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# Importing of Libaries | |
import streamlit as st | |
from sklearn.feature_extraction.text import CountVectorizer | |
from sklearn.preprocessing import LabelEncoder | |
import pickle | |
pickled_model = pickle.load(open('detector.model', 'rb')) | |
loaded_vectorizer = pickle.load(open('vectorizer.pickle', 'rb')) | |
label_encoder = pickle.load(open('label_encoder', 'rb')) | |
# Creating a function to be used in streamlit | |
def main(): | |
st.sidebar.header("Language Detector") | |
st.sidebar.text("This is a web app that tell contain 20 language trained with a model,i.e the app can different 20 languages") | |
st.sidebar.header("just fill in the information below") | |
st.sidebar.text("Naive Bayes model was used") | |
pred_review_text=st.text_input("Enter a sentence in a particular language") | |
# A conditional statement to display the result using Streamlit | |
if st.button("Detect"): | |
lang=pickled_model.predict(loaded_vectorizer.transform([pred_review_text])) | |
lang=label_encoder.inverse_transform(lang) | |
st.write(lang[0]) | |
# This is a lamguage detector |