app.py

import streamlit as st
import tensorflow as tf
import tensorflow_text  # 👈 required so ops like CaseFoldUTF8 are registered
import numpy as np

# Load the trained BERT model
@st.cache_resource
def load_model():
    return tf.keras.models.load_model("Disaster_Tweet_Verification_Using_NLP-4-150times")

model = load_model()

st.title("🌪️ Disaster Tweet Classifier")
st.write("Enter a tweet below to check if it's related to a disaster.")

# Input
tweet = st.text_area("Tweet:")

# Predict
if tweet:
    prediction = model.predict([tweet])
    score = float(prediction[0][0])
    label = "🚨 Disaster-related" if score >= 0.5 else "✅ Not disaster-related"

    st.markdown(f"### Prediction: {label}")
    st.write(f"Confidence: `{score:.2f}`")