AuthentiQ

An AI-powered luxury goods identifier built with Python, Flask, and Google Gemini Vision API.

What it does

Upload a photo of any luxury item — bags, watches, shoes, accessories — and get:

• Brand and exact model identification
• Retail and resale market price
• How to spot a fake (physical checks)
• Interesting facts about the item

Note: Authenticity detection uses Gemini Vision's general reasoning, not a model trained specifically for counterfeit detection. It works well as a first-pass heuristic but isn't always reliable on subtle fakes — treat its verdict as advisory, not definitive.

Tech Stack

• Backend: Python, Flask
• AI: Google Gemini Vision API (gemini-2.5-flash)
• Frontend: HTML, CSS, JavaScript
• Deployment: Render

Setup

1. Clone the repository

git clone https://github.com/AkshitaSharma211/AuthentiQ.git

2. Install dependencies

pip install -r requirements.txt

3. Add your Gemini API key in .env

4. Run the app

5. Open http://127.0.0.1:5000

Live Demo

AuthentiQ Live

What I Learned

• Integrating vision AI APIs with multimodal inputs
• Building REST APIs with Flask
• Handling file uploads and base64 encoding
• Frontend to backend communication
• Environment variables and API security
• Production deployment on Render