Create app.py

app.py

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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()