AI-Powered Travel Recommendation System

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Welcome! This repository showcases my latest project: an AI-Powered Travel Recommendation System that leverages cutting-edge AI technologies such as vector embeddings, language models, and RAG (Retrieval-Augmented Generation) architecture to deliver personalized destination recommendations. 🧳🤖✈️

This project demonstrates the practical application of artificial intelligence in the travel domain. By combining recommendation algorithms, semantic search, and real-time data integration, the system helps travelers make more informed travel decisions with minimal effort.

🧠 AI Integration & Natural Language Processing ✅ Implemented HuggingFace Zephyr-7B for generating contextual travel summaries.

✅ Deployed FAISS vector database for semantic similarity matching.

✅ Developed intelligent filtering algorithms based on user preferences, budget constraints, and seasonal factors.

✅ Integrated OpenWeatherMap API for live weather updates.

✅ Built interactive mapping using Folium.

✅ Dynamic content reveal for an engaging user experience.

✅ Frontend built with Python and Streamlit.

✅ Integrated LangChain for AI workflow orchestration.

✅ Vector embeddings for advanced search capabilities.

✅ Responsive design with an intuitive interface.

User Input: Travelers enter their preferences (destination type, budget, season, etc.).

Semantic Search: The system finds the most relevant destinations using vector similarity.

Contextual Generation: The LLM generates personalized travel summaries.

Real-Time Updates: Live weather and map visuals enrich the recommendations.

Python

Streamlit

FAISS

HuggingFace Transformers

LangChain

Folium

OpenWeatherMap API