👗 Fashion Sence AI

(Fashion Visual Search & Personalized Styling Assistant)

A modern AI-powered web application that helps users search for visually similar fashion products, receive outfit recommendations based on their style preferences and simulate personalized experiences using recent trends and LLMs — all in one elegant interface built with Streamlit, CLIP, and Gemma LLM.

🌐 Website - DEMO

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🧠 Project Overview

Traditional fashion search engines rely on tags or manual filters. Our system enhances user experience by allowing:

• Image-based Search: Upload any fashion item image to retrieve visually similar products.
• Text-based Search: Enter queries like "oversized hoodie, floral print dress" and retrieve matching products.
• Personalized Suggestions: Based on your browsing or simulated history.
• Intelligent Outfit Completion: Generate LLM-powered suggestions to complete your outfit using trending insights.

This app can serve as a virtual fashion assistant, a personal stylist, or a foundation for an e-commerce AI integration.

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🚀 Features at a Glance

Module	Description
🔍 Visual Search	Upload an image or type a query to find similar fashion products.
🧠 LLM-based Outfit Generator	Uses Gemma 3B via Hugging Face API to suggest outfit completions.
👤 History Simulation	Fake user history generation + product-based similarity suggestions.
📈 Fashion Trend Inference	Extracts trending keywords from inventory and online data.
📊 FAISS Indexing	Efficient nearest-neighbor search using pretrained embeddings.
🌐 Hugging Face Integration	Seamless API token handling and LLM inference.
✅ Modular Pipeline	Each part of the pipeline is reusable, testable, and extensible.

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🗂️ Project Structure

Fashion AI/
├── Assets/                  # FAISS Index, product IDs, and image embeddings
├── Data/                    # Cleaned CSV files for jeans and dresses
├── Modules/                 # Modular Python scripts for each major functionality
│   ├── dataloader.py
│   ├── faiss_index.py
│   ├── outfit_suggester.py
│   ├── preprocessing.py
│   ├── search.py
│   ├── trends.py
│   └── user_profile.py
├── Src/                     # Static assets like animation GIFs or logos
├── Notebooks/               # Python Jupyter Notebooks
├── Test_Images/             # Sample test images
├── app.py                   # Main Streamlit app file
├── requirements.txt         # Required Python libraries
└── README.md                # You're here!

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🛠️ Tech Stack

• Frontend: Streamlit
• Image Embeddings: CLIP (ViT-L/14)
• Text Embeddings: SentenceTransformers
• LLM Suggestion Engine: Gemma-3B (via HF Inference API)
• Nearest Neighbors Search: FAISS
• Trends Analysis: Web scraping + TF-IDF on product metadata
• Deployment-ready: Works on Streamlit Cloud

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📦 Installation & Setup

Recommended Python version: >=3.11

1. Clone the repository

git clone https://github.com/MohitGupta0123/Fashion-Sense-AI.git
cd Fashion-Sense-AI

2. Install the dependencies

pip install -r requirements.txt

3. Run the application

streamlit run app.py

4. Enter Hugging Face Token

• Get your free token from: https://huggingface.co/settings/tokens
• Paste it in the sidebar to access outfit suggestions using the Gemma model.

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🧭 How to Use

Once the app is running:

👤 Step-by-Step

1. Upload an image or enter a style description like "striped top, loose fit".
2. Adjust the number of desired results using the slider.
3. View Top Matching Products — displayed with image and price.
4. Click 🧪 Simulate Fake History to create browsing history (or use your own).
5. See Suggestions Based on History.
6. Tap 🧠 Generate Outfit to let the LLM suggest fashion complements (shoes, accessories, layering items, etc.).

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🖼️ UI Screenshots

🧪 Main Landing Page

📥 Uploaded Image

👕 Visually Similar Products

🧾 Fake History Simulation

🧠 History-Based Recommendations

🎯 Outfit Suggestions

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🧪 Sample Use Cases

• 🔍 A user uploads a black denim jacket → finds 10 similar styles instantly.
• 🛍️ A shopper queries for "Off Shoulder dresses" → retrieves relevant dresses.
• 🤖 Fake user browsing history is generated → recommendations are shown.
• 🎨 LLM completes the outfit with accessories and layering items based on the uploaded look.

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🔐 Hugging Face Token Handling

• The HF_TOKEN is required to query Gemma for outfit recommendations.
• It is securely saved in st.session_state for each user session.
• It is never stored persistently or sent elsewhere.

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🎯 Future Improvements

• 🔒 Authenticated user sessions with persistent history
• 🛒 Add-to-cart integration for e-commerce use
• 🗣️ Voice-based outfit search (via Whisper or Speech-to-Text APIs)
• 🧵 Tailor chatbot integration for fashion Q&A

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👨‍💻 Author

👤 Mohit Gupta

🎓 mgmohit1111@gmail.com

🔗 LinkedIn | GitHub