Instructions to use Rizky2Hero/Gender_Identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rizky2Hero/Gender_Identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Rizky2Hero/Gender_Identification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Rizky2Hero/Gender_Identification") model = AutoModelForImageClassification.from_pretrained("Rizky2Hero/Gender_Identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download py from Rizky2Hero/Gender_Identification: direct link, hf CLI and curl.
- Browser
- Download file 631 Bytes
-
https://huggingface.co/Rizky2Hero/Gender_Identification/resolve/main/py
- Command line
-
hf download hf://Rizky2Hero/Gender_Identification/py
-
curl -L -o py https://huggingface.co/Rizky2Hero/Gender_Identification/resolve/main/py
631 Bytes
| from flask import Flask, request, jsonify | |
| app = Flask(__name__) | |
| def predict(): | |
| # Ambil gambar yang diunggah oleh pengguna | |
| uploaded_image = request.files['image'] | |
| # Lakukan inferensi dengan model Hugging Face di sini | |
| # gantilah ini dengan kode yang sesuai dengan model Anda | |
| # Misalnya, Anda dapat menggunakan model yang telah di-host di Hugging Face: | |
| # result = your_huggingface_model.predict(uploaded_image) | |
| # Kembalikan hasil inferensi ke pengguna | |
| return jsonify({'result': 'prediction_result'}) | |
| if __name__ == '__main__': | |
| app.run(debug=True) | |