---
title: RetailGenie
emoji: 🛍️
colorFrom: yellow
colorTo: gray
sdk: gradio
sdk_version: 5.38.2
app_file: app.py
pinned: false
short_description: AI Shopping Assistant, Locator & Smart Suggestions
license: mit
---

# 🛍️ RetailGenie – In-Store Smart Assistant

**RetailGenie** is an AI-powered shopping assistant that helps users **locate in-store products** using dropdown filters and get **intelligent product suggestions** based on natural language queries. Built with Gradio and sample retail data, this project simulates a real-time in-store assistant combining **rule-based filtering** with **FLAN-T5-based AI recommendations**.

> ⚠️ This project is for **educational and demonstration purposes only**. Product data is mock/simulated and may not reflect real-time inventory.

---

## 🌐 Try Live on Hugging Face

RetailGenie is live and accessible via Hugging Face Spaces:

👉 [**Launch RetailGenie on Hugging Face Spaces**](https://huggingface.co/spaces/raviix46/RetailGenie)

No setup needed – just visit and start exploring!

---

## ✨ Key Features

### 🧭 Navigator Tab
- Multi-level dropdown filters:
  - Country → State → City → Store → Category → Product → Brand
- Displays:
  - ✅ Availability
  - 💰 Price
  - 🏬 Floor, 🪑 Aisle
  - 🎁 Offers
- Data is dynamically loaded from nested `.csv` files

### 🧠 Smart Suggestions Tab
- Accepts natural language queries like:
  - `"gift under 500"`, `"shampoo for dry hair"`
- Filters products based on:
  - Price limits
  - Tags (dry, oily, gift, budget, etc.)
  - Stock status
- Response is generated using:
  - `google/flan-t5-small` via Hugging Face Transformers
- Fallback to hardcoded suggestions if no match is found

---

## 🧠 System Architecture

```text
User Input (Dropdown / Text)
    │
    ├── Navigator Tab     → File system path chaining → Data lookup → Result
    └── Smart Suggestions → Rule filters + model call → Generated response

---

## 🛠️ Tech Stack

| Category            | Tool / Library                       | Purpose                                      |
|---------------------|--------------------------------------|----------------------------------------------|
| **Programming Language** | Python 3.10                         | Core backend logic and data handling         |
| **Frontend Framework**  | Gradio                              | Web UI with tabs, dropdowns, and text input  |
| **NLP Model**           | google/flan-t5-small (via 🤗 Transformers) | Natural language product recommendations     |
| **Data Handling**       | Pandas                              | Reading, filtering, and managing CSV data    |
| **Deployment**          | Hugging Face Spaces                 | Hosting and public access to the app         |
| **UI Styling**          | HTML, CSS (via Gradio Markdown)     | Custom styling for layout and response boxes |
| **Fallback Logic**      | Python conditionals + emoji formatting | Default suggestions when model fails       |

---

## 📈 Example Queries and Outputs

### 🔹 Smart Suggestions Tab (Natural Language)

| 💬 User Query                            | 🤖 AI Response (Sample)                                                        |
|------------------------------------------|--------------------------------------------------------------------------------|
| `shampoo for dry hair under 300`         | "You may try Dove Dryness Repair – ₹280, available at Floor 1, Aisle 3."      |
| `gift for brother under 500`             | "A perfect gift is our Men’s Grooming Kit – ₹450, available at Floor 2, Aisle 5." |
| `budget skincare for oily skin`          | "Try Clean & Clear Oil Control Face Wash – ₹150, available at Floor 1, Aisle 2." |
| `face cream above 700`                   | "You might like Olay Regenerist – ₹899, found at Floor 3, Aisle 7."            |

### 🔹 Navigator Tab (Dropdowns)

Sample user path:

```
Country → India  
→ State → Karnataka  
→ City → Bangalore  
→ Store → StoreA  
→ Category → Shampoo  
→ Product → Dove  
→ Brand → Dryness Repair  
→ Quantity → 500ml
```

**Output:**

```
✅ In Stock: Yes  
💰 Price: ₹280  
🏬 Floor: 1  
🪑 Aisle: 3  
🎁 Offer: Buy 1 Get 1 Free
```

---

## 🧪 Run Locally

Follow the steps below to set up and run RetailGenie on your machine:

### 1. Clone the Repository

```bash
git clone https://github.com/your_username/RetailGenie.git
cd RetailGenie
```

### 2. Install Dependencies

```bash
pip install -r requirements.txt
```

### 3. Launch the App

```bash
python app.py
```

The app will be available at `http://localhost:7860/`