app.py

import gradio as gr
import tensorflow as tf
import numpy as np
import json
from PIL import Image

# 1. Load Model EfficientNet
# Ganti nama file sesuai dengan file .h5 efficientnet kamu
model = tf.keras.models.load_model("efficientnet_b3_isic2019.h5")

# 2. Load Label
with open("class_indices.json", "r") as f:
    class_indices = json.load(f)

labels = {v: k for k, v in class_indices.items()}

# 3. Fungsi Preprocessing
def predict_image(img):
    img = img.resize((300, 300))
    img_array = np.array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    preds = model.predict(img_array)[0]

    confidences = {}

    for i, label in labels.items():
        if i < len(preds):
            confidences[label] = float(preds[i])

    predicted_class = max(confidences, key=confidences.get)
    confidence = confidences[predicted_class]

    return {
        "predicted_class": predicted_class,
        "confidence": confidence,
        "confidences": confidences
    }

# 4. Interface Gradio
iface = gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.JSON(),
    title="Skin Disease Detection - EfficientNet",
    description="Deteksi penyakit kulit menggunakan model EfficientNet."
)

iface.launch()