Upload inference.py

inference.py

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+from tensorflow.keras.models import load_model # type: ignore
+from tensorflow.keras.preprocessing import image as keras_image # type: ignore
+import numpy as np
+from PIL import Image
+import io
+import os
+import gdown
+
+# === Path model lokal ===
+model_path = 'saved_model_palm_disease.keras'
+
+# === Unduh model dari Google Drive jika belum ada ===
+if not os.path.exists(model_path):
+    url = 'https://drive.google.com/uc?id=1g-QPUIsySVm1oBl0KXpKKlxe7x_JPe7B'
+    gdown.download(url, model_path, quiet=False)
+
+# === Load model hanya sekali ===
+model = load_model(model_path)
+
+# === Label urutan class_name (dari training) ===
+labels = ['Boron Excess', 'Ganoderma', 'Healthy', 'Scale insect']
+
+# === Fungsi preprocessing gambar ===
+def preprocess_image(image_bytes):
+    img = Image.open(io.BytesIO(image_bytes)).convert("RGB").resize((224, 224))
+    img_array = keras_image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+# === Fungsi prediksi utama ===
+def predict_image(image_bytes):
+    img_array = preprocess_image(image_bytes)
+    predictions = model.predict(img_array)
+    class_index = int(np.argmax(predictions))
+    confidence = float(np.max(predictions))
+    return {
+        'class': labels[class_index],
+        'confidence': confidence
+    }
+
+# === Fungsi handler yang dipanggil Hugging Face API ===
+def handler(inputs):
+    try:
+        # Input berupa base64 atau byte dari gambar
+        image_bytes = inputs['inputs']
+
+        # Jika data berupa string base64, ubah ke byte
+        if isinstance(image_bytes, str):
+            import base64
+            image_bytes = base64.b64decode(image_bytes)
+
+        result = predict_image(image_bytes)
+        return result
+
+    except Exception as e:
+        return {"error": str(e)}
+