Spaces:
Sleeping
Sleeping
File size: 3,176 Bytes
408900f |
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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
import streamlit as st
from keras.models import load_model
import nltk
import re
from nltk.tokenize import TweetTokenizer
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import subprocess
import numpy as np
# Download NLTK stopwords if not already downloaded
try:
nltk.data.find('corpora/stopwords')
except LookupError:
nltk.download('stopwords')
# Additional imports
from nltk.corpus import stopwords
# Download NLTK punkt tokenizer if not already downloaded
try:
nltk.data.find('tokenizers/punkt/PY3/english.pickle')
except LookupError:
nltk.download('punkt')
# Additional imports
from nltk.tokenize import word_tokenize
# Load the LSTM model
model_path = "./my_model.h5" # Set your model path here
def load_lstm_model(model_path):
return load_model(model_path)
def clean_text(text):
# Remove stopwords
stop_words = set(stopwords.words('english'))
words = word_tokenize(text)
filtered_words = [word for word in words if word not in stop_words]
# Remove Twitter usernames
text = re.sub(r'@\w+', '', ' '.join(filtered_words))
# Remove URLs
text = re.sub(r'http\S+', '', text)
# Tokenize using TweetTokenizer
tokenizer = TweetTokenizer(preserve_case=True)
text = tokenizer.tokenize(text)
# Remove hashtag symbols
text = [word.replace('#', '') for word in text]
# Remove short words
text = ' '.join([word.lower() for word in text if len(word) > 2])
# Remove digits
text = re.sub(r'\d+', '', text)
# Remove non-alphanumeric characters
text = re.sub(r'[^a-zA-Z\s]', '', text)
return text
def preprocess_text(text):
# Clean the text
cleaned_text = clean_text(text)
# Tokenize and pad sequences
token = Tokenizer()
token.fit_on_texts([cleaned_text])
text_sequences = token.texts_to_sequences([cleaned_text])
padded_sequences = pad_sequences(text_sequences, maxlen=100)
return padded_sequences
# Function to predict hate speech
def predict_hate_speech(text, lstm_model):
# Preprocess the text
padded_sequences = preprocess_text(text)
prediction = lstm_model.predict(padded_sequences)
return prediction
# Main function to run the Streamlit app
def main():
# Set up Streamlit UI
st.title("Hate Speech Detection")
st.write("Enter text below to detect hate speech:")
input_text = st.text_area("Input Text", "")
if st.button("Detect Hate Speech"):
if input_text:
# Load the model
lstm_model = load_lstm_model(model_path)
# Predict hate speech
prediction = predict_hate_speech(input_text, lstm_model)
# Convert the list to a numpy array
arr = np.array(prediction[0])
max_index = np.argmax(arr)
if max_index == 1:
#negative
st.error("Hate Speech Detected")
else:
st.success("No Hate Speech Detected")
else:
st.warning("Please enter some text")
# Run the app
if __name__ == "__main__":
main()
|