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🍽️ AI Nutrition Vision — Food Recognition & Nutrition Analyzer

Powered by OpenAI GPT-4o Vision + Streamlit

ai-nutrition-vision is an AI-powered food image analysis app that identifies food items and generates structured nutritional information using OpenAI Vision (GPT-4o).

Upload a food image → AI identifies the food → returns a description or a detailed JSON nutrition breakdown.

🚀 Features 🔍 Food Recognition

Identifies the food shown in the image

Provides a clean, human-readable description

Useful for calorie-tracking & diet apps

🧮 Nutrition Analysis (JSON Output)

AI returns structured data:

{ "food_name": "", "serving_description": "", "calories": "", "fat_grams": "", "protein_grams": "", "confidence_level": "" }

Perfect for:

Fitness apps

Meal trackers

Diet automation tools

🖼️ Vision AI Powered

Uses GPT-4o Vision:

Reads image content

Understands food types

Produces contextual nutrition insights

🎨 Beautiful Streamlit Interface

Drag & drop food image upload

Toggle between description vs. nutrition JSON

Clean results section

📁 Project Structure ai-nutrition-vision/ ├── app.py ├── requirements.txt ├── .env.example ├── .gitignore └── README.md

🔧 Setup Instructions 1️⃣ Clone the Repository git clone https://github.com/Shehjad2019/ai-nutrition-vision.git cd ai-nutrition-vision

2️⃣ Create a Virtual Environment python -m venv venv source venv/bin/activate # macOS/Linux venv\Scripts\activate # Windows

3️⃣ Install Dependencies pip install -r requirements.txt

4️⃣ Add Your API Key

Copy:

cp .env.example .env

Then fill in .env:

OPENAI_API_KEY=your_openai_api_key_here

▶️ Run the App streamlit run app.py

Upload a food image → choose mode → click Analyze Food.

🧠 How It Works 1️⃣ Upload an image

User uploads jpg/png → Streamlit displays it.

2️⃣ Image → Base64

The app converts the image to Base64 for OpenAI Vision.

3️⃣ AI Vision Processing

The model receives:

Image

Text prompt And returns:

Either a human-readable description

Or a structured JSON nutrition output

4️⃣ Display Results

AI output is displayed inside Streamlit.

🔑 Environment Variables OPENAI_API_KEY=your_openai_api_key_here

👤 Author

Shehjad Patel GitHub: https://github.com/Shehjad2019

⭐ Support

If this project helped you, please ⭐ star the repo on GitHub!

👉 https://github.com/Shehjad2019/ai-nutrition-vision

About

AI Nutrition Vision analyzes food images using OpenAI Vision to detect food items and produce detailed nutrition insights (calories, protein, fat, serving size, etc.) with clean Streamlit UI.

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