Spaces:
Runtime error
Runtime error
File size: 1,594 Bytes
f8a43b2 |
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 |
from sentence_transformers import SentenceTransformer
from flask import Flask, request, jsonify
from flask_cors import CORS # Import CORS
app = Flask(__name__)
CORS(app) # Enable CORS for all routes
# Load the model once when the application starts
# This is efficient as it avoids reloading on every request.
print("Loading sentence-transformer model...")
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
print("Model loaded successfully.")
@app.route('/', methods=['GET'])
def health_check():
"""A simple endpoint to check if the service is running."""
return jsonify({
'status': 'ok',
'model': 'sentence-transformers/all-MiniLM-L6-v2'
})
@app.route('/embed', methods=['POST'])
def embed_text():
"""The main endpoint to generate embeddings."""
data = request.json
if not data or 'text' not in data:
return jsonify({'error': 'No text provided in JSON body'}), 400
text = data.get('text')
if not isinstance(text, str) or not text.strip():
return jsonify({'error': 'Text must be a non-empty string'}), 400
try:
# Generate embedding
embedding = model.encode(text) # No need to wrap in a list for a single string
return jsonify({
'embedding': embedding.tolist(),
'model': 'all-MiniLM-L6-v2',
'dimension': len(embedding)
})
except Exception as e:
print(f"Error during embedding: {e}")
return jsonify({'error': 'Failed to generate embedding'}), 500
|