suicidality_model.py

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from textblob import TextBlob
import torch

# Load the Hugging Face model
model_name = "sentinet/suicidality"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

labels = ["non-suicidal", "suicidal"]

def sentiment_score(text):
    """Calculate basic sentiment polarity (-1 = negative, +1 = positive)."""
    blob = TextBlob(text)
    return round(blob.sentiment.polarity, 3)

def predict_suicidality(text: str):
    """Predict suicidality and sentiment for the given (English) text."""
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=1)
    pred_class = torch.argmax(probs, dim=1).item()
    confidence = probs[0][pred_class].item()

    sentiment = sentiment_score(text)  # ✅ this is now correctly scoped

    return {
        "label": labels[pred_class],
        "confidence": round(confidence, 3),
        "sentiment": sentiment
    }