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app.py

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+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()