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import streamlit as st
import pandas as pd
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from functions.text_preprocessed import text_preprocessing
from tensorflow.keras.models import load_model
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
def run():
st.title('Predict')
st.write('You can use our model here by inputting your text (review) here:')
# -------------------------------------------------------------------------
# Dataframe
data = pd.DataFrame()
# -------------------------------------------------------------------------
# App name
data['app'] = [st.text_input('Application name')]
# -------------------------------------------------------------------------
# Category
data['category'] = [st.text_input('Application category')]
# -------------------------------------------------------------------------
# Rating
data['rating'] = [round(st.slider('Rating', min_value=0.0, max_value=5.0), 1)]
# -------------------------------------------------------------------------
# Reviews
data['reviews'] = [st.number_input('Total review count', min_value=0)]
# -------------------------------------------------------------------------
# Size
data['size'] = [st.text_input('File size')]
# -------------------------------------------------------------------------
# Installs
data['installs'] = [st.number_input('Total installs', min_value=0)]
# -------------------------------------------------------------------------
# Type
data['type'] = [st.text_input('Paid / Free')]
# -------------------------------------------------------------------------
# Price
data['price'] = [st.number_input('Application price', min_value = 0.00)]
# -------------------------------------------------------------------------
# Content rating
data['content_rating'] = [st.text_input('Age rating')]
# -------------------------------------------------------------------------
# Genres
data['genres'] = [st.text_input("Genres").split(',')]
st.caption("Separate by ',' if multiple genres")
# -------------------------------------------------------------------------
# Last updated
data['last_updated'] = [st.date_input('Last updated')]
# -------------------------------------------------------------------------
# Current version
data['current_ver'] = [st.text_input('Current version')]
# -------------------------------------------------------------------------
# Android version
data['android_ver'] = [st.text_input('Android version')]
# -------------------------------------------------------------------------
# Review
review = st.text_input('Application review (in English)')
## Stop words
stop_words = set(stopwords.words('english'))
## Lemmatizer
lemmatizer = WordNetLemmatizer()
## Processed text
text_processed = text_preprocessing(review, lemmatizer, stop_words)
data['translated_review'] = [review]
data['text_processed'] = [text_processed]
# -------------------------------------------------------------------------
# User data
st.dataframe(data.T, width=800, height=565)
# -------------------------------------------------------------------------
# Prediction
if st.button('Predict'):
model = load_model('lstm')
sentiment_pred = model.predict(data['text_processed'])
# st.write(sentiment_pred)
if sentiment_pred > 1.5:
st.write('Positive Review')
elif (sentiment_pred < 1.5) & (sentiment_pred >= 1.0):
st.write('Negative Review')
else:
st.write('Neutral Review') |