from flask import Flask, request, jsonify, send_from_directory
from flask_cors import CORS
import joblib
import requests
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

app = Flask(__name__, static_folder='.', static_url_path='')
CORS(app)  # Enable CORS for all routes

# Load model and vectorizer
try:
    model = joblib.load("spam_model.pkl")
    vectorizer = joblib.load("vectorizer.pkl")
    print("Model and vectorizer loaded successfully.")
except Exception as e:
    print(f"Error loading model: {e}")
    model = None
    vectorizer = None

N8N_URL = os.getenv("N8N_WEBHOOK_URL")

@app.route('/')
def serve_frontend():
    return send_from_directory('.', 'index.html')

@app.route('/<path:path>')
def serve_static(path):
    return send_from_directory('.', path)

@app.route('/predict', methods=['POST'])
def predict():
    if not model or not vectorizer:
        return jsonify({"error": "Model not loaded"}), 500

    data = request.get_json(force=True)
    message = data.get('message', '')

    if not message:
        return jsonify({"error": "No message provided"}), 400

    # Vectorize input
    vectorized_message = vectorizer.transform([message])
    
    # Predict
    prediction = model.predict(vectorized_message)[0]
    
    # Get probabilities
    probabilities = model.predict_proba(vectorized_message)
    confidence = max(probabilities[0])

    result = {
        "prediction": prediction,
        "confidence": float(confidence),
        "message": message
    }

    # Send to N8N (Fire and forget, or wait for response?)
    if N8N_URL:
        try:
            print(f"Sending to N8N: {N8N_URL}")
            requests.post(N8N_URL, json=result, timeout=5) # Add timeout
        except Exception as e:
            print(f"Failed to send to N8N: {e}")

    return jsonify(result)

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=7860) # Port 7860 is standard for HF Spaces