import gradio as gr
import tensorflow as tf
import pickle
import numpy as np

# Load model + vectorizer + label encoder
model = tf.keras.models.load_model("subway_alert_classifier_model.keras")
vectorizer = pickle.load(open("tfidf_vectorizer.pkl", "rb"))
label_encoder = pickle.load(open("label_encoder.pkl", "rb"))

def classify_alert(text):
    X = vectorizer.transform([text])
    y_pred = model.predict(X)
    label = label_encoder.inverse_transform([np.argmax(y_pred)])
    return label[0]

iface = gr.Interface(
    fn=classify_alert,
    inputs=gr.Textbox(lines=2, placeholder="Enter MTA alert description"),
    outputs="text",
    title="Subway Delay Cause Classifier",
    description="Classifies MTA alerts into causes like 'track', 'signals', 'medical', etc."
)

iface.launch()