Aherobo Ovie Victor
65 lines
2.3 KiB
Markdown
65 lines
2.3 KiB
Markdown
# Tag Scan Mini App
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## Overview
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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.
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## Features
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- **Tag Identification:** Uses computer vision to identify clothing tag brands from images
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- **Text-Based Matching:** Implements TF-IDF and cosine similarity for tag name matching
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- **Image Similarity Search:** Uses CLIP embeddings and FAISS for visually similar tag images
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- **LLM Enhancement:** Optional LLM analysis for improved similarity filtering
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- **Metadata Extraction:** Provides appraisal values, years, and status information for similar tags
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- **Simple Frontend:** Web UI to upload image URL, toggle LLM, and view results visually
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## Tech Stack
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- Python, Flask
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- CLIP (Hugging Face), FAISS, scikit-learn, pandas, numpy
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- OpenAI (optional, for LLM)
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- HTML/CSS/JS frontend (Flask template)
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## Setup & Installation
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1. **Clone the Repository**
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```bash
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git clone <repository-url>
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cd ds_task_tag_scan_project/ds_task/backend
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```
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2. **Create and Activate Virtual Environment**
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```bash
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python3 -m venv ../venv
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source ../venv/bin/activate
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```
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3. **Install Requirements**
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```bash
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pip install -r requirements.txt
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```
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4. **Set Environment Variables** (if using LLM)
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```bash
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export OPENAI_API_KEY=your-openai-key
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```
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5. **Run the App**
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```bash
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python app.py
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```
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(For production, use Docker or a Linux server for stability.)
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## Usage
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- Go to [http://localhost:8000/](http://localhost:8000/) in your browser
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- Enter a tag image URL
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- Toggle "Use LLM Similarity" if desired
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- Click "Scan Tag" to see results (tag info, similar images, metadata)
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## File Structure
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- `backend/` - Flask app, ML/DS logic, templates
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- `data/` - Tag guides, expert and community CSVs
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- `docs/` - Documentation (this file, API doc)
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## Notes
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- For best stability, run in a Linux environment or Docker.
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- On Mac, the app is configured to use only one thread for all ML/numerical libraries.
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- LLM similarity requires a valid OpenAI API key.
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---
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See `API_Documentation.md` for API details. |