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sentiment_analysis.py
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sentiment_analysis.py
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import re, pickle
import numpy as np
import tensorflow as tf
from tensorflow import keras
# from tensorflow.keras.preprocessing.text import Tokenizer
from keras.models import load_model
# from tensorflow.keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import string
import pandas as pd
from flask import Flask, jsonify, request
from flask_restful import Api, Resource
from flasgger import Swagger, swag_from, LazyString, LazyJSONEncoder
from sqlalchemy import create_engine
app = Flask(__name__)
class CustomFlaskAppWithEncoder(Flask):
json_provider_class = LazyJSONEncoder
app = CustomFlaskAppWithEncoder(__name__)
app.json_encoder = LazyJSONEncoder
max_features = 100000
# tokenizer = Tokenizer(num_words=max_features,split=' ',lower=True)
# sentiment = ['positive', 'neutral', 'negative'] #yang bener gimana cuy
sentiment = ['negative', 'neutral', 'positive']
file = open("resources_of_rnn/x_pad_sequences2.pickle",'rb')
feature_file_from_rnn = pickle.load(file)
file.close()
file = open("resources_of_lstm/x_pad_sequences2.pickle",'rb')
feature_file_from_lstm = pickle.load(file)
file.close()
file = open('resources_of_lstm/tokenizer2.pickle', 'rb')
tokenizerLSTM = pickle.load(file)
file.close()
file = open('resources_of_rnn/tokenizer2.pickle', 'rb')
tokenizerRNN = pickle.load(file)
file.close()
rnn_model = load_model('model_of_rnn/modelRNN_new1.h5')
lstm_model = load_model('model_of_lstm/modelLSTM2.h5')
#------------------------------------------------------------------------------------
@swag_from("docs/rnn.yml", methods=["POST"])
@app.route('/inputFormRNN', methods=['POST'])
def main_RNN():
teks = request.form.get('teks')
teks = teks.lower()
teks = removePunctuation(teks)
teks = removeWhitespace(teks)
max_features = 100000
# tokenizer = Tokenizer(num_words=max_features, split=' ', lower=True)
tokenizerRNN.fit_on_texts([teks])
feature = tokenizerRNN.texts_to_sequences([teks])
feature = pad_sequences(feature, maxlen=feature_file_from_rnn.shape[1])
prediction = rnn_model.predict(feature)
get_sentiment = sentiment[np.argmax(prediction[0])]
json_response={
'status_code': 200,
'description': "Analisis Sentimen",
'teks': teks,
'sentiment': get_sentiment
}
response_data = jsonify(json_response)
return response_data
#------------------------------------------------------------------------------------
# UPLOAD_FOLDER = 'uploads'
ALLOWED_EXTENSIONS = {'csv'}
api = Api(app)
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
class UploadCSVRNN(Resource):
@swag_from("docs/csv_upload_rnn.yml", methods=["POST"])
def post(self):
if 'file' not in request.files:
return jsonify({'error': 'File tidak ditemukan'}), 400
file = request.files['file']
if file.filename == '':
return jsonify({'error': 'Tidak ada file terpilih'}), 400
if file and allowed_file(file.filename):
datasetOri = pd.read_csv(file,encoding='latin-1')
datasetOri = datasetOri.astype(str)
output = pd.DataFrame()
# datasentimen = datasetOri["sentiment"] #wajib dihapus ntar
dataset = datasetOri.iloc[:,0] #yang diprediksi kolom 0
dataset = dataset.to_frame(name="output")
for column_name in dataset.columns:
output[column_name] = dataset[column_name].apply(removePunctuation)
output[column_name] = output[column_name].str.lower()
output[column_name] = output[column_name].apply(removeWhitespace)
output["Pred_Sentiment"] = output[column_name].apply(predictRNN)
# feature = tokenizer.texts_to_sequences(output???)
# feature = pad_sequences(feature, maxlen=feature_file_from_rnn.shape[1])
# # prediction = rnn_model.predict(feature)
# get_sentiment = sentiment[np.argmax(prediction[0])]
# output = pd.concat([output, datasentimen], axis=1) #ini pas ngecek aja
result_json = output.to_json(orient='records')
engine = create_engine('sqlite:///outputRNN.db', echo=True)
sqlite_connection = engine.connect()
sqlite_table = "output_table"
output.to_sql(sqlite_table, sqlite_connection, if_exists='replace')
sqlite_connection.close()
response_data = {
'status_code': 200,
'message': 'File berhasil diunggah.',
'sqlite3_url': '/outputRNN.db',
'output': result_json
}
return jsonify(response_data), 200
else:
return jsonify({'error': 'Format file tidak valid'}), 400
app.add_url_rule('/uploadCSVRNN', view_func=UploadCSVRNN.as_view('upload_csv'))
# api.add_resource(UploadCSVRNN, '/uploadCSVRNN')
#------------------------------------------------------------------------------------
# UPLOAD_FOLDER = 'uploads'
ALLOWED_EXTENSIONS = {'csv'}
api = Api(app)
def allowed_file(filename):
return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
class UploadCSVLSTM(Resource):
@swag_from("docs/csv_upload_lstm.yml", methods=["POST"])
def post(self):
if 'file' not in request.files:
return jsonify({'error': 'File tidak ditemukan'}), 400
file = request.files['file']
if file.filename == '':
return jsonify({'error': 'Tidak ada file terpilih'}), 400
if file and allowed_file(file.filename):
datasetOri = pd.read_csv(file,encoding='latin-1')
datasetOri = datasetOri.astype(str)
output = pd.DataFrame()
dataset = datasetOri.iloc[:,0]
dataset = dataset.to_frame(name="output")
for column_name in dataset.columns:
output[column_name] = dataset[column_name].apply(removePunctuation)
output[column_name] = output[column_name].str.lower()
output[column_name] = output[column_name].apply(removeWhitespace)
output["Pred_Sentiment"] = output[column_name].apply(predictLSTM)
# feature = tokenizer.texts_to_sequences(output???)
# feature = pad_sequences(feature, maxlen=feature_file_from_rnn.shape[1])
# # prediction = rnn_model.predict(feature)
# get_sentiment = sentiment[np.argmax(prediction[0])]
result_json = output.to_json(orient='records')
engine = create_engine('sqlite:///outputLSTM.db', echo=True)
sqlite_connection = engine.connect()
sqlite_table = "output_table"
output.to_sql(sqlite_table, sqlite_connection, if_exists='replace')
sqlite_connection.close()
response_data = {
'status_code': 200,
'message': 'File berhasil diunggah.',
'sqlite3_url': '/outputLSTM.db',
'output': result_json
}
return jsonify(response_data), 200
else:
return jsonify({'error': 'Format file tidak valid'}), 400
app.add_url_rule('/uploadCSVLSTM', view_func=UploadCSVLSTM.as_view('upload_csv2'))
# api.add_resource(UploadCSVLSTM, '/uploadCSVLSTM')
#------------------------------------------------------------------------------------
swagger_template = dict(
info = {
'title': LazyString(lambda:'API Documentation for Sentiment Analysis'),
'version': LazyString(lambda:'1.0.0'),
'description': LazyString(lambda:'Dokumentasi API untuk Sentiment Analysis')
},
host = "127.0.0.1:5000/"
)
swagger_config = {
"headers": [],
"specs": [
{
"endpoint": 'docs',
"route": '/docs.json',
}
],
"static_url_path": "/flasgger_static",
"swagger_ui": True,
"specs_route": "/docs/"
}
swagger = Swagger(app, template=swagger_template,
config=swagger_config)
def removePunctuation(teks):
punctuation = string.punctuation
output = ''.join(char if char not in punctuation else ' ' for char in teks)
output = re.sub(r'[^a-zA-Z0-9\s]', ' ', output) #remove simbol aneh2
output = re.sub(r'\b[x]\w{2}\b', ' ', output)
return output
def removeWhitespace(teks):
output = ' '.join(teks.split())
return output
def predictRNN(teks):
max_features = 100000
# tokenizer = Tokenizer(num_words=max_features, split=' ', lower=True)
tokenizerRNN.fit_on_texts([teks])
feature = tokenizerRNN.texts_to_sequences([teks])
feature = pad_sequences(feature, maxlen=feature_file_from_rnn.shape[1])
prediction = rnn_model.predict(feature)
get_sentiment = sentiment[np.argmax(prediction[0])]
return (get_sentiment)
def predictLSTM(teks):
max_features = 100000
# tokenizer = Tokenizer(num_words=max_features, split=' ', lower=True)
tokenizerLSTM.fit_on_texts([teks])
feature = tokenizerLSTM.texts_to_sequences([teks])
feature = pad_sequences(feature, maxlen=feature_file_from_lstm.shape[1])
prediction = lstm_model.predict(feature)
get_sentiment = sentiment[np.argmax(prediction[0])]
return (get_sentiment)
@swag_from("docs/lstm.yml", methods=["POST"])
@app.route('/inputFormLSTM',methods=['POST'])
def main_lstm():
teks = request.form.get('teks')
teks = teks.lower()
teks = removePunctuation(teks)
teks = removeWhitespace(teks)
max_features = 100000
# tokenizer = Tokenizer(num_words=max_features, split=' ', lower=True)
tokenizerLSTM.fit_on_texts([teks])
feature = tokenizerLSTM.texts_to_sequences([teks])
feature = pad_sequences(feature, maxlen=feature_file_from_lstm.shape[1])
prediction = lstm_model.predict(feature)
get_sentiment = sentiment[np.argmax(prediction[0])]
json_response={
'status_code': 200,
'description': "Analisis Sentimen",
'teks': teks,
'sentiment': get_sentiment
}
response_data = jsonify(json_response)
return response_data
if __name__ == '__main__':
app.run() #debug=True