model = EmotionClassifier("bhadresh-savani/bert-base-uncased-emotion")
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+ iface = gr.Interface(
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+ fn=model.predict,
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+ inputs=gr.inputs.Textbox(
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+ lines=3,
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+ placeholder="Type a phrase that has some emotion",
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+ label="Input Text",
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+ ),
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+ outputs="label",
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+ title="Emotion Classification",
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+ examples=[
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+ "I get so down when I'm alone",
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+ "I believe that today everything will work out",
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+ "It was so dark there I was afraid to go",
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+ "I loved the gift you gave me",
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+ "I was very surprised by your presentation.",
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+ ],
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+ )
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+
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+ iface.launch()
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+
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+
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+ if __name__ == "__main__":
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+ main()