This project is an AI-powered clothing tag identification and similarity search system. It analyzes clothing tag images, identifies brands using computer vision, and finds similar tags from a database. The system uses advanced AI techniques including image embeddings, text similarity, and (optionally) LLM-based filtering to provide accurate tag matching and recommendations.

Features

Tag Identification: Uses computer vision to identify clothing tag brands from images
Text-Based Matching: Implements TF-IDF and cosine similarity for tag name matching
Image Similarity Search: Uses CLIP embeddings and FAISS for visually similar tag images
LLM Enhancement: Optional LLM analysis for improved similarity filtering
Metadata Extraction: Provides appraisal values, years, and status information for similar tags
Simple Frontend: Web UI to upload image URL, toggle LLM, and view results visually

Tech Stack

Python, Flask
CLIP (Hugging Face), FAISS, scikit-learn, pandas, numpy
OpenAI (optional, for LLM)
HTML/CSS/JS frontend (Flask template)

Setup & Installation

Clone the Repository

git clone <repository-url>
cd ds_task_tag_scan_project/ds_task/backend

Create and Activate Virtual Environment

python3 -m venv ../venv
source ../venv/bin/activate

Install Requirements
```
pip install -r requirements.txt
```
Set Environment Variables (if using LLM)
```
export OPENAI_API_KEY=your-openai-key
```
Run the App
```
python app.py
```
(For production, use Docker or a Linux server for stability.)

Usage

Go to http://localhost:8000/ in your browser
Enter a tag image URL
Toggle "Use LLM Similarity" if desired
Click "Scan Tag" to see results (tag info, similar images, metadata)

File Structure

backend/ - Flask app, ML/DS logic, templates
data/ - Tag guides, expert and community CSVs
docs/ - Documentation (this file, API doc)

Notes

For best stability, run in a Linux environment or Docker.
On Mac, the app is configured to use only one thread for all ML/numerical libraries.
LLM similarity requires a valid OpenAI API key.

See API_Documentation.md for API details.