Enhance README.md with Docker installation instructions and update Ollama API endpoint to be configurable via environment variable.

.dockerignore

@@ -0,0 +1,77 @@
+# Git and version control
+.git
+.gitignore
+.gitattributes
+
+# Docker files
+Dockerfile
+docker-compose.yml
+.dockerignore
+
+# Environment and config files
+.env
+.env.*
+docker.env.example
+
+# Documentation
+*.md
+docs/
+DOCKER.md
+README.md
+INSTALLATION.md
+GEMINI_INSIGHTS.md
+
+# Python cache and virtual environments
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+venv/
+env/
+ENV/
+
+# IDE and editor files
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Local directories that will be mounted as volumes
+videos/
+outputs/
+cache/
+config/
+
+# Logs
+*.log
+logs/
+
+# Temporary files
+tmp/
+temp/
+*.tmp
+
+# Test files
+tests/
+*_test.py
+test_*.py
+
+# Build artifacts
+build/
+dist/
+*.egg-info/
+
+# Jupyter notebooks
+*.ipynb
+.ipynb_checkpoints/ 

DOCKER.md

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+# Docker Deployment Guide for VideoTranscriber
+
+This guide explains how to run VideoTranscriber in a Docker container while using Ollama models on your host system.
+
+## Architecture Overview
+
+```
+┌─────────────────────────────────────────┐
+│ Host System                             │
+│ ┌─────────────────┐  ┌──────────────────│
+│ │ Ollama Service  │  │ Video Files      │
+│ │ (port 11434)    │  │ Directory        │
+│ └─────────────────┘  └──────────────────│
+│         ▲                     ▲         │
+│         │                     │         │
+│ ┌───────┼─────────────────────┼─────────│
+│ │ Docker Container            │         │
+│ │ ┌─────▼─────────┐          │         │
+│ │ │ VideoTranscriber         │         │
+│ │ │ - Streamlit App          │         │
+│ │ │ - Whisper Models         │         │
+│ │ │ - ML Dependencies        │         │
+│ │ └───────────────┘          │         │
+│ └────────────────────────────┼─────────│
+│                              │         │
+│         Mounted Volumes ─────┘         │
+└─────────────────────────────────────────┘
+```
+
+## Quick Start
+
+### Prerequisites
+
+1. **Docker & Docker Compose** installed
+2. **Ollama running on host**:
+   ```bash
+   # Install Ollama (if not already installed)
+   curl -fsSL https://ollama.ai/install.sh | sh
+   
+   # Start Ollama service
+   ollama serve
+   
+   # Pull a model (in another terminal)
+   ollama pull llama3
+   ```
+
+### 1. Setup Environment
+
+```bash
+# Copy environment template
+cp docker.env.example .env
+
+# Edit .env file with your paths
+# Key settings to update:
+VIDEO_PATH=/path/to/your/videos
+OUTPUT_PATH=/path/to/save/outputs
+HF_TOKEN=your_huggingface_token_if_needed
+```
+
+### 2. Create Required Directories
+
+```bash
+# Create directories for mounting
+mkdir -p videos outputs cache config
+```
+
+### 3. Build and Run
+
+```bash
+# Build and start the container
+docker-compose up -d
+
+# View logs
+docker-compose logs -f
+
+# Access the application
+# Open browser to: http://localhost:8501
+```
+
+## Configuration Options
+
+### Environment Variables
+
+| Variable | Description | Default | Required |
+|----------|-------------|---------|----------|
+| `VIDEO_PATH` | Host directory containing video files | `./videos` | Yes |
+| `OUTPUT_PATH` | Host directory for outputs | `./outputs` | Yes |
+| `CACHE_PATH` | Host directory for model cache | `./cache` | No |
+| `OLLAMA_API_URL` | Ollama API endpoint | `http://host.docker.internal:11434/api` | No |
+| `HF_TOKEN` | HuggingFace token for advanced features | - | No |
+| `CUDA_VISIBLE_DEVICES` | GPU devices to use | - | No |
+
+### Volume Mounts
+
+| Host Path | Container Path | Purpose |
+|-----------|----------------|---------|
+| `${VIDEO_PATH}` | `/app/data/videos` | Input video files |
+| `${OUTPUT_PATH}` | `/app/data/outputs` | Generated transcripts/summaries |
+| `${CACHE_PATH}` | `/app/data/cache` | Model and processing cache |
+| `${CONFIG_PATH}` | `/app/config` | Configuration files |
+
+## Platform-Specific Setup
+
+### Windows (Docker Desktop)
+
+```yaml
+# In docker-compose.yml - use bridge networking
+networks:
+  - videotranscriber-network
+
+environment:
+  - OLLAMA_API_URL=http://host.docker.internal:11434/api
+```
+
+### macOS (Docker Desktop)
+
+Same as Windows - uses `host.docker.internal` to access host services.
+
+### Linux
+
+Option 1 - Host Networking (Recommended):
+```yaml
+# In docker-compose.yml
+network_mode: host
+
+environment:
+  - OLLAMA_API_URL=http://localhost:11434/api
+```
+
+Option 2 - Bridge Networking:
+```yaml
+environment:
+  - OLLAMA_API_URL=http://172.17.0.1:11434/api  # Docker bridge IP
+```
+
+## GPU Support
+
+### NVIDIA GPU Setup
+
+1. **Install NVIDIA Container Toolkit**:
+   ```bash
+   # Ubuntu/Debian
+   curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
+   curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
+       sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
+       sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+   sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
+   sudo systemctl restart docker
+   ```
+
+2. **Enable in docker-compose.yml**:
+   ```yaml
+   deploy:
+     resources:
+       reservations:
+         devices:
+           - driver: nvidia
+             count: 1
+             capabilities: [gpu]
+   ```
+
+## Usage in Container
+
+### Application Settings
+
+When running in Docker, update these settings in the VideoTranscriber UI:
+
+1. **Base Folder**: Set to `/app/data/videos`
+2. **Ollama Models**: Should auto-detect from host
+3. **GPU Settings**: Will use container GPU if configured
+
+### File Access
+
+- **Input Videos**: Place in your `${VIDEO_PATH}` directory on host
+- **Outputs**: Generated files appear in `${OUTPUT_PATH}` on host
+- **Cache**: Models cached in `${CACHE_PATH}` for faster subsequent runs
+
+## Troubleshooting
+
+### Common Issues
+
+#### 1. Can't Connect to Ollama
+
+**Symptoms**: "Ollama service is not available" message
+
+**Solutions**:
+- Verify Ollama is running: `curl http://localhost:11434/api/tags`
+- Check firewall settings
+- For Linux, try host networking mode
+- Verify OLLAMA_API_URL in environment
+
+#### 2. No Video Files Detected
+
+**Symptoms**: "No recordings found" message
+
+**Solutions**:
+- Check VIDEO_PATH points to correct directory
+- Ensure directory contains supported formats (.mp4, .avi, .mov, .mkv)
+- Check file permissions
+
+#### 3. GPU Not Detected
+
+**Symptoms**: Processing is slow, no GPU utilization
+
+**Solutions**:
+- Install NVIDIA Container Toolkit
+- Uncomment GPU section in docker-compose.yml
+- Verify: `docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi`
+
+#### 4. Permission Issues
+
+**Symptoms**: Cannot write to output directory
+
+**Solutions**:
+```bash
+# Fix permissions
+sudo chown -R $(id -u):$(id -g) outputs cache config
+chmod -R 755 outputs cache config
+```
+
+### Debugging
+
+```bash
+# View container logs
+docker-compose logs -f videotranscriber
+
+# Execute shell in container
+docker-compose exec videotranscriber bash
+
+# Check Ollama connectivity from container
+docker-compose exec videotranscriber curl -f $OLLAMA_API_URL/tags
+
+# Monitor resource usage
+docker stats videotranscriber
+```
+
+## Advanced Configuration
+
+### Custom Dockerfile
+
+For specialized requirements, modify the Dockerfile:
+
+```dockerfile
+# Add custom dependencies
+RUN pip install your-custom-package
+
+# Set custom environment variables
+ENV YOUR_CUSTOM_VAR=value
+
+# Copy custom configuration
+COPY custom-config.yaml /app/config/
+```
+
+### Multi-Instance Deployment
+
+Run multiple instances for different use cases:
+
+```bash
+# Copy docker-compose.yml to docker-compose.prod.yml
+# Modify ports and paths
+docker-compose -f docker-compose.prod.yml up -d
+```
+
+### CI/CD Integration
+
+```yaml
+# .github/workflows/docker.yml
+name: Build and Deploy
+on:
+  push:
+    branches: [main]
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Build Docker image
+        run: docker build -t videotranscriber .
+```
+
+## Performance Optimization
+
+### Memory Management
+
+```yaml
+# In docker-compose.yml
+deploy:
+  resources:
+    limits:
+      memory: 8G
+    reservations:
+      memory: 4G
+```
+
+### Model Caching
+
+- Use persistent volumes for `/app/data/cache`
+- Pre-download models to reduce startup time
+- Configure appropriate cache size limits
+
+### Network Optimization
+
+- Use host networking on Linux for better performance
+- Consider running Ollama and VideoTranscriber on same machine
+- Use SSD storage for cache directories 

Dockerfile

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+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    git \
+    wget \
+    curl \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better Docker layer caching
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Install PyTorch with CUDA support (adjust based on your needs)
+RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+
+# Copy application code
+COPY . .
+
+# Create directories for mounted volumes
+RUN mkdir -p /app/data/videos /app/data/outputs /app/data/cache
+
+# Set environment variables
+ENV STREAMLIT_SERVER_PORT=8501
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+ENV STREAMLIT_SERVER_HEADLESS=true
+ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
+# Expose Streamlit port
+EXPOSE 8501
+
+# Health check
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD curl -f http://localhost:8501/_stcore/health || exit 1
+
+# Start the application
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"] 

README.md

@@ -13,6 +13,32 @@ https://github.com/user-attachments/assets/990e63fc-232e-46a0-afdf-ca8836d46a13
 
 ## Installation
 
+### 🐳 Docker Installation (Recommended)
+
+**Benefits**: Isolated environment, no dependency conflicts, easy deployment
+
+```bash
+# 1. Clone repository
+git clone https://github.com/DataAnts-AI/VideoTranscriber.git
+cd VideoTranscriber
+
+# 2. Setup environment
+cp docker.env.example .env
+# Edit .env with your video directory paths
+
+# 3. Ensure Ollama is running on host
+ollama serve  # In separate terminal
+ollama pull llama3
+
+# 4. Start with Docker Compose
+docker-compose up -d
+
+# 5. Access application
+# Open browser to: http://localhost:8501
+```
+
+See [DOCKER.md](DOCKER.md) for complete Docker setup guide.
+
 ### Easy Installation (Recommended)
 
 #### Windows

docker-compose.yml

@@ -0,0 +1,51 @@
+version: '3.8'
+
+services:
+  videotranscriber:
+    build: .
+    container_name: videotranscriber
+    ports:
+      - "8501:8501"
+    volumes:
+      # Mount your video files directory (change the left path to your actual videos folder)
+      - "${VIDEO_PATH:-./videos}:/app/data/videos"
+      # Mount output directory for transcripts and summaries
+      - "${OUTPUT_PATH:-./outputs}:/app/data/outputs" 
+      # Mount cache directory for model caching (optional, improves performance)
+      - "${CACHE_PATH:-./cache}:/app/data/cache"
+      # Mount a config directory if needed
+      - "${CONFIG_PATH:-./config}:/app/config"
+    environment:
+      # Ollama configuration for host access
+      - OLLAMA_API_URL=${OLLAMA_API_URL:-http://host.docker.internal:11434/api}
+      # Optional: HuggingFace token for advanced features
+      - HF_TOKEN=${HF_TOKEN:-}
+      # GPU configuration
+      - CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-}
+      # Cache settings
+      - TRANSFORMERS_CACHE=/app/data/cache/transformers
+      - WHISPER_CACHE=/app/data/cache/whisper
+    # For GPU access (uncomment if you have NVIDIA GPU and nvidia-docker)
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: 1
+    #           capabilities: [gpu]
+    restart: unless-stopped
+    # For Linux hosts, you might prefer host networking for better Ollama access
+    # network_mode: host  # Uncomment for Linux hosts
+    # Use bridge networking for Windows/Mac with host.docker.internal
+    networks:
+      - videotranscriber-network
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8501/_stcore/health"]
+      interval: 30s
+      timeout: 10s
+      retries: 3
+      start_period: 60s
+
+networks:
+  videotranscriber-network:
+    driver: bridge 

docker.env.example

@@ -0,0 +1,63 @@
+# 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 

utils/ollama_integration.py

@@ -13,8 +13,8 @@ import os
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Default Ollama API endpoint
+# Default Ollama API endpoint - configurable via environment variable
-OLLAMA_API_URL = "http://localhost:11434/api"
+OLLAMA_API_URL = os.environ.get("OLLAMA_API_URL", "http://localhost:11434/api")
 
 
 def check_ollama_available():