WanderWise: AI-Powered Travel Planner

WanderWise is an intelligent travel planning tool that integrates AI, real-time web search, and YouTube video data to provide users with highly personalized travel recommendations. Users can create, modify, and download detailed itineraries with ease through a conversational chatbot interface.

1. Deployed Application - http://13.59.247.191:8501/
2. Deployed FastAPI (Backend) - http://13.59.247.191:8000/docs
3. youtube video link: https://youtu.be/xKh24o73VIQ

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Key Features

• Personalized Recommendations:
  • Travel itineraries tailored to user preferences.
• Dual Helper System:
  • RAG Helper: Fetches stored data from a Pinecone vector database.
  • Web Search Helper: Provides real-time updates using web APIs.
• YouTube Integration:
  • Enriches recommendations with YouTube travel content.
• User-Friendly Design:
  • Built with Streamlit (Frontend) and FastAPI (Backend).
• Downloadable Itineraries:
  • Users can export travel plans as PDFs.
• Persionalised Budget:
  • Users can create personalized budgets based on their plans.

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Tools and Technologies

• Programming Languages: Python
• AI Models: OpenAI GPT-3.5 Turbo, OpenAI Text Ada
• Databases: Pinecone Vector Database
• APIs: YouTube API, Tavily Web Search API
• Development Tools: Streamlit, FastAPI, Docker

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Folder Structure

.github/workflows/
    deployment.yml                # GitHub deployment workflow

Streamlit/
    app.py                        # Main Streamlit application file
    chat_page.py                  # Chatbot interface
    login_page.py                 # User login functionality
    signup_page.py                # User signup functionality
    welcome_page.py               # Welcome page for the app
    budget.py                     # Personalised budget creation
    requirements.txt              # Python dependencies for Streamlit
    Dockerfile                    # Docker setup for the Streamlit app
    travel_itinerary.pdf          # Sample exported itinerary

fast_api/
    main.py                       # FastAPI backend main file
    openai_response.py            # AI response handling
    websearch_normal.py           # Handles real-time web search
    youtube_search.py             # YouTube data integration
    requirements.txt              # Python dependencies for FastAPI
    Dockerfile                    # Docker setup for the FastAPI backend

youtube/
    youtube.py                    # YouTube data management
    transcripts.py                # Processing YouTube transcripts

docker-compose.yml                # Docker Compose file for orchestrating services

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Architecture Diagram

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How to Run the Project

Prerequisites

• Python 3.10.12
• Docker installed

Steps

1. Clone the repository.

   git clone <repo_url>
   cd <repo_directory>

2. Start the backend with Docker:

   docker-compose up --build

3. Access the application on your browser at http://localhost:8501.

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License

MIT License

Copyright (c) 2024 NOISHEE (Nishita Vijay Matlani) and Abhinav Gupta

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.