app.py

import streamlit as st
import joblib
from sentence_transformers import SentenceTransformer

# Load the pre-trained embedding model
@st.cache_resource  # Cache the embedding model to save loading time
def load_embedding_model():
    return SentenceTransformer('neuml/pubmedbert-base-embeddings')

# Load the MLP model
@st.cache_resource  # Cache the loaded model
def load_mlp_model():
    with open("MLP.pkl", "rb") as file:
        return joblib.load(file)

# Embed text
def get_embeddings(title, abstract, embedding_model):
    # Concatenate title and abstract
    combined_text = title + " " + abstract
    return embedding_model.encode(combined_text)

# Main Streamlit app
def main():
    st.title("MLP Predictor for Titles and Abstracts")
    
    # Input fields
    title = st.text_input("Enter the Title:")
    abstract = st.text_area("Enter the Abstract:")
    
    # Load models
    embedding_model = load_embedding_model()
    mlp_model = load_mlp_model()
    
    # Predict button
    if st.button("Predict Label"):
        if title.strip() == "" or abstract.strip() == "":
            st.error("Both Title and Abstract are required!")
        else:
            # Get embeddings
            embeddings = get_embeddings(title, abstract, embedding_model)
            
            # Make prediction
            prediction = mlp_model.predict([embeddings])[0]  # Input should be a 2D array
            
            # Display result
            st.success(f"The predicted label is: {prediction}")

if __name__ == "__main__":
    main()