File size: 1,015 Bytes
0566214 be55b7c 0566214 be55b7c 0566214 be55b7c 0566214 be55b7c 0566214 be55b7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | import streamlit as st
from tensorflow.keras.models import load_model
from sklearn.feature_extraction.text import CountVectorizer
from textblob import TextBlob
from nltk.stem import PorterStemmer
import numpy as np
pr=PorterStemmer()
def lemmafn(text):
words=TextBlob(text).words
return [pr.stem(word) for word in words]
vect=CountVectorizer(stop_words="english",ngram_range=(1,3),max_features=10000)
model=load_model("model.h5")
st.title("Predict Comments Toxicity")
comment=st.text_area("Comment")
if comment is not None:
coment=comment.lower()
comment=comment.replace("[^\w\s]","",)
comment=comment.replace("\d+","")
comment=comment.replace("\n","")
comment=[comment]
if st.button("Predict"):
data=vect.fit_transform(comment)
prediction=model.predict(data)
predicted_class=np.argmax(prediction)
st.write("1st->Toxicity,2nd->Severe Toxicity,3rd->Obscene,4th->Is it a Threat?,5th->Insult,6th->Identity Hate",predicted_class) |