https://github.com/singhxtushar/floravision

In this Project we classify different categories of Flowers using a Pre-Trained model and also using the functionality of Streamlit Application to build an API or frontend for the Project along with the various libraries of python for proper functioning of Model.
https://github.com/singhxtushar/floravision

classification flower-classification pathlib pillow pre-trained-model streamlit tensorflow

Last synced: 3 months ago
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In this Project we classify different categories of Flowers using a Pre-Trained model and also using the functionality of Streamlit Application to build an API or frontend for the Project along with the various libraries of python for proper functioning of Model.

• Host: GitHub
• URL: https://github.com/singhxtushar/floravision
• Owner: SINGHxTUSHAR
• License: mit
• Created: 2024-05-08T15:06:19.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-08T17:17:01.000Z (over 1 year ago)
• Last Synced: 2025-05-19T21:44:46.188Z (5 months ago)
• Topics: classification, flower-classification, pathlib, pillow, pre-trained-model, streamlit, tensorflow
• Language: Python
• Homepage: https://floravision-k8l1.onrender.com/
• Size: 41.9 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# FloraVision 🌷:

Flower-Image-Classification-Streamlit-TensorFlow

![Designer (1)](https://github.com/SINGHxTUSHAR/FloraVision/assets/113624520/9cd300a2-cb1b-4f63-bf3c-8d72428bd6e4)

A basic web-app for image classification using Streamlit and TensorFlow.

It classifies the given image of a flower into one of the following five categories :-  

1. Daisy

2. Dandelion

3. Rose

4. Sunflower

5. Tulip

### `Notes:`

* A simple flower classification model was trained using TensorFlow.  

* The weights are stored as `flower_model_trained.hdf5`.  

* The code to train the modify and train the model can be found in `model.py`.  

* The web-app created using Streamlit can be found in `app.py`

## Commands ✍️:

To run the app locally, use the following command :-  

`streamlit run app.py`  

The webpage should open in the browser automatically.  

If it doesn't, the local URL would be output in the terminal, just copy it and open it in the browser manually.  

By default, it would be `http://localhost:8501/`  

Click on `Browse files` and choose an image from your computer to upload.  

Once uploaded, the model will perform inference and the output will be displayed.  

## Output 📁:

For more output images visit:  Link 

![out_1](https://github.com/SINGHxTUSHAR/FloraVision/assets/113624520/26275ee5-63f1-4c22-bdc9-0f77b4ccd368)

## Reference 🧧:

* Image classification  Documentation 

* Streamlit  Documentation 

## Requirements💻 :

Ensure you have the following dependencies installed:

- Python (version 3.9.x || 3.12.x)

- IDE: VS-CODE or collab

- Virtual-environment(venv)

- Other dependencies (refer to the requirements.txt)

You can install the required Python packages using:

```bash

pip install -r requirements.txt

```

## Setup 💿:

- Clone the repository:

```bash

git clone https://github.com/SINGHxTUSHAR/FloraVision.git

cd FloraVision

```

- Create a virtual environment (optional but recommended):

```bash

python -m venv venv

```

- Activate the virtual environment:

  - On Windows:

   ```bash

   venv\Scripts\activate

   ```

  - On macOS/Linux:

  ```bash

  source venv/bin/activate

  ```

## Contributing 📌:

If you'd like to contribute to this project, please follow the standard GitHub fork and pull request process. Contributions, issues, and feature requests are welcome!

## Suggestion 🚀: 

If you have any suggestions for me related to this project, feel free to contact me at tusharsinghrawat.delhi@gmail.com or LinkedIn.

## License 📝:

This project is licensed under the MIT License - see the LICENSE file for details.