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| 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/` |