File size: 2,098 Bytes
291793f c12c8cd 29448e1 a681e0a 26f04af 0d00d6d 29448e1 4d1b284 29448e1 3f7ec6a 0d00d6d 3f7ec6a 0d00d6d 3f7ec6a 0d00d6d a681e0a ff694a9 a681e0a c33cdf2 a681e0a c33cdf2 807d96d c33cdf2 |
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 63 64 65 66 |
# Use a pipeline as a high-level helper
from transformers import pipeline
import streamlit as st
import streamlit.components.v1 as components
pipe_1 = pipeline("text-classification", model="mavinsao/mi-roberta-mental-finetuned")
pipe_2 = pipeline("text-classification", model="mavinsao/roberta-mental-finetuned")
# Streamlit app with background image
def st_display_background(image_url):
st_style = f"""
<style>
body {{
background-image: url("{image_url}") !important;
background-size: cover;
}}
</style>
"""
st.markdown(st_style, unsafe_allow_html=True)
image_url = "https://images.unsplash.com/photo-1504701954957-2010ec3bcec1?q=80&w=1974&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
def ensemble_predict(text):
# Store results from each model
results_1 = pipe_1(text)
results_2 = pipe_2(text)
ensemble_scores = {}
for results in [results_1, results_2]: # Iterate through predictions
for result in results:
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] # 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:")
st_display_background(image_url)
if st.button('Predict'):
# ... (input validation ... )
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)
st.write("Result:", predicted_label)
st.write("Confidence:", confidence)
|