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Travel Planner Assistant

An AI-powered travel planning assistant that helps users create personalized travel itineraries through conversation.

Click here to see demo

  • Note: Due to my endsemester exams, I am able to just meet basic requirements of the project. I will be updating the project with more features and improvements in the future.

Requirements

  • Python 3.9+
  • SQLite3
  • Ollama (for LLM)

Architecture Overview

Frontend (Streamlit)

  • Built with app.py
  • Uses Streamlit for the UI components and session management
  • Features secure cookie-based authentication
  • Real-time chat interface with message history
  • Sidebar navigation for multiple chat sessions

Backend (FastAPI)

Data Flow

  1. Authentication Flow

    • User credentials → FastAPI /# or /register endpoints
    • JWT token generation and verification
    • Token stored in secure cookies and session state

  2. Chat Flow

    • User message → Streamlit frontend
    • Request with auth token → FastAPI backend
    • Message processing with LLMs via Ollama:

      1. User Input → TinyLLAMA (Extracts key information from user prompt):

        • Location preferences
        • Date ranges
        • Budget constraints
        • Travel interests

      2. Processed Information → LLAMA2:

        • Generates detailed travel plans
        • Maintains conversation context
        • Provides recommendations
    • Response stored in database and returned to frontend

  3. Database Structure

    • Users table: Authentication data
    • Chats table: Conversation sessions
    • Chat_messages table: Message history

AI Implementation

  • Uses Ollama with LLaMA2 model
  • Context-aware travel planning using TravelContext
  • Progressive information gathering for:
    • Location
    • Dates
    • Budget
    • Interests
  • Dynamic prompt engineering based on missing information

Setup Instructions

1. Install Dependencies

pip install -r requirements.txt

2. Setup Ollama (below for macOS):

  • In one terminal, Install Ollama using Homebrew and serve the server:
brew install ollama
ollama serve
  • In another terminal, pull the LLaMA2 and TinyLLaMA models:
ollama pull llama2
ollama pull tinyllama

3. Run FastAPI Server

uvicorn backend.main:app --reload

4. Run Streamlit App

streamlit run frontend/app.py

5. Open http://localhost:8501 in your browser and happy planning!

6. Checkout my demo here

Features

  • Chat-based travel planning
  • Persistent conversation history
  • User authentication using Json Web Tokens
  • Real-time AI responses
  • Multi-chat support
  • Weather preferences
  • Budget constraints
  • Location-based recommendations
  • Date-based recommendations
  • Interest-based recommendations
  • Dynamic prompt generation

Future Work

The project can be extended in the following ways:

  • Implement more advanced AI models
  • Add support for multiple languages
  • Integrate with external APIs for real-time data
  • Map-based itinerary visualization with Map APIs
  • Admin panel for user management

DataBase Schema

users (
    username TEXT PRIMARY KEY,
    password TEXT NOT NULL
)

chats (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    username TEXT NOT NULL,
    title TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (username) REFERENCES users(username)
)

chat_messages (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    chat_id INTEGER NOT NULL,
    user_input TEXT NOT NULL,
    bot_response TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (chat_id) REFERENCES chats(id) ON DELETE CASCADE
)