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import tensorflow as tf |
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from tensorflow.keras.models import load_model |
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import gradio as gr |
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import cv2 |
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import numpy as np |
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from tensorflow.keras.applications.inception_v3 import preprocess_input |
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import gdown |
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file_id = '10LuyD0erYpFO2D4dN_BYoBQU9sblpLNL' |
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gdown.download(f"https://drive.google.com/uc?export=download&id={file_id}", "Dalam_Nama_TuhanYesus.keras", quiet=False) |
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model = load_model("Dalam_Nama_TuhanYesus.keras") |
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acne_labels = {0: 'Clear', 1: 'Comedo', 2: 'Acne'} |
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acne_treatment = { |
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0: "✨ Keep up the good work! Maintain your skincare routine.", |
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1: "- Benzoyl peroxide\n- Azelaic acid\n- Salicylic acid +/- sulfur\n- Retinoids (tretinoin, isotretinoin, adapalene)\n*Consult a dermatologist if needed*", |
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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*" |
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} |
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def detect_acne(image, threshold=0.4): |
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image_resized = cv2.resize(image, (299, 299)) |
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input_data = preprocess_input(np.expand_dims(image_resized, axis=0)) |
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predictions = model.predict(input_data) |
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max_index = np.argmax(predictions[0]) |
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max_prob = predictions[0][max_index] |
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detections, treatment_suggestion = [], "" |
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if max_prob > threshold: |
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detections.append(acne_labels[max_index]) |
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treatment_suggestion = acne_treatment[max_index] |
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return f"Detected: {detections}", treatment_suggestion |
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custom_css = """ |
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@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@400;600;700&display=swap'); |
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body { |
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background: linear-gradient(135deg, #ff7e5f, #feb47b); |
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font-family: 'Poppins', sans-serif; |
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color: #333; |
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margin: 0; |
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padding: 0; |
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} |
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/* Container utama */ |
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.gradio-container { |
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max-width: 950px !important; |
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margin: auto !important; |
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padding: 30px !important; |
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border-radius: 16px; |
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background: rgba(255, 255, 255, 0.95); |
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box-shadow: 0 10px 25px rgba(0,0,0,0.15); |
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} |
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/* Judul */ |
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h1 { |
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font-size: 40px !important; |
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font-weight: 700 !important; |
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text-align: center; |
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margin-bottom: 10px !important; |
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color: #222 !important; |
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} |
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/* Subjudul */ |
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h2 { |
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font-size: 22px !important; |
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font-weight: 500 !important; |
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margin-top: 5px; |
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text-align: center; |
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color: #444; |
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} |
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/* Deskripsi */ |
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p { |
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font-size: 16px; |
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font-weight: 400; |
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color: #555; |
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text-align: center; |
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margin: 8px 0; |
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} |
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/* Kotak Output */ |
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textarea { |
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background: #ffffff !important; |
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color: #222 !important; |
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border-radius: 10px !important; |
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padding: 15px !important; |
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font-size: 16px !important; |
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border: 1px solid #ddd !important; |
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box-shadow: inset 0 1px 3px rgba(0,0,0,0.1); |
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} |
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/* Tombol */ |
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.gr-button-primary { |
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background: linear-gradient(to right, #ff7e5f, #feb47b) !important; |
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color: white !important; |
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border-radius: 8px !important; |
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font-weight: 600 !important; |
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font-size: 16px !important; |
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padding: 10px 20px !important; |
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transition: 0.3s ease-in-out; |
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} |
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.gr-button-primary:hover { |
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background: linear-gradient(to right, #ff6a00, #ee0979) !important; |
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transform: scale(1.05); |
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} |
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/* Tombol Clear */ |
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.gr-button-secondary { |
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background: #444 !important; |
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color: #fff !important; |
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border-radius: 8px !important; |
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font-weight: 500 !important; |
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padding: 10px 20px !important; |
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} |
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""" |
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interface = gr.Interface( |
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fn=detect_acne, |
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inputs=gr.Image(type="numpy", label="📷 Upload Your Face Image"), |
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outputs=[ |
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gr.Textbox(label="🩺 Detection Result"), |
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gr.Textbox(label="💡 Treatment Suggestion") |
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], |
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title="AiCNE: Smart Acne Detection", |
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description=( |
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"<div style='text-align: center;'>" |
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"<h2>Your AI-powered assistant for acne detection</h2>" |
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"<p>Upload a clear face image and get instant analysis with treatment suggestions.</p>" |
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"</div>" |
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), |
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css=custom_css, |
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) |
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interface.launch() |
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