done

README.md

@@ -1,175 +1,13 @@
-# PlateMate - Your Culinary Assistant 🍽️
-
-PlateMate is a smart and interactive web app that uses state-of-the-art AI technologies to classify food images, provide key ingredients for your favorite dishes, and suggest healthier alternatives to enjoy guilt-free meals. Whether you're a home chef looking for inspiration or a health enthusiast, PlateMate has something for everyone.
-
----
-
-## Features
-
-1. **Food Image Classification**  
-   Upload an image of any dish, and PlateMate will identify the food with a high level of confidence using a pretrained image classification model tailored for food.
-
-2. **Ingredient Suggestion**  
-   Once classified, PlateMate provides a concise, AI-generated list of main ingredients for the dish, helping you understand what goes into your favorite foods.
-
-3. **Healthier Alternatives**  
-   PlateMate goes beyond basic suggestions with **GPT-4-powered Retrieval-Augmented Generation (RAG)** to provide personalized, healthier alternatives for your favorite dishes. This cutting-edge approach ensures the recommendations are both relevant and grounded in accurate information.
-
-4. **Sample Images**  
-   Choose from predefined food images to try the app's features instantly.
-
-5. **Interactive Sidebar**  
-   Learn more about the AI models powering PlateMate and their purpose in making your culinary journey exciting and informative.
-
----
-
-## Technologies Used
-
-1. **Streamlit**: For a responsive and user-friendly web interface.
-2. **Hugging Face Transformers**: To classify food images using a custom pretrained model (`Shresthadev403/food-image-classification`).
-3. **GPT-4 as a RAG System**: Combines retrieval-based data with generative capabilities to suggest healthier alternatives based on AI-generated insights.
-4. **Hugging Face Inference API**: To generate key ingredients for classified dishes.
-5. **Python Libraries**: PIL for image handling, os for file operations.
-
----
-
-## Installation Guide
-
-### Prerequisites
-- Python 3.8 or later
-- Pip
-- Streamlit
-
-### Steps
-1. Clone the repository:
-   ```bash
-   git clone https://huggingface.co/spaces/LuckyHappyFish/CTP_Project
-   cd CTP_Project
-   ```
-
-2. Install dependencies:
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Add your API keys:
-   - Create a `.streamlit/secrets.toml` file in the project directory:
-     ```toml
-     [HF_API_KEY]
-     value = "your_huggingface_api_key"
-
-     [openai]
-     value = "your_openai_api_key"
-     ```
-
-4. Run the app:
-   ```bash
-   streamlit run app.py
-   ```
-
-5. Open the app in your browser at `http://localhost:8501`.
-
----
-
-## How It Works
-
-### **1. Upload Image**
-Users can upload a food image or select a sample. The app displays the image in the interface.
-
-### **2. Image Classification**
-Using the Hugging Face image classification pipeline, the app identifies the food item in the image.
-
-### **3. Ingredient Generation**
-The Hugging Face NLP model suggests the main ingredients for the identified dish.
-
-### **4. Healthier Alternatives with GPT-4 RAG**
-GPT-4, integrated as a RAG system, retrieves relevant nutritional data and combines it with generative capabilities to suggest healthier, personalized recipe alternatives. This ensures scientifically accurate and context-aware recommendations.
-
----
-
-## Architecture Diagram
-
-```plaintext
-+-----------------------------------------------------------+
-|                                                           |
-|                    PlateMate Architecture                 |
-|                                                           |
-+-----------------------------------------------------------+
-|                                                           |
-|  User Interface (Streamlit)                              |
-|      - Upload Image                                       |
-|      - Display Results                                    |
-|                                                           |
-+-----------------------------------------------------------+
-|                                                           |
-|     Backend Processing                                    |
-|      - Image Classification (Hugging Face Transformers)  |
-|      - Ingredients (Hugging Face Inference API)          |
-|      - Healthy Recipes (GPT-4 RAG)                       |
-|                                                           |
-+-----------------------------------------------------------+
-|                                                           |
-|  External Services                                        |
-|      - Hugging Face API                                   |
-|      - OpenAI API                                         |
-|                                                           |
-+-----------------------------------------------------------+
-```
-
----
-
-## Example Use Case
-
-1. **Upload an Image**: A user uploads an image of a pizza.
-2. **Classification Output**: Food: Pizza (97.65% confidence)
-3. **Ingredients**: Generated: Flour, cheese, tomato sauce, olive oil, basil.
-4. **Healthier Alternative**:  
-   "Try a cauliflower crust pizza with reduced-fat cheese and fresh vegetables. GPT-4 RAG ensures that this option is both lower in calories and higher in nutritional value, offering the best balance between health and flavor!"
-
----
-
-## Future Enhancements
-
-- **Multi-dish Recognition**: Support for identifying multiple dishes in a single image.
-- **Nutritional Analysis**: Detailed breakdown of macronutrients and calories.
-- **Meal Planning**: Suggesting weekly meal plans based on user preferences.
-
----
-
-## Contributing
-
-We welcome contributions! To contribute:
-1. Fork the repository.
-2. Create a feature branch.
-3. Submit a pull request with a detailed description of your changes.
-
----
-
-## License
-
-This project is licensed under the MIT License.
-
 ---
-
-## Contact
-
-Developed by Muhammad Hassan Butt.  
-Feel free to reach out via [GitHub](https://github.com/yourusername) or [LinkedIn](https://linkedin.com/in/yourprofile).  
-
-Happy cooking with **PlateMate**! 🎉
-
----
-
-## Hugging Face Configuration
-
-```yaml
-title: PlateMate
-emoji: 🍽️
+title: CTP Project
+emoji: ⚡
 colorFrom: purple
 colorTo: green
 sdk: streamlit
 sdk_version: 1.40.1
 app_file: app.py
 pinned: false
-short_description: Food classification with GPT-4-powered healthier alternatives.
-```
+short_description: Food image recognition models that recognizes food.
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference