File size: 2,016 Bytes
291793f c12c8cd 29448e1 a681e0a bb77b43 35bd57e 0d00d6d bb77b43 3f7ec6a 0d00d6d bb77b43 3f7ec6a bb77b43 0d00d6d bb77b43 3f7ec6a 0d00d6d a681e0a ff694a9 a681e0a bb77b43 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 |
from transformers import pipeline
import streamlit as st
import streamlit.components.v1 as components
# Load the models
pipe_1 = pipeline("text-classification", model="mavinsao/roberta-base-finetuned-mental-health")
pipe_2 = pipeline("text-classification", model="mavinsao/mi-roberta-base-finetuned-mental-health")
# Function for ensemble prediction
def ensemble_predict(text):
# Store results from each model
results_1 = pipe_1(text)
results_2 = pipe_2(text)
# Initialize a dictionary with all potential labels to ensure they are considered
ensemble_scores = {}
# Add all labels from the first model's output
for result in results_1:
ensemble_scores[result['label']] = 0
# Add all labels from the second model's output
for result in results_2:
ensemble_scores[result['label']] = 0
# Aggregate scores from both models
for results in [results_1, results_2]:
for result in results:
label = result['label']
score = result['score']
ensemble_scores[label] += score / 2 # Averaging the scores
# Determine the predicted label and confidence
predicted_label = max(ensemble_scores, key=ensemble_scores.get)
confidence = ensemble_scores[predicted_label] # Ensemble confidence
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'):
# Perform the prediction
predicted_label, confidence = ensemble_predict(sentence)
# CSS injection to target the labels
st.markdown("""
<style>
div[data-testid="metric-container"] {
font-weight: bold;
font-size: 18px; /* Adjust the font size as desired */
}
</style>
""", unsafe_allow_html=True)
# Display the result
st.write("Result:", predicted_label)
st.write("Confidence:", confidence)
|