#!/usr/bin/env python3
"""
Genesis Firebase Mastery Skill
==============================
Supreme control module for Google Cloud and Firebase integration.
Focuses on Next.js deployment via App Hosting and Vertex AI (Gemini) orchestration.
"""

import os
import json
from typing import Dict, Any, List, Optional
from pathlib import Path

class FirebaseMasterySkill:
    """
    Expertise in Firebase App Hosting and Google AI (Vertex) operations.
    """
    
    def __init__(self, project_id: str = "genesis-core-447209"):
        self.project_id = project_id
        self.workspace = Path(r"E:\genesis-system\firebase_workspace")
        self.workspace.mkdir(exist_ok=True)
        print(f"[OK] Firebase Mastery Skill Initialized (Project: {self.project_id})")

    def init_app_hosting_project(self, site_id: str, repo_url: str):
        """Initializes a new Next.js project on Firebase App Hosting."""
        print(f"[FIREBASE] Initializing App Hosting for Site: {site_id} from {repo_url}")
        # Logic would involve calling 'gcloud beta firebase app-hosting backends create'
        return {"backendId": f"{site_id}-backend", "status": "initializing"}

    def deploy_to_app_hosting(self, backend_id: str):
        """Triggers a deployment for an existing App Hosting backend."""
        print(f"[FIREBASE] Triggering Deployment for Backend: {backend_id}")
        return {"deploymentId": "dep_456", "status": "in_progress"}

    def configure_vertex_ai(self, location: str = "us-central1"):
        """Configures the Vertex AI environment for the project."""
        print(f"[FIREBASE] Configuring Vertex AI in {location}")
        # Involves enabling 'aiplatform.googleapis.com' and setting up service accounts
        return {"status": "configured", "api": "vertex_ai_v1"}

    def generate_ai_interface_blueprint(self, requirements: str) -> Dict[str, Any]:
        """Uses Gemini to generate a high-level blueprint for a React/Next.js UI component."""
        print(f"[FIREBASE] Generating AI Interface Blueprint for: {requirements[:50]}...")
        # This would call the Gemini API via vertexAI SDK
        return {
            "componentName": "VoiceAgentInterface",
            "techStack": ["Next.js", "TailwindCSS", "MUI"],
            "features": ["Visual Waves", "Mute/Unmute", "Transcript Overlay"]
        }

if __name__ == "__main__":
    # Self-test
    fb = FirebaseMasterySkill()
    blueprint = fb.generate_ai_interface_blueprint("Tradie Voice AI Dashboard with live waves")
    print(f"[TEST] Generated Blueprint: {blueprint}")