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https://github.com/ankushrathour/fruitnutritiondetector
FruitNutritionDetector: FastAPI-based API for Fruit Detection and Nutritional Information Retrieval using ImageAI and USDA API. Detect fruits from images and fetch detailed nutritional data.
https://github.com/ankushrathour/fruitnutritiondetector
fastapi fruit-recognition imageai nutrition-information python3 yolo
Last synced: 7 days ago
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FruitNutritionDetector: FastAPI-based API for Fruit Detection and Nutritional Information Retrieval using ImageAI and USDA API. Detect fruits from images and fetch detailed nutritional data.
- Host: GitHub
- URL: https://github.com/ankushrathour/fruitnutritiondetector
- Owner: AnkushRathour
- License: gpl-3.0
- Created: 2024-08-10T08:40:52.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-08-10T08:59:53.000Z (3 months ago)
- Last Synced: 2024-08-10T09:49:54.333Z (3 months ago)
- Topics: fastapi, fruit-recognition, imageai, nutrition-information, python3, yolo
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# FruitNutritionDetector
**FruitNutritionDetector** is a powerful **FastAPI-based API** designed to detect fruits from images and retrieve their nutritional information using **ImageAI** and the **USDA API**. This API utilizes advanced models like Xception for image classification and fetches nutritional data from the USDA FoodData Central database.
## Key Features
- **Fruit Detection API:** Detect fruits from images with high accuracy using the ImageAI framework.
- **Nutritional Information Retrieval:** Fetch detailed nutritional data from the USDA API for the detected fruit.
- **FastAPI Framework:** Built with FastAPI, providing high performance and easy deployment.
- **YOLO Integration:** Potential for advanced object detection with YOLO models.
## Getting Started
### Installation
Follow these steps to set up the **FruitNutritionDetector** API:
1. **Clone the repository:**
```bash
git clone https://github.com/your-username/FruitNutritionDetector.git
cd FruitNutritionDetector
2. **Install dependencies:**
Make sure you have Python 3.7+ installed, then install the required packages:
```bash
pip install fastapi requests pyngrok uvicorn
pip install cython 'pillow>=7.0.0' 'numpy>=1.18.1' 'opencv-python>=4.1.2' 'torch>=1.9.0' --extra-index-url 'https://download.pytorch.org/whl/cpu' 'torchvision>=0.10.0' --extra-index-url 'https://download.pytorch.org/whl/cpu' 'pytest==7.1.3' 'tqdm==4.64.1' 'scipy>=1.7.3' 'matplotlib>=3.4.3' 'mock==4.0.3'
3. **Download pre-trained models:**
```bash
wget https://github.com/OlafenwaMoses/ImageAI/releases/download/3.0.0-pretrained/resnet50-19c8e357.pth/ -O resnet50-19c8e357.pth
wget https://github.com/vishalbharti1990/deep_feature_clustering/blob/master/Documents/.keras/models/xception_weights_tf_dim_ordering_tf_kernels.h5?raw=true -O xception_weights_tf_dim_ordering_tf_kernels.h5
4. **Set up USDA API Key:**
Replace {USDA_API_KEY} in the code with your actual USDA API key.
5. **Launch the API:**
Start the server with Uvicorn:
```bash
uvicorn main:app --host 0.0.0.0 --port 5000
The API will be accessible at http://localhost:5000.
6. **API Usage**
Endpoint: POST /fruit-detection
This endpoint detects a fruit from an uploaded image and retrieves its nutritional information.
Request Parameters:
- file: The image file containing the fruit (Supported formats: .jpg, .png, .jpeg).
```bash
curl -X POST "http://localhost:5000/fruit-detection" \
-H "accept: application/json" \
-H "Content-Type: multipart/form-data" \
-F "file=@/path/to/fruit_image.jpg"
Response:
- detected_fruit: The name of the detected fruit.
- probability: The probability score of the detection.
- nutrition_info: A dictionary containing the nutritional information retrieved from the USDA API.
```bash
{
"detected_fruit": "Apple",
"probability": 97.5,
"nutrition_info": {
"name": "Apple",
"nutrients": {
"Energy": 52,
"Protein": 0.26,
"Total lipid (fat)": 0.17,
"Carbohydrate, by difference": 13.81
}
}
}
## Contributing
We welcome contributions to FruitNutritionDetector! Feel free to fork the repository, create a branch, and submit a Pull Request with your improvements or new features.