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import gradio as gr |
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import tensorflow as tf |
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import pickle |
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import numpy as np |
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model = tf.keras.models.load_model("subway_alert_classifier_model.keras") |
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vectorizer = pickle.load(open("tfidf_vectorizer.pkl", "rb")) |
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label_encoder = pickle.load(open("label_encoder.pkl", "rb")) |
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def classify_alert(text): |
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X = vectorizer.transform([text]) |
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y_pred = model.predict(X) |
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label = label_encoder.inverse_transform([np.argmax(y_pred)]) |
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return label[0] |
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iface = gr.Interface( |
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fn=classify_alert, |
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inputs=gr.Textbox(lines=2, placeholder="Enter MTA alert description"), |
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outputs="text", |
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title="Subway Delay Cause Classifier", |
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description="Classifies MTA alerts into causes like 'track', 'signals', 'medical', etc." |
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) |
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iface.launch() |
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