| """Streamlit demo app for the MindSignal triage assistant.""" |
|
|
| from __future__ import annotations |
|
|
| import streamlit as st |
|
|
| from mindsignal_utils import load_model_and_tokenizer, predict_text |
|
|
|
|
| st.set_page_config(page_title="MindSignal", page_icon="MS", layout="centered") |
|
|
| st.title("MindSignal: Mental Health Support Triage Assistant") |
| st.caption("Prototype classifier for short mental-health-related messages.") |
|
|
| st.warning( |
| "Disclaimer: MindSignal is not a medical diagnosis tool, therapist, crisis line, " |
| "or emergency service. If someone may be in immediate danger, contact local " |
| "emergency services or a crisis hotline right away." |
| ) |
|
|
|
|
| @st.cache_resource |
| def cached_model(): |
| """Load the model once so Streamlit interactions stay fast.""" |
|
|
| return load_model_and_tokenizer() |
|
|
|
|
| message = st.text_area( |
| "User message", |
| height=160, |
| placeholder="Type a short message here...", |
| ) |
|
|
| if st.button("Classify", type="primary"): |
| if not message.strip(): |
| st.error("Please enter a message to classify.") |
| else: |
| try: |
| tokenizer, model = cached_model() |
| prediction = predict_text(message, tokenizer, model) |
| except FileNotFoundError as error: |
| st.error(str(error)) |
| st.stop() |
|
|
| st.subheader("Prediction") |
| st.metric("Label", prediction.label) |
| st.metric("Confidence", f"{prediction.confidence:.2%}") |
|
|
| if prediction.used_safety_override: |
| st.info("Rule-based safety override was triggered by high-risk wording.") |
|
|
| if prediction.label == "escalation_required": |
| st.error( |
| "Safety warning: this message may need urgent escalation. " |
| "Encourage the person to contact emergency services, a trusted person, " |
| "or a crisis support line immediately." |
| ) |
|
|
