Spaces:
Sleeping
Sleeping
File size: 1,662 Bytes
c985b4c 487fb9f c985b4c 02c45ef c985b4c 02c45ef c985b4c 02c45ef c985b4c 02c45ef c985b4c 02c45ef c985b4c 02c45ef 487fb9f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
from flask import Flask, request, jsonify
from flask_cors import CORS
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from api.predict import predict_review, load_resources
app = Flask(__name__)
CORS(app)
print("loading models on startup...")
try:
load_resources()
print("models loaded")
except Exception as e:
print(f"couldnt preload models: {e}")
@app.route('/health', methods=['GET'])
def health():
return jsonify({"status": "ok"}), 200
@app.route('/predict', methods=['POST'])
def predict():
try:
data = request.get_json()
if not data or 'text' not in data:
return jsonify({"error": "missing 'text' field"}), 400
reviewText = data['text']
if not isinstance(reviewText, str):
return jsonify({"error": "'text' must be a string"}), 400
if len(reviewText.strip()) == 0:
return jsonify({"error": "text cannot be empty"}), 400
result = predict_review(reviewText)
if 'error' in result:
return jsonify({"error": result['error']}), 400
return jsonify({
"prediction": result['prediction'],
"confidence": result['confidence'],
"is_fake": result['is_fake'],
"length_category": result.get('length_category'),
"token_count": result.get('token_count')
}), 200
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == '__main__':
print("starting api server")
app.run(host='0.0.0.0', port=7860, debug=False) |