app.py

#Import required libraries
import pickle
import gradio as gr
import gradio.inputs
import pandas as pd
import numpy as np
import tensorflow as tf 
from tensorflow.keras.preprocessing.sequence import pad_sequences
from huggingface_hub.keras_mixin import from_pretrained_keras

#Loading the tokenizer
with open('tokenizer.pickle', 'rb') as f:
    tokenizer = pickle.load(f)
   


def predict_sentiment(text):
    sentiment = ["I guess, I liked the movie, but I'm not sure it's my favorite."]
    sequence_test = tokenizer.texts_to_sequences([text])
    padded_test = pad_sequences(sequence_test, maxlen= 64)
    text=padded_test
    model = from_pretrained_keras("keras-io/bidirectional-lstm-imdb")
    X = [text for _ in range(len(model.input))]
    a=model.predict(X)
    return sentiment[np.around(a, decimals=0).argmax(axis=1)[0]]
description = "Provide an opinion regarding a movie as input and this app will suggest what the underlying sentiment is. "


#Gradio app     
iface = gr.Interface(predict_sentiment,
                     inputs= gradio.inputs.Textbox( lines=1, placeholder=None, default="", label=None),
                     outputs='text',
                     title="Sentiment Analysis of Movie Reviews",
                     description=description,
                     theme="grass")
iface.launch(enable_queue = True, inline=False, share = True)