# VideoTranscriber Docker Configuration
# Copy this file to .env and modify the values as needed

# =============================================================================
# DOCKER VOLUME PATHS (Host Directories)
# =============================================================================

# Path to your video files directory on the host
# This directory will be mounted into the container at /app/data/videos
VIDEO_PATH=./videos

# Path where outputs (transcripts, summaries) will be saved on the host
# This directory will be mounted into the container at /app/data/outputs
OUTPUT_PATH=./outputs

# Path for caching ML models and processed files (improves performance)
# This directory will be mounted into the container at /app/data/cache
CACHE_PATH=./cache

# Optional: Configuration directory for custom settings
CONFIG_PATH=./config

# =============================================================================
# OLLAMA CONFIGURATION
# =============================================================================

# Ollama API URL - how the container accesses your host Ollama service
# For Windows/Mac with Docker Desktop: use host.docker.internal
# For Linux: use host networking or the actual host IP
OLLAMA_API_URL=http://host.docker.internal:11434/api

# =============================================================================
# ML MODEL CONFIGURATION
# =============================================================================

# HuggingFace token for advanced features (speaker diarization, etc.)
# Get your token at: https://huggingface.co/settings/tokens
# Leave empty if not using advanced features
HF_TOKEN=

# GPU Configuration
# Specify which GPU devices to use (leave empty for all available)
# Examples: "0" for first GPU, "0,1" for first two GPUs
CUDA_VISIBLE_DEVICES=

# =============================================================================
# DOCKER-SPECIFIC SETTINGS
# =============================================================================

# Container name (change if you want to run multiple instances)
CONTAINER_NAME=videotranscriber

# Port mapping (host:container)
HOST_PORT=8501

# =============================================================================
# EXAMPLE USAGE
# =============================================================================
# 1. Copy this file: cp docker.env.example .env
# 2. Edit the paths to match your system
# 3. Make sure Ollama is running on your host: ollama serve
# 4. Start the container: docker-compose up -d
# 5. Access the app at: http://localhost:8501