curl -sSL https://raw.githubusercontent.com/yourusername/hawkeye/main/install.sh | bash

# 1. Clone the repository
git clone https://github.com/yourusername/hawkeye.git
cd hawkeye

# 2. Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Configure AI providers (optional but recommended)
cp env.example .env
# Edit .env file with your API keys (see AI Provider Setup section)

# 5. Verify installation
python application.py --version
python application.py info

# Method 1: Environment file (recommended)
echo "AI_PROVIDER=openai" >> .env
echo "AI_OPENAI_API_KEY=your_api_key_here" >> .env

# Method 2: Environment variables
export AI_PROVIDER=openai
export AI_OPENAI_API_KEY=your_api_key_here

# Method 3: Configuration file
python application.py config set ai.provider openai
python application.py config set ai.openai.api_key your_api_key_here

# Test OpenAI connection
python -c "
import openai
import os
from dotenv import load_dotenv
load_dotenv()
client = openai.OpenAI(api_key=os.getenv('AI_OPENAI_API_KEY'))
print('✅ OpenAI connection successful')
"

# Test with HawkEye
python application.py detect local --output test.json
python application.py analyze-threats --input test.json --cost-limit 0.50

# Method 1: Environment file (recommended)
echo "AI_PROVIDER=anthropic" >> .env
echo "AI_ANTHROPIC_API_KEY=your_api_key_here" >> .env

# Method 2: Environment variables
export AI_PROVIDER=anthropic
export AI_ANTHROPIC_API_KEY=your_api_key_here

# Method 3: Configuration file
python application.py config set ai.provider anthropic
python application.py config set ai.anthropic.api_key your_api_key_here

# Test Anthropic connection
python -c "
import anthropic
import os
from dotenv import load_dotenv
load_dotenv()
client = anthropic.Anthropic(api_key=os.getenv('AI_ANTHROPIC_API_KEY'))
print('✅ Anthropic connection successful')
"

# Test with HawkEye
python application.py detect local --output test.json
python application.py analyze-threats --input test.json --cost-limit 0.50

# Linux/macOS
curl -fsSL https://ollama.com/install.sh | sh

# Windows
# Download from https://ollama.com/download/windows

# Alternative: Docker
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

# Install recommended models for threat analysis
ollama pull llama3.1:8b      # Fast, good quality
ollama pull llama3.1:70b     # High quality, slower
ollama pull codellama:13b    # Good for technical analysis

# Verify installation
ollama list

# Method 1: Environment file (recommended)
echo "AI_PROVIDER=local_llm" >> .env
echo "AI_LOCAL_LLM_ENDPOINT=http://localhost:11434" >> .env
echo "AI_LOCAL_LLM_MODEL=llama3.1:8b" >> .env

# Method 2: Environment variables
export AI_PROVIDER=local_llm
export AI_LOCAL_LLM_ENDPOINT=http://localhost:11434
export AI_LOCAL_LLM_MODEL=llama3.1:8b

# Test Ollama connection
curl http://localhost:11434/api/tags

# Test with HawkEye
python application.py detect local --output test.json
python application.py analyze-threats --input test.json

# Primary provider
echo "AI_PROVIDER=anthropic" >> .env
echo "AI_ANTHROPIC_API_KEY=your_anthropic_key" >> .env

# Fallback provider
echo "AI_FALLBACK_PROVIDER=openai" >> .env
echo "AI_OPENAI_API_KEY=your_openai_key" >> .env

# Local LLM for privacy-sensitive analysis
echo "AI_LOCAL_LLM_ENDPOINT=http://localhost:11434" >> .env
echo "AI_LOCAL_LLM_MODEL=llama3.1:8b" >> .env

# Set in environment file
echo "AI_MAX_COST_PER_ANALYSIS=1.00" >> .env
echo "AI_MAX_DAILY_COST=25.00" >> .env

# Or via command line for each analysis
python application.py analyze-threats --input results.json --cost-limit 5.0

# Check current usage
python application.py config show | grep -i cost

# View analysis statistics
python -c "
from src.hawkeye.detection.ai_threat import AIThreatAnalyzer
analyzer = AIThreatAnalyzer()
stats = analyzer.get_analysis_stats()
print(f'Total cost: ${stats[\"total_cost\"]:.4f}')
print(f'Analyses performed: {stats[\"analyses_performed\"]}')
"

# Update package list
sudo apt update

# Install required system packages
sudo apt install -y python3 python3-pip python3-venv git curl

# Install development tools (for building some packages)
sudo apt install -y build-essential python3-dev libssl-dev libffi-dev

# Install additional dependencies for AI features
sudo apt install -y python3-tk  # For matplotlib if using visualization

# Clone repository
git clone https://github.com/yourusername/hawkeye.git
cd hawkeye

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Upgrade pip
pip install --upgrade pip

# Install dependencies
pip install -r requirements.txt

# Configure AI providers
cp env.example .env
nano .env  # Edit with your API keys

# Verify installation
python application.py info

# Install EPEL repository (for additional packages)
sudo yum install -y epel-release

# Install required packages
sudo yum install -y python3 python3-pip python3-devel git curl gcc openssl-devel libffi-devel

# Alternative for newer versions
sudo dnf install -y python3 python3-pip python3-devel git curl gcc openssl-devel libffi-devel

# Clone repository
git clone https://github.com/yourusername/hawkeye.git
cd hawkeye

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install --upgrade pip
pip install -r requirements.txt

# Configure
cp env.example .env
vi .env  # Edit with your API keys

# Verify
python application.py info

# Install Homebrew (if not already installed)
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# Install Python 3.9+
brew install [email protected] git curl

# Alternative: Use system Python with pyenv
brew install pyenv
pyenv install 3.9.0
pyenv global 3.9.0

# Clone repository
git clone https://github.com/yourusername/hawkeye.git
cd hawkeye

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install --upgrade pip
pip install -r requirements.txt

# Configure AI providers
cp env.example .env
open .env  # Edit with your preferred editor

# Verify installation
python application.py info

# Pull the latest image
docker pull hawkeye/hawkeye:latest

# Run with AI provider configuration
docker run -it --rm \
  -e AI_PROVIDER=anthropic \
  -e AI_ANTHROPIC_API_KEY=your_key_here \
  -v $(pwd)/results:/app/results \
  hawkeye/hawkeye:latest \
  detect local

# Run with configuration file
docker run -it --rm \
  -v $(pwd)/.env:/app/.env \
  -v $(pwd)/results:/app/results \
  hawkeye/hawkeye:latest \
  analyze-threats -i /app/results/detection.json

# Clone repository
git clone https://github.com/yourusername/hawkeye.git
cd hawkeye

# Build image
docker build -t hawkeye-local .

# Run with AI configuration
docker run -it --rm \
  --env-file .env \
  -v $(pwd)/results:/app/results \
  hawkeye-local \
  detect local -o /app/results/local.json

# Start services
docker-compose up -d

# Run analysis
docker-compose exec hawkeye python application.py detect local -o /app/results/local.json
docker-compose exec hawkeye python application.py analyze-threats -i /app/results/local.json

# Fork the repository on GitHub first, then:
git clone https://github.com/YOUR_USERNAME/hawkeye.git
cd hawkeye

# Add upstream remote
git remote add upstream https://github.com/original-owner/hawkeye.git

# Create development virtual environment
python3 -m venv venv-dev
source venv-dev/bin/activate

# Install development dependencies
pip install --upgrade pip
pip install -r requirements.txt
pip install -r requirements-dev.txt

# Install in development mode
pip install -e .

# Install pre-commit hooks
pre-commit install

# Install code quality tools
pip install black ruff mypy pytest pytest-cov

# Run code quality checks
black src/ tests/
ruff src/ tests/
mypy src/

# Set up development AI environment
cp env.example .env.dev

# Edit with development API keys (use separate keys for dev)
echo "AI_PROVIDER=anthropic" >> .env.dev
echo "AI_ANTHROPIC_API_KEY=your_dev_key" >> .env.dev
echo "AI_MAX_COST_PER_ANALYSIS=0.10" >> .env.dev

# Test AI development setup
python -c "
from src.hawkeye.detection.ai_threat import AIThreatAnalyzer
analyzer = AIThreatAnalyzer()
print('✅ AI development setup successful')
"

# Core settings
export HAWKEYE_DEBUG=true
export HAWKEYE_LOG_LEVEL=DEBUG
export HAWKEYE_LOG_FILE=/var/log/hawkeye.log

# AI provider settings
export AI_PROVIDER=anthropic
export AI_ANTHROPIC_API_KEY=your_key
export AI_FALLBACK_PROVIDER=openai
export AI_OPENAI_API_KEY=your_key
export AI_MAX_COST_PER_ANALYSIS=1.00
export AI_MAX_DAILY_COST=25.00

# Local LLM settings
export AI_LOCAL_LLM_ENDPOINT=http://localhost:11434
export AI_LOCAL_LLM_MODEL=llama3.1:8b
export AI_LOCAL_LLM_TIMEOUT=120

# Performance settings
export HAWKEYE_MAX_THREADS=50
export HAWKEYE_DEFAULT_TIMEOUT=30

python application.py --config-file hawkeye.yaml detect local

# Check installation
python application.py --version
python application.py info

# Test basic functionality
python application.py detect local --output test_local.json
cat test_local.json | head -20

# Test each configured provider
python application.py config show | grep -i ai

# Test OpenAI (if configured)
python -c "
import os
from src.hawkeye.detection.ai_threat.ai_providers import OpenAIProvider
try:
    provider = OpenAIProvider()
    print('✅ OpenAI provider initialized successfully')
except Exception as e:
    print(f'❌ OpenAI provider failed: {e}')
"

# Test Anthropic (if configured)
python -c "
import os
from src.hawkeye.detection.ai_threat.ai_providers import AnthropicProvider
try:
    provider = AnthropicProvider()
    print('✅ Anthropic provider initialized successfully')
except Exception as e:
    print(f'❌ Anthropic provider failed: {e}')
"

# Test Local LLM (if configured)
curl -s http://localhost:11434/api/tags || echo "❌ Local LLM not running"

# Complete workflow test
echo "🧪 Testing complete HawkEye workflow with AI analysis"

# Step 1: Detection
python application.py detect local --output workflow_test.json
echo "✅ Detection completed"

# Step 2: AI Analysis (with cost limit for safety)
python application.py analyze-threats \
  --input workflow_test.json \
  --cost-limit 0.50 \
  --output workflow_analysis.json
echo "✅ AI analysis completed"

# Step 3: Report generation
python application.py analyze-threats \
  --input workflow_test.json \
  --format html \
  --cost-limit 0.50 \
  --output workflow_report.html
echo "✅ HTML report generated"

echo "🎉 Full workflow test successful!"
echo "📊 View report: workflow_report.html"

# Check Python version
python --version
python3 --version

# If too old, install newer version
# Ubuntu/Debian
sudo apt install python3.9 python3.9-venv

# macOS
brew install [email protected]

# If venv creation fails
python3 -m pip install --user virtualenv
python3 -m virtualenv venv

# If activation fails on Windows
venv\Scripts\activate.bat  # Instead of .ps1

# If pip install fails
pip install --upgrade pip setuptools wheel

# If specific packages fail
pip install --only-binary=all package_name

# Clear pip cache
pip cache purge

# Check API key format
echo $AI_ANTHROPIC_API_KEY | head -c 20  # Should start with 'sk-ant'
echo $AI_OPENAI_API_KEY | head -c 20     # Should start with 'sk-'

# Test API key directly
curl https://api.anthropic.com/v1/models \
  -H "x-api-key: $AI_ANTHROPIC_API_KEY"

# Test connectivity
curl -I https://api.openai.com/v1/models
curl -I https://api.anthropic.com/v1/models

# Check proxy settings
echo $HTTP_PROXY
echo $HTTPS_PROXY

# Check Ollama status
systemctl status ollama  # Linux with systemd
brew services list | grep ollama  # macOS

# Check Ollama logs
journalctl -u ollama --no-pager -n 50  # Linux
brew services restart ollama  # macOS

# Test direct API
curl http://localhost:11434/api/generate \
  -d '{"model": "llama3.1:8b", "prompt": "Hello", "stream": false}'

# If permission denied on Linux/macOS
chmod +x application.py

# If docker permission denied
sudo usermod -a -G docker $USER
# Then logout and login again

# If log file permission denied
sudo mkdir -p /var/log/hawkeye
sudo chown $USER:$USER /var/log/hawkeye

# If analysis is slow
export AI_MAX_WORKERS=1  # Reduce parallel processing
python application.py analyze-threats \
  --input results.json \
  --sequential-processing

# If memory issues
python application.py analyze-threats \
  --input results.json \
  --confidence-threshold 0.8  # Filter results

# Monitor resource usage
htop  # Or Activity Monitor on macOS

# Remove virtual environment
rm -rf venv

# Remove project directory
cd ..
rm -rf hawkeye

# Remove configuration files (optional)
rm ~/.hawkeye.yaml
rm .env

# Remove all HawkEye files
rm -rf hawkeye/
rm -rf ~/.hawkeye/
rm -rf /var/log/hawkeye/

# Remove Docker images (if used)
docker rmi hawkeye/hawkeye:latest
docker rmi hawkeye-local

# Remove Ollama models (if no longer needed)
ollama rm llama3.1:8b
ollama rm llama3.1:70b

# Remove from shell profile (.bashrc, .zshrc, etc.)
# Remove lines containing:
# export AI_PROVIDER=
# export AI_ANTHROPIC_API_KEY=
# export AI_OPENAI_API_KEY=
# etc.

# Or temporarily unset
unset AI_PROVIDER
unset AI_ANTHROPIC_API_KEY
unset AI_OPENAI_API_KEY