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https://github.com/bektesfish/cnn-image-classification-web-application
This web application classifies potato leaf health by detecting Potato Early Blight, Potato Late Blight, and healthy leaves based on uploaded images. It uses React for the frontend, FastAPI for the backend, and TensorFlow Serving for real-time predictions.
https://github.com/bektesfish/cnn-image-classification-web-application
cnn-classification fastapi react tensorflowserving
Last synced: about 2 months ago
JSON representation
This web application classifies potato leaf health by detecting Potato Early Blight, Potato Late Blight, and healthy leaves based on uploaded images. It uses React for the frontend, FastAPI for the backend, and TensorFlow Serving for real-time predictions.
- Host: GitHub
- URL: https://github.com/bektesfish/cnn-image-classification-web-application
- Owner: BEKtesfish
- License: mit
- Created: 2024-11-08T14:50:08.000Z (about 2 months ago)
- Default Branch: master
- Last Pushed: 2024-11-08T16:03:45.000Z (about 2 months ago)
- Last Synced: 2024-11-08T16:30:28.746Z (about 2 months ago)
- Topics: cnn-classification, fastapi, react, tensorflowserving
- Language: Jupyter Notebook
- Homepage:
- Size: 47 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# CNN Image Classification Web Application
This project is a web application that classifies potato leaf health based on uploaded images. It can detect *Potato Early Blight*, *Potato Late Blight*, and *Healthy Potato Leaves*, providing a confidence score for each prediction.
## Project Overview
- **Frontend**: Built with React, featuring a user-friendly interface for image upload via drag-and-drop or file selection.
- **Backend**: FastAPI serves as the API layer, facilitating communication with the TensorFlow Serving model for real-time predictions.
- **Model Deployment**: A pre-trained CNN model, hosted on TensorFlow Serving, processes input images and returns classification results.
## Dataset
The model is trained on the [PlantVillage Dataset](https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset), which includes labeled images of potato leaves with various conditions, helping to differentiate diseased and healthy samples.
p.s you can also find the Dataset here as a ZIP file: C:\\potato_classifier\training\PlantVillage.zip
## Application Workflow
1. **Model Training**: The CNN model was trained using the PlantVillage dataset and saved for deployment.
2. **Model Deployment**: The trained model is deployed on TensorFlow Serving, accessed through FastAPI to serve predictions to the frontend.
3. **Web Interface**: Users upload an image of a potato leaf, and the app displays the classification and confidence score.
## Getting Started
To get a local copy of the project running, follow these steps:
### Prerequisites
1. **Clone the Repository**:
```bash
git clone https://github.com/BEKtesfish/CNN-Image-Classification-Web-Application.git
CNN-Image-Classification-Web-Application
```
2. **Create and Activate a Virtual Environment**:
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install Backend Dependencies**:
```bash
pip install -r requirements.txt
```
### Running the Application
1. **Setup TensorFlow Serving**:
To serve the model with TensorFlow Serving, use Docker:
```bash
docker run -t --rm -p 8501:8501 -v C:\potato_classifier\models:/models tensorflow/serving --rest_api_port=8501 --model_config_file=/potato_classifier/models.config
```
For more details, see the [TensorFlow Serving Documentation](https://www.tensorflow.org/tfx/serving/docker).
2. **Run the FastAPI Backend**:
Inside the api directory, start the backend server:
```bash
cd api
python main-tf-serving.py
```
3. **Run the Frontend**:
Navigate to the frontend directory, install dependencies, and start the React app:
```bash
cd ../front
npm install
npm start
```
4. **Access the Application**: Open your browser and go to http://localhost:3000.
Here, you’ll find a drag-and-drop section where you can upload an image of a
potato leaf to receive predictions and a confidence score
### Additional Resources
Model Training Notebook: In the training folder,contains the Jupyter notebook used for training the CNN model. and
the 1st version of the trained model can be found in the models folder