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| from pathlib import Path | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| from tensorflow import keras | |
| PROJECT_ROOT = Path(__file__).resolve().parent | |
| MODEL_PATH = PROJECT_ROOT / "face_mask_model.keras" | |
| IMAGE_SIZE = (224, 224) | |
| CLASS_NAMES = ["with_mask", "without_mask"] | |
| if not MODEL_PATH.exists(): | |
| raise FileNotFoundError( | |
| f"Model tidak ditemukan di {MODEL_PATH}. Pastikan file .keras tersedia." | |
| ) | |
| model = keras.models.load_model(MODEL_PATH) | |
| def build_examples() -> list[list[str]]: | |
| example_paths = [] | |
| candidate_files = [ | |
| PROJECT_ROOT / "data" / "with_mask" / "with_mask_1.jpg", | |
| PROJECT_ROOT / "data" / "without_mask" / "without_mask_1.jpg", | |
| PROJECT_ROOT / "data" / "with_mask" / "with_mask_10.jpg", | |
| PROJECT_ROOT / "data" / "without_mask" / "without_mask_10.jpg", | |
| ] | |
| for path in candidate_files: | |
| if path.exists(): | |
| example_paths.append([str(path)]) | |
| return example_paths | |
| def predict_mask(image: Image.Image): | |
| if image is None: | |
| raise gr.Error("Silakan unggah gambar wajah terlebih dahulu.") | |
| image = image.convert("RGB").resize(IMAGE_SIZE) | |
| image_array = np.asarray(image, dtype=np.float32) | |
| batch = np.expand_dims(image_array, axis=0) | |
| predictions = model.predict(batch, verbose=0)[0] | |
| scores = {label: float(score) for label, score in zip(CLASS_NAMES, predictions)} | |
| predicted_index = int(np.argmax(predictions)) | |
| predicted_label = CLASS_NAMES[predicted_index] | |
| confidence = float(predictions[predicted_index]) * 100 | |
| summary = ( | |
| f"Prediksi: {predicted_label.replace('_', ' ').title()} | " | |
| f"Confidence: {confidence:.2f}%" | |
| ) | |
| return scores, summary | |
| custom_css = """ | |
| body { | |
| background: | |
| radial-gradient(circle at top left, #fff5d6 0%, transparent 32%), | |
| radial-gradient(circle at top right, #d8f5ff 0%, transparent 30%), | |
| linear-gradient(135deg, #f7f2e8 0%, #eef7ff 48%, #f5efe4 100%); | |
| } | |
| .gradio-container { | |
| max-width: 1100px !important; | |
| } | |
| .hero { | |
| padding: 20px 24px; | |
| border-radius: 24px; | |
| background: linear-gradient(135deg, rgba(17, 24, 39, 0.94), rgba(31, 41, 55, 0.88)); | |
| color: #f9fafb; | |
| box-shadow: 0 16px 40px rgba(15, 23, 42, 0.15); | |
| margin-bottom: 16px; | |
| } | |
| .hero h1 { | |
| margin: 0 0 8px 0; | |
| font-size: 2.2rem; | |
| } | |
| .hero p { | |
| margin: 0; | |
| color: #e5e7eb; | |
| line-height: 1.6; | |
| } | |
| """ | |
| with gr.Blocks( | |
| theme=gr.themes.Soft( | |
| primary_hue="cyan", | |
| secondary_hue="amber", | |
| neutral_hue="slate", | |
| ), | |
| css=custom_css, | |
| title="Face Mask Detector", | |
| ) as demo: | |
| gr.HTML( | |
| """ | |
| <div class="hero"> | |
| <h1>Face Mask Detector</h1> | |
| <p> | |
| Unggah gambar wajah untuk mendeteksi apakah orang tersebut memakai masker | |
| atau tidak. Model diambil langsung dari file <code>face_mask_model.keras</code>. | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=5): | |
| image_input = gr.Image( | |
| type="pil", | |
| label="Input Wajah", | |
| sources=["upload", "webcam"], | |
| ) | |
| predict_button = gr.Button("Prediksi Sekarang", variant="primary") | |
| with gr.Column(scale=4): | |
| label_output = gr.Label( | |
| label="Probabilitas Kelas", | |
| num_top_classes=2, | |
| ) | |
| summary_output = gr.Textbox( | |
| label="Ringkasan Hasil", | |
| interactive=False, | |
| ) | |
| examples = build_examples() | |
| if examples: | |
| gr.Examples( | |
| examples=examples, | |
| inputs=image_input, | |
| label="Contoh Gambar", | |
| ) | |
| predict_button.click( | |
| fn=predict_mask, | |
| inputs=image_input, | |
| outputs=[label_output, summary_output], | |
| ) | |
| image_input.change( | |
| fn=predict_mask, | |
| inputs=image_input, | |
| outputs=[label_output, summary_output], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |