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

# Load the pre-trained model
model = tf.keras.models.load_model('sentimentality.h5')

# Define a function to preprocess the text input
def preprocess(text):
    tokenizer = tf.keras.preprocessing.text.Tokenizer()
    tokenizer.fit_on_texts([text])
    text = tokenizer.texts_to_sequences([text])
    text = tf.keras.preprocessing.sequence.pad_sequences(text, maxlen=500, padding='post', truncating='post')
    return text

def sentiment_analysis(text):
    scores = model.predict(text)
    return scores

iface = gr.Interface(
    fn=sentiment_analysis, 
    inputs=gr.Textbox(placeholder="Enter a positive or negative sentence here..."), 
    outputs="label", 
    interpretation="default",
    examples=[["This is wonderful!"]])

iface.launch()