Upload app.py

app.py

@@ -1,7 +1,9 @@
-#import os
-#os.system('pip install nltk')
-#os.system('pip install sklearn')
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jun  6 20:56:08 2022
 
+@author: User
+"""
 import nltk
 
 nltk.download('punkt')
@@ -11,11 +13,7 @@ nltk.download('omw-1.4')
 
 # importing relevant python packages
 import streamlit as st
-import pandas as pd
-import numpy as np
-import pickle
 import joblib
-from PIL import Image
 # preprocessing
 import re
 import string
@@ -24,9 +22,6 @@ from nltk.corpus import stopwords
 from nltk.stem import WordNetLemmatizer 
 from sklearn.feature_extraction.text import TfidfVectorizer
 # modeling
-from sklearn import svm
-# sentiment analysis
-
 
 # creating page sections
 site_header = st.container()
@@ -67,13 +62,13 @@ with model_results:
 
         # instantiating count vectorizor
         tfidf = TfidfVectorizer(stop_words=stop_words)
-        X_train = joblib.load(open('resources/X_train.pickel', 'rb'))
+        X_train = joblib.load(open('X_train.pickel', 'rb'))
         X_test = lemmatized_output
         X_train_count = tfidf.fit_transform(X_train)
         X_test_count = tfidf.transform(X_test)
 
         # loading in model
-        final_model = joblib.load(open('resources/final_bayes.pickle', 'rb'))
+        final_model = joblib.load(open('final_bayes.pickle', 'rb'))
 
         # apply model to make predictions
         prediction = final_model.predict(X_test_count[0])
@@ -82,4 +77,4 @@ with model_results:
             st.subheader('**Not Hate Speech**')
         else:
             st.subheader('**Hate Speech**')
-        st.text('')
+        st.text('')