app.py

import gradio as gr
import tensorflow as tf

# Load the saved model
model = tf.keras.models.load_model("sentimentality.h5")

def predict_sentiment(text):
    """
    This function takes a text input and returns the predicted sentiment using a saved model.
    
    Parameters:
    text (str): The input text to be analyzed
    
    Returns:
    str: The predicted sentiment of the input text (either "positive" or "negative")
    """
    # Preprocess the input text
    text = preprocess_text(text)
    
    # Make the prediction using the loaded model
    prediction = model.predict([text])[0][0]
    
    # Return the predicted sentiment
    if prediction > 0.5:
        return "positive"
    else:
        return "negative"

# Create the Gradio interface
iface = gr.Interface(fn=predict_sentiment, inputs="text", outputs="text")

# Run the interface
iface.launch()