Update app.py

app.py

@@ -1,34 +0,0 @@
-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)
-
-
-
-