AI Model Serving Framework
This tool provides a simple and flexible way to deploy and serve AI models in production environments. It supports various model formats and frameworks, including TensorFlow, PyTorch, and scikit-learn. The tool allows developers to easily integrate their models with web applications, APIs, or other services, making it easier to deploy and manage AI models in production.
Installation
To install the required packages, run the following command:
pip install flask tensorflow torch scikit-learnUsage
To use the framework, run the following command:
python model_serve.py --model_path <model_path> --framework <framework> --endpoint <endpoint>Replace <model_path> with the path to your model file, <framework> with the framework type (tensorflow, pytorch, scikit-learn), and <endpoint> with the API endpoint.
Source Code
import argparse
import json
from flask import Flask, request, jsonify
import tensorflow as tf
import torch
from sklearn import ensemble
app = Flask(__name__)
def load_model(model_path, framework):
if framework == 'tensorflow':
return tf.keras.models.load_model(model_path)
elif framework == 'pytorch':
return torch.load(model_path)
elif framework == 'scikit-learn':
return ensemble.RandomForestClassifier() # dummy model for testing
else:
raise ValueError('Unsupported framework')
def serve_model(model, endpoint, framework):
def predict():
data = request.get_json()
if framework == 'tensorflow':
predictions = model.predict(data)
elif framework == 'pytorch':
predictions = model(torch.tensor(data))
elif framework == 'scikit-learn':
predictions = model.predict(data)
return jsonify({'predictions': predictions.tolist()})
app.add_url_rule(endpoint, endpoint, predict, methods=['POST'])
return app
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='AI Model Serving Framework')
parser.add_argument('--model_path', type=str, required=True, help='Path to the model file')
parser.add_argument('--framework', type=str, required=True, help='Framework type (tensorflow, pytorch, scikit-learn)')
parser.add_argument('--endpoint', type=str, required=True, help='API endpoint')
args = parser.parse_args()
model = load_model(args.model_path, args.framework)
app = serve_model(model, args.endpoint, args.framework)
app.run(debug=True)README
AI Model Serving Framework
This framework provides a simple and flexible way to deploy and serve AI models in production environments. It supports various model formats and frameworks, including TensorFlow, PyTorch, and scikit-learn.
Installation
To install the required packages, run the following command:
pip install flask tensorflow torch scikit-learnUsage
To use the framework, run the following command:
python model_serve.py --model_path <model_path> --framework <framework> --endpoint <endpoint>Replace <model_path> with the path to your model file, <framework> with the framework type (tensorflow, pytorch, scikit-learn), and <endpoint> with the API endpoint.
Testing
To run the tests, use the following command:
pytest test_model_serve.pyCommunity
Downloads
···
Rate this tool
No ratings yet — be the first!
Details
- Tool Name
- model_serve
- Category
- AI Model Serving
- Generated
- August 12, 2026
- Tests
- Passing ✅
- Fix Loops
- 3
Quick Install
Clone just this tool:
git clone --depth 1 --filter=blob:none --sparse \ https://github.com/ptulin/autoaiforge.git cd autoaiforge git sparse-checkout set generated_tools/2026-08-12/model_serve cd generated_tools/2026-08-12/model_serve pip install -r requirements.txt 2>/dev/null || true python model_serve.py