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import gradio as gr | |
import gradio.inputs | |
import pandas as pd | |
import numpy as np # linear algebra | |
import os #interacting with input and output directories | |
import tensorflow as tf #framework for creating the neural network | |
from tensorflow.keras.preprocessing.sequence import pad_sequences | |
import pickle | |
with open('tokenizer.pickle', 'rb') as handle: | |
tokenizer = pickle.load(handle) | |
# loading | |
def fn(X_test): | |
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([X_test]) | |
padded_test = pad_sequences(sequence_test, maxlen= 52) | |
Xtest=padded_test | |
model = tf.keras.models.load_model(os.path.join(os.getcwd(), 'deepverse.h5')) | |
X = [Xtest 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. " | |
here = gr.Interface(fn, | |
inputs= gradio.inputs.Textbox( lines=1, placeholder=None, default="", label=None), | |
outputs='text', | |
title="Sentiment analysis of movie reviews", | |
description=description, | |
theme="peach", | |
allow_flagging="auto", | |
flagging_dir='flagging records') | |
here.launch(inline=False, share = True) | |