#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


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


def predict_sentiment(text):
    sentiment = ['Do you really dislike the movie so much?','Hmm...your thoughts are neutral about the movie.','Wow! Your a big fan.']
    sequence_test = tokenizer.texts_to_sequences([text])
    padded_test = pad_sequences(sequence_test, maxlen= 52)
    text=padded_test
    model = tf.keras.models.load_model("huggingface/keras-io/bidirectional-lstm-imdb")
    X = [text for _ in range(len(model.input))]
    a=model.predict(X, verbose=0)
    return sentiment[np.around(a, decimals=0).argmax(axis=1)[0]]
description = "Give a review of a movie that you like(or hate, sarcasm intended XD) and the model will let you know just how much your review truely reflects your emotions. "


#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)