An intelligent travel planning application that uses HuggingFace's Llama 4 model to generate personalized travel itineraries and research destinations. Built with Streamlit for an intuitive web interface.

• 🔍 Smart Destination Research: AI-powered research for attractions, accommodations, dining, and transportation
• 📅 Personalized Itinerary Generation: Custom day-by-day travel plans based on your preferences
• 💡 Travel Tips: Essential tips and local insights for your destination
• 📱 Multiple Travel Styles: Support for Honeymoon, Adventure, Cultural, Luxury, Family, Solo, and Business travel
• 💰 Budget-Aware Planning: Tailored recommendations for different budget ranges
• 🚶 Transportation Preferences: Plans adapted to walking, public transport, car rental, or mixed mobility
• 📄 Export Options: Download your itinerary as a text file
• 🔄 Offline Mode: Basic templates available when API is unavailable

• Python 3.12 or higher
• HuggingFace API key (optional but recommended for AI features)

1. Clone the repository:

   git clone https://github.com/thrishank007/AI-Travel-Planner
   cd AI-Travel-Planner

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   streamlit run src/app.py

4. Open your browser to http://localhost:8501

1. Go to HuggingFace Settings
2. Create a new token with 'Read' permissions
3. Enter the token in the sidebar of the application

1. Configure your preferences in the sidebar:

   • Enter your HuggingFace API key
   • Select travel style, budget range, and interests
   • Choose accommodation and transportation preferences

2. Enter trip details:

   • Destination (e.g., "Tokyo, Japan")
   • Start and end dates
   • Number of travelers
   • Special requirements

3. Generate your plan:

   • 🔍 Research Destination: Get comprehensive information about your destination
   • 📅 Generate Full Itinerary: Create a detailed day-by-day plan
   • 💡 Quick Tips: Get essential travel tips

4. Export and save:

   • Download your itinerary as a text file
   • View detailed research results and itinerary in expandable sections

• Travel Styles: Honeymoon, Adventure, Relaxation, Cultural, Luxury, Family, Solo, Business
• Budget Ranges: Budget ($), Mid-range ($$), Premium ($$$), Luxury ($$$$)
• Accommodations: Hotels, Hostels, Airbnb, Resorts, Boutique Hotels, Camping
• Interests: Food & Dining, Museums, Nature, Nightlife, Shopping, History, Art, Sports, Photography
• Transportation: Walking, Public Transport, Car Rental, Mixed

gt_hk/
├── src/
│   └── app.py              # Main Streamlit application
├── requirements.txt        # Python dependencies
└── README.md              # This file

• get_hf_client(): Initializes HuggingFace Inference Client
• hf_chat_completion(): Handles chat completion with error handling
• HFChatAgent: Custom agent class for AI-powered responses
• OfflineAgent: Fallback agent for offline mode with templates

• Primary Model: meta-llama/Llama-4-Scout-17B-16E-Instruct
• Provider: Together AI via HuggingFace
• Max Tokens: 2048 per response

streamlit              # Web application framework
huggingface_hub       # HuggingFace API integration
textwrap              # Text formatting utilities
datetime              # Date and time handling
os                    # Operating system interface

1. Configuration Sidebar:

   • API key input
   • Travel preferences
   • Budget and style selection

2. Trip Details:

   • Destination input
   • Date selection
   • Traveler count
   • Special requirements

3. Quick Actions:

   • Feature overview cards
   • Action buttons

4. Results Display:

   • Research results
   • Generated itinerary
   • Export options

• Gradient header design
• Responsive card layouts
• API status indicators
• Color-coded sections
• Mobile-friendly interface

The application includes comprehensive error handling for:

• API Connection Issues: Graceful fallback to offline mode
• Invalid API Keys: Clear error messages and setup instructions
• Model Unavailability: Automatic retry mechanisms
• Input Validation: Checks for required fields and valid dates
• Network Errors: Timeout and connection error handling

When no API key is provided, the application operates in offline mode with:

• Research Templates: Basic destination research structure
• Itinerary Templates: Generic day-by-day planning templates
• Travel Tips Templates: Essential travel preparation guidelines

Error: 401 Unauthorized

• Verify your HuggingFace API key is correct
• Ensure the token has proper permissions
• Try regenerating your API key

Error: Model not available

• Some models may be temporarily unavailable
• Try again later or use offline mode
• Check HuggingFace status page

Slow responses

• Free tier APIs have rate limits
• Consider upgrading to paid tiers for faster responses
• Use offline mode for immediate basic results

Dependencies not installing

• Ensure Python 3.1+ is installed
• Try using a virtual environment
• Update pip: pip install --upgrade pip

Streamlit not starting

• Check if port 8501 is available
• Try: streamlit run src/app.py --port 8502

• 📧 Email itinerary functionality
• 📱 Social sharing features
• 🗺️ Interactive maps integration
• 💾 Save/load itinerary profiles
• 🌐 Multi-language support
• 📊 Budget tracking and expense estimation
• 🎨 Custom itinerary themes
• 🔄 Real-time weather integration

This project is open source and available under the MIT License.

Contributions are welcome! Please feel free to submit a Pull Request.

If you encounter any issues or have questions:

1. Check the troubleshooting section above
2. Review the HuggingFace documentation
3. Open an issue in the repository

Built with ❤️ using Streamlit and HuggingFace

🌟 Plan smarter, travel better!