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Update app.py
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app.py
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import gradio as gr
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import gradio as gr
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from transformers import AutoTokenizer, pipeline, AutoModelForSequenceClassification
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# load the tokenizer and model from Hugging Face
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tokenizer = AutoTokenizer.from_pretrained("ethanrom/a2")
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model = AutoModelForSequenceClassification.from_pretrained("ethanrom/a2")
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# define the classification labels
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class_labels = ["Negative", "Positive", "No Impact", "Mixed"]
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# create the zero-shot classification pipeline
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classifier = pipeline("zero-shot-classification", model=model, tokenizer=tokenizer, device=0)
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# define the Gradio interface
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def predict_sentiment(text, model_choice):
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if model_choice == "bert":
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# use the default BERT sentiment analysis pipeline
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sentiment_classifier = pipeline("sentiment-analysis", device=0)
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result = sentiment_classifier(text)[0]
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label = result["label"]
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score = result["score"]
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return f"{label} ({score:.2f})"
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else:
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# use the fine-tuned RoBERTa model for multi-class classification
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labels = class_labels
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hypothesis_template = "This text is about {}."
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result = classifier(text, hypothesis_template=hypothesis_template, multi_class=True, labels=labels)
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scores = result["scores"]
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predicted_label = result["labels"][0]
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return f"{predicted_label} ({scores[0]:.2f})"
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# define the Gradio interface inputs and outputs
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inputs = [gr.inputs.Textbox(label="Input Text"), gr.inputs.Radio(["bert", "fine-tuned RoBERTa"], label="Model Choice")]
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outputs = gr.outputs.Textbox(label="Sentiment Prediction")
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# create the Gradio interface
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gr.Interface(predict_sentiment, inputs, outputs, title="Sentiment Analysis App").launch()
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