app.py

from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.models import load_model
import numpy as np
import gradio as gr
model = load_model('trained.h5')

tokenizer = Tokenizer()
def encoder(text):
    text = tokenizer.texts_to_sequences([text])
    text = sequence.pad_sequences(text, maxlen=200)
    return text

def predict(text):
    encoded_text = encoder(text)
    #print(encoded_text)
    prediction = (model.predict(encoded_text))
    print(prediction)
    prediction = np.round(prediction)
    if prediction==1:
        return "Disaster"
    return "Not a Disaster"

title="Relevance Classifier"
description="<p style='text-align:center'>Classifies input text into Disaster-related or not disaster related."

gr.Interface(fn=predict, inputs='text', output='text', title=title, description=description).launch()