This project features a Vegetable Classification App, built using a state-of-the-art Convolutional Neural Network (CNN). The app allows users to upload images of vegetables and receive accurate classifications, along with confidence scores for each category. Check out the App ---> Vegetable Classification App

Upload vegetable images (JPG, PNG, or JPEG formats).

Utilizes a CNN trained on 15 vegetable classes for high accuracy.

Visualizes classification probabilities with a bar chart.

Easy-to-use interface accessible via a web browser.

Vegetable-Classification-App/

├── streamlit_app.py

├── requirements.txt
├── model/

│ └── Vegetable_model_last.h5
├── README.md

└── assets/

Users upload a vegetable image in JPG/PNG format.

The app uses the pre-trained CNN to classify the vegetable.

Predicted vegetable name and Confidence scores for all 15 classes, displayed as a bar chart.

Bean, Bitter_Gourd, Bottle_Gourd, Brinjal, Broccoli, Cabbage, Capsicum, Carrot, Cauliflower, Cucumber, Papaya, Potato, Pumpkin, Radish, Tomato

Using Streamlit Cloud The app is deployed via Streamlit Cloud for easy access. Check it out here: Vegetable Classification App

TensorFlow/Keras

Convolutional Neural Network (CNN)

15 vegetable types

High-resolution vegetable images

Softmax for multi-class classification

Contributions are welcome! If you’d like to improve the model, app interface, or documentation:

1. Fork the repository.
2. Create a feature branch.
3. Submit a pull request.

This project is licensed under the MIT License. Feel free to use and modify the code as needed.

Amr Ahmed, Mohamed Yasser, Omar Khaled, Ibrahim Mahmoud.

TensorFlow, Keras, Streamlit.

Open-source communities for making this possible.

If you have any questions or suggestions, feel free to reach out: