Instructions to use Manav2op/EMOTIA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manav2op/EMOTIA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Manav2op/EMOTIA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| import requests | |
| import time | |
| import subprocess | |
| import signal | |
| import os | |
| import sys | |
| def test_api(): | |
| # Start the server | |
| print("Starting FastAPI server...") | |
| server_process = subprocess.Popen([ | |
| sys.executable, "-m", "uvicorn", "backend.main:app", | |
| "--host", "0.0.0.0", "--port", "8000", "--log-level", "warning" | |
| ], cwd=os.getcwd()) | |
| # Wait for server to start | |
| time.sleep(3) | |
| try: | |
| base_url = "http://localhost:8000" | |
| # Test root endpoint | |
| print("Testing root endpoint...") | |
| response = requests.get(f"{base_url}/") | |
| print(f"Status: {response.status_code}") | |
| print(f"Response: {response.json()}") | |
| # Test health endpoint | |
| print("Testing health endpoint...") | |
| response = requests.get(f"{base_url}/health") | |
| print(f"Status: {response.status_code}") | |
| print(f"Response: {response.json()}") | |
| # Test analyze/frame endpoint (should return validation error) | |
| print("Testing analyze/frame endpoint...") | |
| response = requests.post(f"{base_url}/analyze/frame") | |
| print(f"Status: {response.status_code}") | |
| print(f"Response: {response.text}") | |
| print("All tests passed!") | |
| except Exception as e: | |
| print(f"Test failed: {e}") | |
| return False | |
| finally: | |
| # Stop the server | |
| server_process.terminate() | |
| server_process.wait() | |
| return True | |
| if __name__ == "__main__": | |
| success = test_api() | |
| sys.exit(0 if success else 1) |