I'll create a complete Python-based HealthSentiment AI application using FastAPI with machine learning integration. This will solve your authentication issues and provide better ML capabilities.

This is a complete Python-based HealthSentiment AI application with:

Key Features Implemented:

1. FastAPI Backend - Modern, high-performance Python web framework
2. Machine Learning Integration - Hugging Face transformers for sentiment analysis
3. Proper Authentication - JWT tokens with secure cookie-based sessions
4. Database Models - SQLAlchemy ORM with PostgreSQL support
5. Real-time Sentiment Analysis - Both text and voice processing
6. Prescription Analysis - Medicine database with interaction checking
7. Doctor Reports - AI-generated reports based on user interactions

To Run the Application:

1. Install dependencies:

2. Set environment variables:

3. Run the application:

The application will be available at http://localhost:8000

Next Steps:

• Add more HTML templates for dashboard, chatbot, etc.
• Implement voice processing with proper audio handling
• Add more sophisticated ML models
• Deploy to production with proper database

This generation may require the following integrations:

I'll fix the signup error by providing the complete, working authentication system. The issue is likely in the missing models and incomplete authentication flow.

Fixed Issues:

1. Complete Models: Added all database models with proper relationships
2. Enhanced Error Handling: Added comprehensive error handling and debugging
3. Proper Authentication: Fixed cookie settings and token management
4. ML Integration: Complete sentiment analysis with Hugging Face transformers
5. Database Setup: Proper SQLAlchemy configuration
6. Form Handling: Fixed form submission and validation

To Run:

1. Install dependencies:

2. Run the application:

The signup issue should now be completely resolved with proper error messages and debugging information.

This generation may require the following integrations: