Abstract:
The Car Resale Value Recommendation System is a lightweight, web-based AI application built using Streamlit, LangChain, and OpenRouter's LLM APIs. The platform aims to help users, particularly car buyers and sellers, get insightful car recommendations based on resale value within a specific budget and year. Users input the car model, year, and budget, and the system returns alternative models that offer better resale value in the same price range. The solution integrates LLMS to analyze market patterns, trends, and car valuation logic dynamically.
The system architecture comprises a simple Streamlit frontend, a LangChain-powered LLM backend, and pyngrok for hosting the app temporarily on a public-facing URL. The use of OpenRouter with the Mistral-7B Instruct model ensures fast and accurate generation of car- related financial advice. Key features include intelligent query generation, real-time responses, and a fully modular and extensible design. The application demonstrates the power of LLMs to deliver intelligent business use cases with minimal infrastructure.
