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# -*- coding: utf-8 -*-
import os
from flask import Flask, request, jsonify
from flask_cors import CORS
from explain_model import SentenceBasedTextDetector
from ensemble_image_detector import EnsembleImageDetector
import traceback
app = Flask(__name__)
CORS(app)
# Load models
TEXT_MODEL = "Hello-SimpleAI/chatgpt-detector-roberta"
IMAGE_MODEL = "Organika/sdxl-detector"
print("Starting server and loading models...")
text_detector = SentenceBasedTextDetector(TEXT_MODEL)
image_detector = EnsembleImageDetector()
print("Server ready!")
@app.route('/', methods=['GET'])
def home():
"""Home endpoint"""
return jsonify({
'status': 'ok',
'message': 'DetectAI API is running',
'endpoints': {
'health': '/health',
'text': '/analyze',
'image': '/analyze-image'
}
})
@app.route('/health', methods=['GET'])
def health():
"""Check if server is running"""
return jsonify({
'status': 'ok',
'message': 'Server is running',
'text_model': TEXT_MODEL,
'image_model': IMAGE_MODEL
})
@app.route('/analyze', methods=['POST'])
def analyze_text():
"""Analyze text and return prediction"""
try:
data = request.get_json()
if not data or 'text' not in data:
return jsonify({'error': 'No text provided'}), 400
text = data['text'].strip()
if len(text) == 0:
return jsonify({'error': 'Text is empty'}), 400
if len(text) < 10:
return jsonify({'error': 'Text is too short (minimum 10 characters)'}), 400
print(f"Analyzing text ({len(text)} characters)...")
result = text_detector.explain(text)
print(f"Result: {result['prediction']} ({result['ai_probability']}%)")
return jsonify(result)
except Exception as e:
print(f"Error: {str(e)}")
traceback.print_exc()
return jsonify({'error': 'Analysis failed'}), 500
@app.route('/analyze-image', methods=['POST'])
def analyze_image():
"""Analyze image and return prediction"""
try:
data = request.get_json()
if not data or 'image' not in data:
return jsonify({'error': 'No image provided'}), 400
image_base64 = data['image']
print("Analyzing image...")
result = image_detector.detect_from_base64(image_base64)
print(f"Result: {result['prediction']} ({result['ai_probability']}%)")
return jsonify(result)
except Exception as e:
print(f"Error: {str(e)}")
traceback.print_exc()
return jsonify({'error': 'Analysis failed'}), 500
if __name__ == '__main__':
PORT = int(os.environ.get('PORT', 5000))
print("\n" + "=" * 70)
print("DetectAI API Server")
print("=" * 70)
print(f"Text Model: {TEXT_MODEL}")
print(f"Image Model: {IMAGE_MODEL}")
print(f"Server running on port: {PORT}")
print("=" * 70 + "\n")
app.run(host='0.0.0.0', port=PORT, debug=False)
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