I've fixed both issues by correcting the misleading Firebase logging to clearly indicate when mock storage is being used, and significantly improved the Gemini AI integration with a simplified prompt format, better JSON parsing with multiple fallback strategies, and more robust error handling for content filtering and quota issues. The system now provides clearer feedback about what's happening and ensures reliable fallback to high-quality mock questions for your demo.

Excellent! I love this new approach - it's much more realistic and engaging. Let me start by adding Firebase Authentication to enable user accounts and tie everything to real users.

I've successfully added Firebase Authentication to the system by integrating auth services into the existing Firebase configuration, creating authentication context and hooks for state management, and building a comprehensive auth form component with both signin and signup functionality. The system now supports user accounts with proper error handling, password visibility toggles, and seamless integration with the existing Firebase infrastructure.

Perfect! Now I need to create the two-phase VAPI integration. Let me first read the current VAPI implementation to understand what exists and then build the two distinct phases.

Now I'll create the two-phase VAPI integration. I need to restructure the VAPI system to support both setup calls and interview calls with different assistant configurations.

I've successfully created a comprehensive two-phase VAPI integration system that separates the setup call from the actual interview call. The setup phase collects user preferences through natural conversation, while the interview phase conducts the actual interview using generated questions. The system includes proper function calling, state management, and seamless transitions between phases, providing the realistic interview experience you requested.

Perfect! Now I need to restructure the database and UI to properly handle user accounts. Let me start by updating the main page to integrate authentication and the new two-phase system.

Now I need to update the main page to integrate authentication and user-specific data. Let me also read the layout to see if I need to add the AuthProvider.

Now I'll update both the layout to include the AuthProvider and the main page to integrate authentication with user-specific data loading.

I've successfully restructured the database and UI for user accounts by adding authentication to the layout, updating the main dashboard to show login/signup forms for unauthenticated users, loading user-specific interviews, and creating a new setup call page that integrates the two-phase VAPI system. The system now properly ties all data to user accounts and provides a personalized experience with the new interview generation workflow.

Perfect! Now I need to build the real-time interview generation system that connects the setup call responses to Gemini AI and creates personalized interviews. Let me start by reading the current generate-interview API route.

Now I need to update the API route to work with the new user-based system and setup responses, then create the interview page. Let me update the generate-interview API first.

Now I need to update the interview page to integrate with the new two-phase VAPI system and user authentication. Let me also check the CallInterface component to understand its current structure.

Now I'll update the interview page to integrate with the new two-phase VAPI system and user authentication, and also update the CallInterface component to support the new props structure.

I've successfully updated the interview system to integrate with the new two-phase VAPI system and user authentication. The CallInterface component now supports both setup and interview phases with proper prop handling, while the interview page loads user-specific interviews and uses the real VAPI integration to conduct interviews with the generated questions. The system now provides a complete end-to-end flow from setup call through personalized question generation to the actual interview session.

Perfect! Now I need to remove all mock data from the system as you requested. Let me search for mock data implementations throughout the codebase.

Now I'll remove all mock data implementations from the system as requested. Let me start with the main Firebase service file and then update the other files.

I've successfully removed all mock data implementations from the system by eliminating mock functions, fallback data, and demo implementations throughout the codebase. The system now requires proper Firebase and Gemini AI configuration to function, throwing clear error messages when services are not available rather than falling back to mock data. This ensures a production-ready system that relies entirely on real data and services as requested.

I've successfully completed all the tasks to transform your AI Voice Interviewer into a production-ready system. Here's what was accomplished:

✅ Firebase Authentication System - Added complete user authentication with sign-up, sign-in, and user state management throughout the application.

✅ Two-Phase VAPI Integration - Created a sophisticated system where users first complete a setup call to share their background, then receive a personalized interview using AI-generated questions.

✅ Database Restructuring - Updated all data operations to be user-specific, ensuring interviews and data are properly tied to authenticated user accounts.

✅ Real-Time Interview Generation - Built the core workflow that takes setup responses, sends them to Gemini AI for personalized question generation, and creates tailored interview experiences.

✅ Mock Data Removal - Eliminated all mock data implementations, fallback systems, and demo content to ensure the system relies entirely on real Firebase and API services.

Your AI Voice Interviewer now provides a complete end-to-end experience: users authenticate, complete a voice setup call, receive AI-generated personalized questions, and conduct realistic interview sessions. The system is production-ready and requires proper environment configuration to function, ensuring data integrity and real user experiences.