app.py

#!/usr/bin/env python
# coding: utf-8

# In[1]:


import gradio as gr
import tensorflow as tf 
from tensorflow.keras.preprocessing.sequence import pad_sequences
import os
import pickle
from keras.preprocessing.text import Tokenizer
from keras.preprocessing import sequence
from keras.utils import to_categorical, pad_sequences
import numpy as np

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model = tf.keras.models.load_model('deep_learning_model_sentiment_analysis.h5', compile=False)
model.compile()


# In[3]:


with open('tokenizer_new.pickle', 'rb') as file:
    tokenizer = pickle.load(file)


# In[11]:


def decide(text):
    sentiment_classes = ['negative', 'neutral', 'positive']
    max_len = 2000

    # Tokenize the text
    text = [text]
    xt = tokenizer.texts_to_sequences(text)
    xt = pad_sequences(xt, padding='post', maxlen=max_len)

    # Make the prediction
    prediction = model.predict(xt)
    prediction_class = sentiment_classes[np.argmax(prediction)]

    return prediction_class


# In[12]:


example_sentence_1 = "aduh mahasiswa sombong kasih kartu kuning belajar usahlah politik selesai kuliah nya politik telat dasar mahasiswa"
example_sentence_2 = "lokasi strategis jalan sumatra bandung nya nyaman sofa lantai paella nya enak pas dimakan minum bir dingin appetiser nya enak enak"
examples = [[example_sentence_1], [example_sentence_2]]


# In[13]:


title = "Sentiment Analysis dengan Deep Learning"
article = "<p style='text-align: center'><a href='https://www.linkedin.com/in/m-afif-rizky-a-a96048182/'>Created by @Vrooh933 Production</a> | <a href='https://github.com/afifrizkyandika11551100310'>GitHub Profile</a>"


# In[14]:


intfc = gr.Interface(fn=decide,
                     inputs=gr.Textbox(label="Input here", lines=2, placeholder="Input your text"),
                     outputs=gr.Label(label="Sentiment Analysis"),
                     examples=examples,
                     title=title,
                     article=article,)
intfc.launch(inline=False)


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