Spaces:
Running on Zero
Running on Zero
| import json | |
| import sys | |
| from pathlib import Path | |
| import numpy as np | |
| import tensorflow as tf | |
| from PIL import Image | |
| from src.model import CLASS_NAMES, IMAGE_SIZE | |
| ROOT = Path(__file__).resolve().parents[1] | |
| MODEL_DIR = ROOT / "models" | |
| def main(image_path: str): | |
| model_path = MODEL_DIR / "human_face_v1.keras" | |
| metadata_path = MODEL_DIR / "human_face_v1.json" | |
| if not model_path.exists(): | |
| raise SystemExit(f"Model not found at {model_path}") | |
| model = tf.keras.models.load_model(model_path) | |
| metadata = json.loads(metadata_path.read_text(encoding="utf-8")) | |
| threshold = float(metadata["threshold"]) | |
| # Preprocess image | |
| image = Image.open(image_path).convert("RGB").resize((IMAGE_SIZE[1], IMAGE_SIZE[0])) | |
| pixels = np.asarray(image, dtype=np.float32)[None, ...] # Shape: (1, 224, 224, 3) | |
| p_non_human = float(model.predict(pixels, verbose=0)[0][0]) | |
| predicted_index = int(p_non_human >= threshold) | |
| result = { | |
| "prediction": CLASS_NAMES[predicted_index], | |
| "human_probability": round(1.0 - p_non_human, 4), | |
| "non_human_probability": round(p_non_human, 4), | |
| "model_version": metadata["model_version"], | |
| } | |
| print(json.dumps(result, indent=2)) | |
| if __name__ == "__main__": | |
| if len(sys.argv) != 2: | |
| raise SystemExit("Usage: python -m src.predict_one path/to/image.jpg") | |
| main(sys.argv[1]) |