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app.py

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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)
+
+
+
+

deepverse.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f8f1f6dc6d025716f12fefe216f8786984a766bea583999cab9b97f5bc278ff6
+size 11804512