Edit fthe ile to deploy dDockerin Hugging Face

.dockerignore

@@ -0,0 +1,18 @@
+__pycache__
+*.pyc
+.git
+.gitignore
+*.ipynb
+train_sample_videos/
+prepared_dataset/
+split_dataset/
+tmp_debug/
+tmp_fake_faces/
+tmp_checkpoint/best_model.h5
+00-*.py
+01-*.py
+02-*.py
+03-*.py
+LICENSE
+App/diag_log.txt
+App/uploads/

.gitattributes

@@ -1,6 +1,6 @@
+*.keras filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.png filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
-*.keras filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
 *.ipynb filter=lfs diff=lfs merge=lfs -text

App/app.py

@@ -39,13 +39,19 @@ logger = logging.getLogger(__name__)
 
 app = Flask(__name__)
 app.config['UPLOAD_FOLDER'] = os.path.join(os.path.dirname(__file__), 'uploads')
-app.config['MAX_CONTENT_LENGTH'] = 200 * 1024 * 1024  # 200 MB limit
+app.config['MAX_CONTENT_LENGTH'] = int(os.environ.get('MAX_UPLOAD_MB', 50)) * 1024 * 1024
 ALLOWED_EXTENSIONS = {'mp4', 'avi', 'mov', 'mkv', 'wmv'}
 
 os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
 
 # Load the trained model
-MODEL_PATH = os.path.join(os.path.dirname(__file__), '..', 'tmp_checkpoint', 'best_model.keras')
+MODEL_PATH = os.environ.get(
+    'MODEL_PATH',
+    os.path.join(os.path.dirname(__file__), 'models', 'best_model.keras')
+)
+if not os.path.exists(MODEL_PATH):
+    # Fallback for local development
+    MODEL_PATH = os.path.join(os.path.dirname(__file__), '..', 'tmp_checkpoint', 'best_model.keras')
 logger.info('Loading model from %s', MODEL_PATH)
 model = load_model(MODEL_PATH)
 logger.info('Model loaded successfully')
@@ -309,5 +315,6 @@ app.register_blueprint(routes)
 
 
 if __name__ == '__main__':
-    logger.info('Starting Flask server on http://0.0.0.0:5000')
-    app.run(debug=False, host='0.0.0.0', port=5000)
+    port = int(os.environ.get('PORT', 7860))
+    logger.info('Starting Flask server on http://0.0.0.0:%d', port)
+    app.run(debug=False, host='0.0.0.0', port=port)

Dockerfile

@@ -0,0 +1,39 @@
+FROM python:3.10-slim
+
+# System dependencies for OpenCV, ffmpeg, and video processing
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ffmpeg \
+    libgl1 \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender1 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create non-root user (required by HF Spaces)
+RUN useradd -m -u 1000 user
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH \
+    PYTHONUNBUFFERED=1 \
+    TF_CPP_MIN_LOG_LEVEL=2
+
+WORKDIR /app
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY App/ .
+
+# Copy model files
+COPY tmp_checkpoint/best_model.keras models/best_model.keras
+
+# Create uploads directory with proper permissions
+RUN mkdir -p uploads && chown -R user:user /app
+
+USER user
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

README.md

@@ -1,2 +1,11 @@
+---
+title: AI-Deepfake Video Detection
+emoji: 🔍
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_port: 7860
+---
+
 # DeepFake-Detect
 Free deepfake detection tool for videos. Upload a video and get per-face authenticity scores in seconds. AI-powered synthetic face detection.

requirements.txt

@@ -1,7 +1,7 @@
 numpy
 pandas
-tensorflow
-opencv-python>=4.1.0
+tensorflow-cpu
+opencv-python-headless>=4.1.0
 mtcnn>=0.1.0
 split_folders
 flask