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from tensorflow import keras |
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import streamlit as st |
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import altair as alt |
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import plotly.express as px |
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import pandas as pd |
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import numpy as np |
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from datetime import datetime |
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import joblib |
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from google.cloud import storage |
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from tempfile import TemporaryFile |
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from csv import writer |
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from datetime import datetime |
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import os |
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from dotenv import load_dotenv |
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from nltk.stem import PorterStemmer |
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from nltk.corpus import stopwords |
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import re |
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from tensorflow import keras |
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import numpy as np |
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import pandas as pd |
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from tensorflow.keras.preprocessing.sequence import pad_sequences |
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import nltk |
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from tensorflow.keras.preprocessing.text import one_hot |
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import re |
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from nltk.corpus import stopwords |
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from nltk.stem import PorterStemmer |
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import pickle |
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pkl_file = open('m_lb.pkl', 'rb') |
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le_departure = pickle.load(pkl_file) |
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pkl_file.close() |
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model = keras.models.load_model('m_odel.h5') |
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nltk.download('stopwords') |
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stopwords = set(nltk.corpus.stopwords.words('english')) |
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vocabSize = 11000 |
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max_len = 1160 |
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load_dotenv() |
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def predict_emotions(sentence): |
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sentence = sentence_cleaning(sentence) |
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result = le_departure.inverse_transform( |
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np.argmax(model.predict(sentence), axis=-1))[0] |
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proba = np.max(model.predict(sentence)) |
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print(result) |
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print(proba) |
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return result, proba |
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def sentence_cleaning(sentence): |
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"""Pre-processing sentence for prediction""" |
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stemmer = PorterStemmer() |
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corpus = [] |
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text = re.sub("[^a-zA-Z]", " ", sentence) |
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text = text.lower() |
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text = text.split() |
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text = [stemmer.stem(word) for word in text if word not in stopwords] |
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text = " ".join(text) |
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corpus.append(text) |
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one_hot_word = [one_hot(input_text=word, n=vocabSize) for word in corpus] |
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pad = pad_sequences(sequences=one_hot_word, maxlen=max_len, padding='pre') |
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return pad |
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def main(): |
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st.title("Emotion Classifier") |
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menu = ["Home", "Monitor"] |
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choice = st.sidebar.selectbox("Menu", menu) |
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if choice == "Home": |
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st.subheader("Home-Emotion In Text") |
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with st.form(key='emotion_clf_form'): |
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raw_text = st.text_area("Type Here") |
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submit_text = st.form_submit_button(label='Submit') |
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if submit_text: |
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col1, col2 = st.beta_columns(2) |
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res, proba = predict_emotions(raw_text) |
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with col1: |
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st.success("Original Text") |
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st.write(raw_text) |
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st.success("Prediction") |
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st.write("{}:{}".format(res, proba)) |
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st.write("Confidence:{}".format(proba)) |
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else: |
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st.subheader("About") |
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if __name__ == '__main__': |
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main() |
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