app.py

# Use a pipeline as a high-level helper
from transformers import pipeline
import streamlit as st

pipe_1 = pipeline("text-classification", model="mavinsao/mi-roberta-base-finetuned-mental-illness")
pipe_2 = pipeline("text-classification", model="mavinsao/roberta-mental-finetuned")


def ensemble_predict(text):
    results_1 = pipe_1(text)
    results_2 = pipe_2(text)

    # Implement your chosen ensemble strategy here. 
    # Example with simple averaging: 
    ensemble_scores = {}
    for result in results_1 + results_2:
        label = result['label']
        score = result['score']
        ensemble_scores[label] = ensemble_scores.get(label, 0) + score / 2  

    predicted_label = max(ensemble_scores, key=ensemble_scores.get)
    confidence = ensemble_scores[predicted_label] 

    return predicted_label, confidence

# Streamlit app
st.title('Mental Illness Prediction')

# Input text area for user input
sentence = st.text_area("Enter the long sentence to predict your mental illness state:")

if st.button('Predict'):
    # ... (input validation ... )
    predicted_label, confidence = ensemble_predict(sentence)
    st.write("Predicted label:", predicted_label)
    st.write("Confidence:", confidence)