Upload app.py

app.py

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+#!/usr/local/bin/python3.9
+
+import streamlit as st
+
+import datetime
+print(datetime.datetime.now(),"Program start.")
+
+#import nltk
+#from nltk.corpus import stopwords
+import re
+#import pandas as pd
+#import nltk
+
+import pickle
+#import re
+#import string
+import numpy as np
+#import pandas as pd
+
+#from sklearn.preprocessing import LabelEncoder
+#from sklearn.model_selection import train_test_split
+
+#from tensorflow.keras.utils import to_categorical
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+#from tensorflow.keras.optimizers import Adam
+#from tensorflow.keras.models import Sequential
+#from tensorflow.keras.callbacks import EarlyStopping
+#from tensorflow.keras.layers import Dense, LSTM, Embedding, Bidirectional
+
+if "model_loaded" not in st.session_state:
+  with open('model.pkl', 'rb') as f:
+    clf2 = pickle.load(f)
+  st.session_state.model_loaded=clf2
+else:
+  clf2=st.session_state.model_loaded
+
+print(datetime.datetime.now(),"Finished import.")
+st.text("Hate Speech Detector")
+sentence=st.text_input('Sentence to analyze')
+
+labels=['Homophobe', 'Sexist', 'OtherHate', 'NotHate', 'Religion', 'Racist']
+
+# LIB LIB str_punc = string.punctuation.replace(',', '').replace("'",'')
+def clean(text):
+    global str_punc
+    text = re.sub(r'[^a-zA-Z ]', '', text)
+    text = text.lower()
+    return text 
+
+tokenizer = Tokenizer()
+# LIB LIB le = LabelEncoder()
+
+print(datetime.datetime.now(),"Program. About to load the model.")
+
+print(datetime.datetime.now(),"Program. Finished loading the model.")
+if sentence:
+  print("*************\nSentence:",sentence)
+  sentence = clean(sentence)
+  sentence = tokenizer.texts_to_sequences([sentence])
+  sentence = pad_sequences(sentence, maxlen=256, truncating='pre')
+  p=clf2.predict(sentence)
+  print("Prediction:",p)
+  a=np.argmax(p)
+  print("ArgMax:",a)
+  result=labels[a]
+  #result = le.inverse_transform(np.argmax(clf2.predict(sentence), axis=-1))[0]
+  proba =  np.max(clf2.predict(sentence))
+  print(f"{result} : {proba}\n\n")
+  st.text(f"{result}")
+  print(datetime.datetime.now(),"Program end.")