Docummentation

Dockerfile

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+# Base Image Python 3.9 Slim
+FROM python:3.9-slim
+
+# Install Library Sistem untuk OpenCV (Debian Bookworm/Trixie Compatible)
+RUN apt-get update && apt-get install -y \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Setup User Non-Root (Standar Keamanan HF)
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+# Setup Direktori Kerja
+WORKDIR /app
+
+# Install Dependencies
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy File Aplikasi
+COPY --chown=user . /app
+
+# Expose Port & Jalankan
+EXPOSE 7860
+CMD ["python", "app.py"]

app.py

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+import os
+import numpy as np
+import cv2
+import pickle
+import tensorflow as tf
+from flask import Flask, request, render_template_string
+from skimage.feature import hog
+
+app = Flask(__name__)
+
+# Load Model & Scaler
+MODEL_PATH = 'model/day_night_model.h5'
+SCALER_PATH = 'model/scaler.pkl'
+
+try:
+    model = tf.keras.models.load_model(MODEL_PATH)
+    with open(SCALER_PATH, 'rb') as f:
+        scaler = pickle.load(f)
+    print("✅ System Loaded Successfully")
+except Exception as e:
+    print(f"❌ Error loading system: {e}")
+
+def preprocess_image(image_bytes):
+    # Decode gambar
+    nparr = np.frombuffer(image_bytes, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+    # Preprocessing (Harus sama persis dengan Training)
+    img = cv2.resize(img, (256, 256))
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    hog_feat = hog(gray, orientations=9, pixels_per_cell=(8,8),
+                   cells_per_block=(2,2), block_norm='L2-Hys',
+                   visualize=False, feature_vector=True)
+
+    return scaler.transform(hog_feat.reshape(1, -1))
+
+@app.route('/', methods=['GET'])
+def home():
+    return render_template_string('''
+    <div style="text-align:center; padding:50px;">
+        <h1>Day vs Night Classifier</h1>
+        <form action="/predict" method="post" enctype="multipart/form-data">
+            <input type="file" name="file" required><br><br>
+            <button type="submit">Prediksi</button>
+        </form>
+    </div>
+    ''')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    try:
+        file = request.files['file']
+        data = preprocess_image(file.read())
+        prediction = model.predict(data)[0][0]
+
+        label = "Day (Siang)" if prediction > 0.5 else "Night (Malam)"
+        return f"<h2 style='text-align:center'>Hasil: {label}</h2><center><a href='/'>Kembali</a></center>"
+    except Exception as e:
+        return f"Error: {e}"
+
+if __name__ == '__main__':
+    # Port 7860 wajib untuk Hugging Face Spaces
+    app.run(host='0.0.0.0', port=7860)

model/day_night_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b845f658b285f31dc40377a751fb818fb92d4d43406b37b2fbe89a3ef77e2bc
+size 53273656

model/scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:453f1b2ef91392936efaab507118f1fd35274372a1eb6e22dafbe6e4235063b8
+size 830760