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+# Tag Scan Mini App
+
+## Overview
+
+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
+
+1. **Clone the Repository**
+   ```bash
+   git clone <repository-url>
+   cd ds_task_tag_scan_project/ds_task/backend
+   ```
+2. **Create and Activate Virtual Environment**
+   ```bash
+   python3 -m venv ../venv
+   source ../venv/bin/activate
+   ```
+3. **Install Requirements**
+   ```bash
+   pip install -r requirements.txt
+   ```
+4. **Set Environment Variables** (if using LLM)
+   ```bash
+   export OPENAI_API_KEY=your-openai-key
+   ```
+5. **Run the App**
+   ```bash
+   python app.py
+   ```
+   (For production, use Docker or a Linux server for stability.)
+
+## Usage
+- Go to [http://localhost:8000/](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.