TripSaver

This is a travel planner that uses AI to create a detailed travel plan based on the user's input, downloadable into a PDF or txt file. Plus, we have a LeBron James chatbot to advise you on your trip!

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Inspiration

Travel planning can be overwhelming—choosing destinations, budgeting, and finding local experiences all take time and effort. We wanted to create an AI-powered travel assistant that simplifies trip planning, providing personalized recommendations in seconds. TripSaver was born out of our love for travel and technology, aiming to make smart, efficient travel accessible to everyone.

What it does

TripSaver is an AI-powered travel planner that: ✅ Generates personalized itineraries based on budget, duration, and travel style ✅ Provides real-time destination insights and local tips ✅ Suggests accommodations, restaurants, and attractions ✅ Estimates a daily budget breakdown ✅ Helps travelers make informed decisions effortlessly

How we built it

Frontend: Built using Streamlit, allowing an interactive and minimalist UI Backend: Integrated Google Gemini AI for smart travel recommendations Search Tools: Used DuckDuckGo Search API to provide up-to-date travel information Deployment: Hosted on Streamlit Community Cloud, making it accessible with one click

Challenges we ran into

🔴 Finding real-time, accurate travel data while avoiding API rate limits 🔴 Ensuring a smooth user experience on Streamlit with dynamic inputs and responses 🔴 Handling conflicts during Git merges while collaborating 🔴 Deploying the app with restricted package support on Streamlit Cloud

Accomplishments that we're proud of

🏆 Successfully deploying TripSaver on Streamlit Community Cloud 🏆 Creating a seamless, easy-to-use AI travel assistant 🏆 Integrating real-time travel insights using DuckDuckGo search 🏆 Overcoming API limitations to generate reliable itineraries

What we learned

📌 How to optimize AI responses for travel planning 📌 Best practices for deploying Streamlit apps in the cloud 📌 The importance of real-time web scraping for dynamic travel data 📌 Collaboration in a version-controlled environment with Git

What's next for TripSaver

🚀 Expand AI capabilities for more personalized trip suggestions 🚀 Add real-time flight & hotel booking integrations 🚀 Enhance UI/UX with interactive maps and better visualizations 🚀 Support multi-user itineraries for group travel planning

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About the Project: TripSaver

  1. Inspiration Travel planning can be overwhelming—choosing destinations, budgeting, and finding local experiences all take time and effort. We wanted to create an AI-powered travel assistant that simplifies trip planning, providing personalized recommendations in seconds. TripSaver was born out of our love for travel and technology, aiming to make smart, efficient travel accessible to everyone.

  2. What It Does TripSaver is an AI-powered travel planner that: Generates personalized itineraries based on budget, duration, and travel style Provides real-time destination insights and local tips Suggests accommodations, restaurants, and attractions Estimates a daily budget breakdown Helps travelers make informed decisions effortlessly

  3. How We Built It Frontend: Built using Streamlit, allowing an interactive and minimalist UI Backend: Integrated Google Gemini AI for smart travel recommendations Search Tools: Used DuckDuckGo Search API to provide up-to-date travel information Deployment: Hosted on Streamlit Community Cloud, making it accessible with one click

  4. Challenges We Ran Into Finding real-time, accurate travel data while avoiding API rate limits Ensuring a smooth user experience on Streamlit with dynamic inputs and responses Handling conflicts during Git merges while collaborating Deploying the app with restricted package support on Streamlit Cloud

  5. Accomplishments We're Proud Of Successfully deploying TripSaver on Streamlit Community Cloud Creating a seamless, easy-to-use AI travel assistant Integrating real-time travel insights using DuckDuckGo search Overcoming API limitations to generate reliable itineraries

  6. What We Learned How to optimize AI responses for travel planning Best practices for deploying Streamlit apps in the cloud The importance of real-time web scraping for dynamic travel data Collaboration in a version-controlled environment with Git

  7. What's Next for TripSaver? Expand AI capabilities for more personalized trip suggestions Add real-time flight & hotel booking integrations Enhance UI/UX with interactive maps and better visualizations Support multi-user itineraries for group travel planning

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