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 InceptionV3, 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 untuk deteksi acne
acne_labels = {
    0: 'Clear',
    1: 'Comedo',
    2: 'Acne'
}

acne_treatment = {
    2: '- Topical anti-acne agents, such as benzoyl peroxide, azelaic acid, and tretinoin or adapalene gel and some antibiotics (clindamycin)\n- New bioactive proteins may also prove successful\n- Newer topical agents such as clascoterone\n- Low-dose combined oral contraceptive\n- Antiseptic or keratolytic washes containing salicylic acid\n- Light/laser therapy',
    0: 'Keep up the good work! ',
    1: '- Benzoyl peroxide\n- Azelaic acid\n- Salicylic acid +/- sulfur and resorcinol\n- Glycolic acid\n- Retinoids such as tretinoin, isotretinoin, adapalene (these require a doctors prescription)'
}

# Fungsi untuk mendeteksi acne
def detect_acne(image, threshold=0.4):
    # Resize gambar menjadi 224x224 piksel agar sesuai dengan ukuran input model
    image_resized = cv2.resize(image, (299, 299))
    
    # Proses gambar dengan preprocess_input untuk menyesuaikan format input model
    input_data = preprocess_input(np.expand_dims(image_resized, axis=0))  # Menambah dimensi untuk batch

    # Mendapatkan prediksi dari model
    predictions = model.predict(input_data)

    # Menemukan indeks kelas dengan probabilitas tertinggi
    max_index = np.argmax(predictions[0])  # Indeks dengan probabilitas tertinggi
    max_prob = predictions[0][max_index]  # Nilai probabilitas tertinggi

    # Inisialisasi variabel untuk hasil deteksi dan saran pengobatan
    detections = []  # Daftar untuk menyimpan label deteksi (misalnya 'Acne', 'Clear', 'Comedo')
    treatment_suggestion = ""  # Saran pengobatan yang sesuai

    # Jika probabilitas tertinggi lebih besar dari threshold, deteksi berhasil
    if max_prob > threshold:
        detections.append(acne_labels[max_index])  # Menambahkan label deteksi ke dalam daftar
        treatment_suggestion = acne_treatment[max_index]  # Mendapatkan saran pengobatan berdasarkan deteksi

    # Mengembalikan hasil: gambar yang sudah dianotasi, hasil deteksi, dan saran pengobatan
    return f"Detected face: {detections}", treatment_suggestion


# CSS untuk mempercantik tampilan antarmuka
custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Itim&display=swap');
@import url('https://fonts.googleapis.com/css2?family=Instrument+Sans:wght@400;700&display=swap');
body {
    background: linear-gradient(to bottom, #FFA500, #1E90FF); 
    color: #ffffff !important; 
    margin: 0;
    padding: 0;
    overflow: hidden; 
}

.gradio-container {
    max-height: 90vh; 
    overflow-y: auto; 
    background: transparent !important;
    color: #ffffff !important;
    border-radius: 12px;
    text-align: center;
    box-sizing: border-box;
    padding: 10px;
}

.gradio-container .wrap h1 {
    font-size: 100px !important; 
    font-family: 'Itim', cursive !important; 
    font-weight: bold !important;
    text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.5); 
    color: #000000 !important; 
}

h2 {
    font-size: 40px;
    font-family: 'Instrument Sans', sans-serif;
    font-weight: bold;
    color: #000000;
    text-align: center;
}

p {
    font-size: 30px;
    font-family: 'Instrument Sans', sans-serif;
    font-weight: bold;
    color: #000000;
    text-align: center;
}

.gradio-container .wrap {
    font-size: 40px !important; 
    line-height: 1.6;
    padding: 20px
}

.output-textbox textarea {
    background-color: #1e1e1e !important;
    color: #ffffff !important;
    border: 2px solid #000000; 
    font-weight: bold;
    font-size: 16px;
    padding: 10px;
    border-radius: 8px;
}

.gradio-container::-webkit-scrollbar {
    width: 12px;
}

.gradio-container::-webkit-scrollbar-track {
    background: rgba(255, 255, 255, 0.2);
    border-radius: 6px; 
}

.gradio-container::-webkit-scrollbar-thumb {
    background: #D3D3D3;
    border-radius: 6px;
    border: #555555; 
}

.gradio-container::-webkit-scrollbar-thumb:active {
    background: #555555; 
    border: #555555; 
}
"""

interface = gr.Interface(
    fn=detect_acne,
    inputs=gr.Image(type="numpy", label="Upload an image"),
    outputs=[
        gr.Textbox(label="Detection Result"),
        gr.Textbox(label="Treatment Suggestion")
    ],
    title="🌟 AiCNE 🌟",
    description=(
        "<div style='text-align: center;'>"
        "<h2>Welcome to <b>AiCNE</b>, your AI-powered assistant for acne detection!</h2>"
        "<h2>Upload a clear image of your face to analyze and classify acne types.</h2>"
        "<h2><b>Get instant results</b> and take a step closer to understanding your skin!</h2>"
        "<br>"
        "<br>"
        "<h2>What is AiCNE?</h2>"
        "<p>AiCNE is an AI-Powered Acne Detection tool, a smart solution to understand your skin condition with AI Technology.</p>"
        "<br>"
        "<h2>Why use AiCNE?</h2>"
        "<p>AiCNE detects acne within seconds with high accuracy, offering a user-friendly interface for your convenience.</p>"
        "<br>"
        "<br>"
        "</div>"
    ),
    css=custom_css,
)

interface.launch()