Update app.py

app.py

@@ -1,15 +1,11 @@
 import streamlit as st
 from keras.models import load_model
-import numpy as np
-import tensorflow as tf
 import nltk
 import re
 from nltk.corpus import stopwords
 from nltk.tokenize import TweetTokenizer
-from nltk.tokenize import word_tokenize
 from tensorflow.keras.preprocessing.text import Tokenizer
 from tensorflow.keras.preprocessing.sequence import pad_sequences
-
 import subprocess
 
 # Command to execute
@@ -24,8 +20,9 @@ except subprocess.CalledProcessError as e:
 
 # Load the LSTM model
 model_path = "model.h5"  # Set your model path here
-lstm_model = load_lstm_model(model_path)
 
+def load_lstm_model(model_path):
+    return load_model(model_path)
 
 def clean_text(text):
     # Remove stopwords
@@ -57,7 +54,6 @@ def clean_text(text):
     
     return text
 
-
 def preprocess_text(text):
     # Clean the text
     cleaned_text = clean_text(text)
@@ -70,13 +66,8 @@ def preprocess_text(text):
     
     return padded_sequences
 
-# Function to load the saved LSTM model
-@st.cache(allow_output_mutation=True)
-def load_lstm_model(model_path):
-    return load_model(model_path)
-
 # Function to predict hate speech
-def predict_hate_speech(text):
+def predict_hate_speech(text, lstm_model):
     # Preprocess the text
     padded_sequences = preprocess_text(text)
     prediction = lstm_model.predict(padded_sequences)
@@ -91,6 +82,8 @@ def main():
 
     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)
             if prediction > 0.5: