start_local.sh

#!/bin/bash

# start_local_ai.sh
# -----------------
# Starts the Dolphin AI Worker locally for the demo.
# 
# Usage:
#   1. Run this script: ./start_local_ai.sh
#   2. In a separate terminal, expose port 7860 using ngrok:
#      ngrok http 7860
#   3. Copy the ngrok URL (e.g., https://xyz.ngrok-free.app)
#   4. Set DOLPHIN_API_URL on Render to that URL.

echo "🚀 Starting Visique Local AI Worker..."
echo "----------------------------------------"

# Ensure we are in the project root (relative to script location)
cd "$(dirname "$0")/../../"

# Check for required python dependencies
python3 -c "import uvicorn, fastapi, torch" 2>/dev/null
if [ $? -ne 0 ]; then
    echo "⚠️  Missing Python dependencies. Installing..."
    pip3 install -r visique/ai-worker/requirements.txt
    if [ $? -ne 0 ]; then
        echo "❌ Failed to install dependencies."
        echo "   Try running: pip3 install -r visique/ai-worker/requirements.txt"
        exit 1
    fi
fi

# Check if model files exist
# (DolphinClient handles lazy loading, but let's warn if missing)
if [ ! -d "visique/backend/models/dolphin-v2" ]; then
    echo "⚠️  Model directory not found at visique/backend/models/dolphin-v2"
    echo "   The application will attempt to download it on first run."
fi

# Check for ngrok
if ! command -v ngrok &> /dev/null; then
    echo "⚠️  'ngrok' command not found!"
    echo "   You need it to create the public tunnel."
    echo "   👉 Install command: brew install ngrok/ngrok/ngrok"
    echo "   (Or download from https://ngrok.com/download)"
    echo ""
    read -p "   Press Enter to continue anyway (if you installed it elsewhere)..."
fi

# Generate a random API key if not set, OR use a fixed default for convenience
# Using a fixed key means you don't have to update Render every time you restart.
if [ -z "$DOLPHIN_API_KEY" ]; then
    export DOLPHIN_API_KEY="visique-local-demo-key"
fi

# Set dummy env vars to satisfy backend Pydantic validation
# (The worker doesn't use the DB, but imports trigger the check)
export SECRET_KEY="dummy-secret-for-worker"
export DATABASE_URL="postgresql://dummy:dummy@localhost/dummy"

# Set python path to include backend
export PYTHONPATH=$PYTHONPATH:$(pwd)/visique/backend

echo "🔒 Security: API Key enabled"
echo "🔑 Key: $DOLPHIN_API_KEY"
echo ""

# Run the worker app
echo "🔥 Launching worker on http://0.0.0.0:7860"
echo "   (This requires ~16GB RAM for the model)"

# Use python -m to run uvicorn, pointing to the renamed worker.py
# We use --app-dir to set the root, avoiding package issues with hyphens
python3 -m uvicorn worker:app --app-dir visique/ai-worker --host 0.0.0.0 --port 7860 --reload