app.py

import tensorflow as tf
from tensorflow.keras.models import load_model
import gradio as gr
import cv2
import numpy as np
from tensorflow.keras.applications.inception_v3 import preprocess_input
import gdown

# Download model dari Google Drive
file_id = '10LuyD0erYpFO2D4dN_BYoBQU9sblpLNL'
gdown.download(f"https://drive.google.com/uc?export=download&id={file_id}", "Dalam_Nama_TuhanYesus.keras", quiet=False)

# Load model
model = load_model("Dalam_Nama_TuhanYesus.keras")

# Labels dan saran pengobatan
acne_labels = {0: 'Clear', 1: 'Comedo', 2: 'Acne'}
acne_treatment = {
    0: "✨ Keep up the good work! Maintain your skincare routine.",
    1: "- Benzoyl peroxide\n- Azelaic acid\n- Salicylic acid +/- sulfur\n- Retinoids (tretinoin, isotretinoin, adapalene)\n*Consult a dermatologist if needed*",
    2: "- Benzoyl peroxide, azelaic acid, or retinoids (tretinoin/adapalene)\n- Antibiotics (clindamycin)\n- Hormonal therapy (OCPs)\n- Light/laser therapy\n*Please consult your dermatologist*"
}

# Fungsi deteksi
def detect_acne(image, threshold=0.4):
    image_resized = cv2.resize(image, (299, 299))
    input_data = preprocess_input(np.expand_dims(image_resized, axis=0))
    predictions = model.predict(input_data)
    
    max_index = np.argmax(predictions[0])
    max_prob = predictions[0][max_index]
    
    detections, treatment_suggestion = [], ""
    if max_prob > threshold:
        detections.append(acne_labels[max_index])
        treatment_suggestion = acne_treatment[max_index]
    
    return f"Detected: {detections}", treatment_suggestion


# CSS elegan & rapih
custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;600;700&display=swap');

body {
    background: linear-gradient(135deg, #ff7e5f, #feb47b);
    font-family: 'Poppins', sans-serif;
    color: #333;
    margin: 0;
    padding: 0;
}

/* Container utama */
.gradio-container {
    max-width: 950px !important;
    margin: auto !important;
    padding: 30px !important;
    border-radius: 16px;
    background: rgba(255, 255, 255, 0.95);
    box-shadow: 0 10px 25px rgba(0,0,0,0.15);
}

/* Judul */
h1 {
    font-size: 40px !important;
    font-weight: 700 !important;
    text-align: center;
    margin-bottom: 10px !important;
    color: #222 !important;
}

/* Subjudul */
h2 {
    font-size: 22px !important;
    font-weight: 500 !important;
    margin-top: 5px;
    text-align: center;
    color: #444;
}

/* Deskripsi */
p {
    font-size: 16px;
    font-weight: 400;
    color: #555;
    text-align: center;
    margin: 8px 0;
}

/* Kotak Output */
textarea {
    background: #ffffff !important;
    color: #222 !important;
    border-radius: 10px !important;
    padding: 15px !important;
    font-size: 16px !important;
    border: 1px solid #ddd !important;
    box-shadow: inset 0 1px 3px rgba(0,0,0,0.1);
}

/* Tombol */
.gr-button-primary {
    background: linear-gradient(to right, #ff7e5f, #feb47b) !important;
    color: white !important;
    border-radius: 8px !important;
    font-weight: 600 !important;
    font-size: 16px !important;
    padding: 10px 20px !important;
    transition: 0.3s ease-in-out;
}

.gr-button-primary:hover {
    background: linear-gradient(to right, #ff6a00, #ee0979) !important;
    transform: scale(1.05);
}

/* Tombol Clear */
.gr-button-secondary {
    background: #444 !important;
    color: #fff !important;
    border-radius: 8px !important;
    font-weight: 500 !important;
    padding: 10px 20px !important;
}
"""
   


# Interface Gradio
interface = gr.Interface(
    fn=detect_acne,
    inputs=gr.Image(type="numpy", label="📷 Upload Your Face Image"),
    outputs=[
        gr.Textbox(label="🩺 Detection Result"),
        gr.Textbox(label="💡 Treatment Suggestion")
    ],
    title="AiCNE: Smart Acne Detection",
    description=(
        "<div style='text-align: center;'>"
        "<h2>Your AI-powered assistant for acne detection</h2>"
        "<p>Upload a clear face image and get instant analysis with treatment suggestions.</p>"
        "</div>"
    ),
    css=custom_css,
)

interface.launch()