done

README.md

@@ -7,13 +7,13 @@ PlateMate is a smart and interactive web app that uses state-of-the-art AI techn
 ## Features
 
 1. **Food Image Classification**  
-   Upload an image of any dish, and DPlateMate will identify the food with a high level of confidence. It uses a pretrained image classification model tailored for food.
+   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 doesn't stop at identification—it also suggests healthier alternatives for the classified dish using the power of OpenAI's GPT models.
+   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.
@@ -27,8 +27,8 @@ PlateMate is a smart and interactive web app that uses state-of-the-art AI techn
 
 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. **Hugging Face Inference API**: To generate key ingredients for classified dishes.
-4. **OpenAI GPT-4**: To provide healthier recipe alternatives and insights.
+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.
 
 ---
@@ -80,10 +80,10 @@ Users can upload a food image or select a sample. The app displays the image in
 Using the Hugging Face image classification pipeline, the app identifies the food item in the image.
 
 ### **3. Ingredient Generation**
-The Hugging Face Qwen NLP model suggests the main ingredients for the identified dish.
+The Hugging Face NLP model suggests the main ingredients for the identified dish.
 
-### **4. Healthier Alternatives**
-OpenAI GPT-4 generates a healthy recipe alternative for the dish, explaining why it is a better choice.
+### **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.
 
 ---
 
@@ -105,7 +105,7 @@ OpenAI GPT-4 generates a healthy recipe alternative for the dish, explaining why
 |     Backend Processing                                    |
 |      - Image Classification (Hugging Face Transformers)  |
 |      - Ingredients (Hugging Face Inference API)          |
-|      - Healthy Recipes (OpenAI GPT-4)                    |
+|      - Healthy Recipes (GPT-4 RAG)                       |
 |                                                           |
 +-----------------------------------------------------------+
 |                                                           |
@@ -120,17 +120,11 @@ OpenAI GPT-4 generates a healthy recipe alternative for the dish, explaining why
 
 ## 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. It's lower in carbs and calories while being equally delicious!"
+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!"
 
 ---
 
@@ -142,16 +136,6 @@ OpenAI GPT-4 generates a healthy recipe alternative for the dish, explaining why
 
 ---
 
-## Screenshots
-
-**1. Homepage**
-![Homepage](docs/homepage.png)
-
-**2. Classification Result**
-![Classification Result](docs/classification_result.png)
-
----
-
 ## Contributing
 
 We welcome contributions! To contribute:
@@ -173,16 +157,19 @@ 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**! 🎉
+
 ---
-title: CTP Project
-emoji: ⚡
+
+## Hugging Face Configuration
+
+```yaml
+title: PlateMate
+emoji: 🍽️
 colorFrom: purple
 colorTo: green
 sdk: streamlit
 sdk_version: 1.40.1
 app_file: app.py
 pinned: false
-short_description: Food image recognition models that recognizes food.
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+short_description: Food classification with GPT-4-powered healthier alternatives.
+```