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import streamlit as st | |
from sklearn.feature_extraction.text import CountVectorizer | |
from textblob import TextBlob | |
from nltk.stem import PorterStemmer | |
from tensorflow.keras.models import load_model | |
import numpy as np | |
import nltk | |
nltk.download("punkt") | |
model=load_model("model.h5") | |
pr=PorterStemmer() | |
def lemmafn(text): | |
words=TextBlob(text).words | |
return [pr.stem(word) for word in words] | |
vect=CountVectorizer(ngram_range=(1,4),max_features=100000,analyzer=lemmafn) | |
st.title("Predicting Emotion of Text") | |
text=st.text_area("Your text") | |
if text is not None: | |
text=text.lower() | |
text=text.replace("[^\w\s]","") | |
text=text.replace("\n","") | |
text=text.replace("\d+","") | |
text=vect.fit_transform([text]) | |
if st.button("Predict"): | |
prediction=model.predict(text) | |
class_names=["Joy","Love","Anger","Sadness","Fear","Surprise"] | |
emotion=class_names[np.argmax(prediction)] | |
st.write(emotion) |