Update README.md

README.md

@@ -1,26 +1,37 @@
-# 🏪 Shelf Photo Analyzer
+---
+title: Shelf Photo Analyzer
+emoji: 🏪
+colorFrom: red
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.28.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
 
-## Description
+# 🏪 Shelf Photo Analyzer
 
-An AI-powered tool for merchandisers and retail managers to instantly analyze product displays on store shelves. Simply upload a shelf photo, enter the product name, and get instant AI analysis with actionable recommendations to improve product placement and boost sales.
+Professional AI-powered tool for merchandisers and retail managers to analyze product displays on store shelves. Get instant analysis with actionable recommendations to improve product placement and boost sales.
 
-## Features
+## ✨ Features
 
 - 📸 **Photo Upload** - Support for JPG, PNG, WebP formats up to 10MB
-- 🔍 **Product Search** - AI-powered product detection and analysis
+- 🔍 **Product Detection** - AI-powered product recognition and analysis
 - 🤖 **Smart Analysis** - Uses GPT-4 Vision API for accurate shelf assessment
 - 📊 **Compliance Scoring** - 1-10 score for product placement effectiveness
 - 💡 **Actionable Recommendations** - Specific suggestions with impact estimates
 - 📱 **Mobile Friendly** - Responsive design for field use
 - 📈 **Impact Forecasting** - Estimated sales improvement from changes
 
-## How to Use
+## 🚀 Quick Start
 
-1. **Upload** a clear photo of the store shelf
-2. **Enter** the product name you want to analyze
-3. **Select** analysis depth (Basic/Detailed/Expert)
-4. **Click** "Analyze Photo" and wait 15-30 seconds
-5. **Review** results and follow the recommendations
+1. **Enter** your OpenAI API key
+2. **Upload** a clear photo of the store shelf
+3. **Enter** the product name you want to analyze
+4. **Select** analysis depth (Basic/Detailed/Expert)
+5. **Click** "🚀 Analyze Photo" and wait 15-30 seconds
+6. **Review** results and follow the recommendations
 
 ## Analysis Results
 
@@ -43,123 +54,29 @@ Get prioritized action items with:
 - 📈 **Impact Predictions** - Expected sales/visibility improvements
 - 🔧 **Difficulty Rating** - How easy each recommendation is to implement
 
-## Tech Stack
-
-- **Frontend:** Streamlit
-- **AI:** OpenAI GPT-4 Vision API
-- **Image Processing:** Pillow (PIL)
-- **Backend:** Python 3.9+
-- **Hosting:** Hugging Face Spaces
-
-## Setup Instructions
-
-### For Hugging Face Spaces Deployment
-
-1. **Fork/Clone** this repository
-2. **Create** a new Hugging Face Space
-3. **Select** "Streamlit" as the SDK
-4. **Upload** all files to your Space
-5. **Add** your OpenAI API key to Space secrets:
-   - Go to Settings → Repository secrets
-   - Add: `OPENAI_API_KEY = "your-api-key-here"`
-6. **Deploy** - your app will be live in minutes!
-
-### Local Development
-
-```bash
-# Clone repository
-git clone <repository-url>
-cd shelf-analyzer
-
-# Install dependencies  
-pip install -r requirements.txt
-
-# Set environment variable
-export OPENAI_API_KEY="your-api-key-here"
-
-# Run application
-streamlit run app.py
-```
-
-## Configuration
-
-The app uses the following configuration (in `config.py`):
-
-- **Max Image Size:** 10MB
-- **Supported Formats:** JPG, JPEG, PNG, WebP
-- **AI Model:** GPT-4 Vision Preview
-- **Analysis Timeout:** 60 seconds
-- **Max Retries:** 3 attempts
-
-## File Structure
-
-```
-shelf-analyzer/
-├── app.py                   # Main Streamlit application
-├── requirements.txt         # Python dependencies
-├── config.py               # Configuration settings
-├── README.md               # This file
-├── utils/
-│   ├── __init__.py
-│   ├── ai_analyzer.py      # AI analysis logic
-│   ├── recommendations.py  # Recommendation engine
-│   └── ui_components.py    # UI display components
-```
-
-## Usage Examples
-
-### Typical Use Cases
-
-- **Merchandisers:** Check product compliance during store visits
-- **Category Managers:** Audit shelf execution across locations  
-- **Store Managers:** Optimize product placement for better sales
-- **Retail Auditors:** Document and track display improvements
-
-### Sample Workflow
-
-1. Walk store aisles with smartphone/tablet
-2. Take photos of key product categories
-3. Analyze each product's shelf presence
-4. Follow AI recommendations immediately
-5. Re-analyze to confirm improvements
-
-## API Costs
-
-- **Hugging Face Hosting:** FREE ✨
-- **OpenAI API:** ~$0.01-0.04 per analysis
-- **Monthly Estimate:** $20-100 (depending on usage)
-
-## Limitations
+## 🔑 API Key Required
 
-- Requires good lighting in photos
-- Works best with clear, unobstructed shelf views
-- AI accuracy depends on image quality
-- Currently supports single product analysis per photo
+- **OpenAI**: Get from [OpenAI Platform](https://platform.openai.com/api-keys)
 
-## Future Roadmap
+## 🛡️ Privacy
 
-### Phase 2
-- [ ] Batch analysis of multiple products
-- [ ] PDF report generation
-- [ ] Analysis history dashboard
-- [ ] Popular product presets
+- API keys never stored permanently
+- Images processed temporarily only
+- No data retention
 
-### Phase 3  
-- [ ] Mobile app development
-- [ ] Real-time video analysis
-- [ ] Planogram compliance checking
-- [ ] Team collaboration features
+## 📄 License
 
-## Support
+Licensed under Apache License 2.0
 
-For issues, questions, or feature requests:
-- Create an issue in this repository
-- Contact: [your-contact-info]
+## 👨‍💻 Author
 
-## License
+**Marek Staniszewski**  
+Heuristica  
+🌐 [www.heuristica.pl](http://www.heuristica.pl)  
+📧 staniszewski@heuristica.pl
 
-This project is licensed under the MIT License - see the LICENSE file for details.
+For questions, suggestions, or support, please contact the author.
 
 ---
 
-**Powered by OpenAI GPT-4 Vision API**
+**Shelf Photo Analyzer** - Professional shelf analysis tool 🏪