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import streamlit as st
from keras.models import load_model
from tensorflow.keras.preprocessing.text import tokenizer_from_json
import contractions
import re
from nltk.corpus import stopwords
import json
# Set page configuration
st.set_page_config(page_title="Mental Health Classification")
# Page title
st.title("Mental Health Classification")
# Load the tokenizer (make sure the tokenizer file is in the correct path)
def load_tokenizer():
with open('tokenizer.json') as f:
tokenizer_json = json.load(f)
return tokenizer_from_json(tokenizer_json)
# Preprocess text function
def preprocess_text(input_text, tokenizer):
text = contractions.fix(input_text)
text = re.sub(r"[^a-z\s]", "", text)
text = text.lower()
# Tokenize the words
words = text.split()
# Remove stopwords
stop_words = set(stopwords.words('english'))
words = [word for word in words if word not in stop_words]
clean_text = " ".join(words)
# Convert to sequences
sequences = tokenizer.texts_to_sequences([clean_text])
return sequences
def main():
# Text input for mental state
input_text = st.text_input("Enter the Mental state here...")
# Submit button
submit_button = st.button("Classify")
if submit_button and input_text:
# Load the model and tokenizer
model = load_model("mental_health_model.h5")
tokenizer = load_tokenizer()
# Preprocess the input text
processed_text = preprocess_text(input_text, tokenizer)
# Make prediction
response = model.predict(processed_text)
predicted_class = response.argmax(axis=-1)
# Display the prediction result
st.write("Predicted Mental State:", predicted_class)
if __name__ == "__main__":
main()
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