app.py

from flask import Flask, render_template, request, jsonify
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

app = Flask(__name__)

# Load model and tokenizer
MODEL_NAME = "viccon23/STU-Injection-aegis"
print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
print("Model loaded successfully!")

def classify_prompt(text):
    """Classify if a prompt is safe or unsafe"""
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    
    with torch.no_grad():
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
        predicted_class = torch.argmax(predictions, dim=-1).item()
        confidence = predictions[0][predicted_class].item()
    
    # Assuming class 0 is safe and class 1 is unsafe
    label = "Safe" if predicted_class == 0 else "Unsafe"
    
    return {
        "label": label,
        "confidence": round(confidence * 100, 2),
        "predicted_class": predicted_class
    }

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/classify', methods=['POST'])
def classify():
    data = request.get_json()
    prompt = data.get('prompt', '')
    
    if not prompt:
        return jsonify({"error": "No prompt provided"}), 400
    
    result = classify_prompt(prompt)
    return jsonify(result)

if __name__ == '__main__':
    import os
    port = int(os.environ.get('PORT', 7860))
    app.run(debug=False, host='0.0.0.0', port=port)