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- # LeafNet - Healyth vs unhealthy tea classifier
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- *A deep learning tool to classify tea leaves as healthy or unhealthy from images.*
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-
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- ![MIT License](https://img.shields.io/badge/license-MIT-green)
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-
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- ---
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-
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- ## Table of Contents
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-
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- - [Demo](#demo)
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- - [Features](#features)
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- - [Installation / Setup](#installation--setup)
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- - [Usage](#usage)
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- - [Configuration / Options](#configuration--options)
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- - [Contributing](#contributing)
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- - [License](#license)
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- - [Acknowledgements / Credits](#acknowledgements--credits)
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-
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- ---
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-
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- ## Demo
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-
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- ![Demo Screenshot](./demo/demo.png)
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- *Main interface for uploading and classifying tea leaf images.*
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-
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- ![Demo Video](./demo/demo.mp4)
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- *Video walkthrough of the classification workflow.*
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-
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- ---
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-
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- ## Features
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-
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- - Classifies tea leaf images as healthy or unhealthy using deep learning.
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- - Simple, interactive web-based UI for image upload and prediction.
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- - Modular codebase for easy extension and retraining.
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- - Fast inference for both single and batch image processing.
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-
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- ---
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-
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- ## Installation / Setup
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-
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- ```bash
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- # Create a virtual environment
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- python -m venv .venv
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-
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- # Activate it
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- # On Linux/Mac:
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- source .venv/bin/activate
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- # On Windows:
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- .venv\Scripts\activate
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-
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- # Install dependencies
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- pip install -r requirements.txt
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- ```
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-
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- ---
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-
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- ## Usage
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-
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- Run the application:
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-
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- ```bash
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- python app.py
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- ```
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-
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- This will launch the web interface in your browser.
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- Upload an image of a tea leaf to get a health classification.
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-
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- ---
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-
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- ## Configuration / Options
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-
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- - UI and model configuration can be adjusted in the source files.
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- - For advanced settings (e.g., model path, thresholds), edit the relevant Python files.
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-
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- ---
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-
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- ## Contributing
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-
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- Contributions are welcome!
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- - Open issues for bugs or feature requests.
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- - Submit pull requests for improvements.
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- - Please follow standard Python code style and include tests where possible.
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-
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- ---
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-
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- ## License
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-
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- This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.
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-
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- ---
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-
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- ## Acknowledgements / Credits
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-
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- - Developed by Eslam Tarek.
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- - Thanks to the open-source community for libraries and inspiration.
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-
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ title: PlateVision – YOLO-based License Plate Detection
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+ emoji: 🚗
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+ colorFrom: yellow
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+ colorTo: blue
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+ sdk: streamlit
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+ sdk_version: "1.40.0" # latest stable streamlit
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+ app_file: app.py
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+ pinned: false
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+ license: mit
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+ ---
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+
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+ *A deep learning tool to classify tea leaves as healthy or unhealthy from images.*
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+
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+ ![MIT License](https://img.shields.io/badge/license-MIT-green)
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+
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+ ---
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+
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+ ## Table of Contents
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+
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+ - [Demo](#demo)
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+ - [Features](#features)
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+ - [Installation / Setup](#installation--setup)
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+ - [Usage](#usage)
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+ - [Configuration / Options](#configuration--options)
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+ - [Contributing](#contributing)
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+ - [License](#license)
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+ - [Acknowledgements / Credits](#acknowledgements--credits)
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+
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+ ---
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+
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+ ## Demo
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+
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+ ![Demo Screenshot](./demo/demo.png)
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+ *Main interface for uploading and classifying tea leaf images.*
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+
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+ ![Demo Video](./demo/demo.mp4)
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+ *Video walkthrough of the classification workflow.*
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+
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+ ---
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+
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+ ## Features
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+
44
+ - Classifies tea leaf images as healthy or unhealthy using deep learning.
45
+ - Simple, interactive web-based UI for image upload and prediction.
46
+ - Modular codebase for easy extension and retraining.
47
+ - Fast inference for both single and batch image processing.
48
+
49
+ ---
50
+
51
+ ## Installation / Setup
52
+
53
+ ```bash
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+ # Create a virtual environment
55
+ python -m venv .venv
56
+
57
+ # Activate it
58
+ # On Linux/Mac:
59
+ source .venv/bin/activate
60
+ # On Windows:
61
+ .venv\Scripts\activate
62
+
63
+ # Install dependencies
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+ pip install -r requirements.txt
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+ ```
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+
67
+ ---
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+
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+ ## Usage
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+
71
+ Run the application:
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+
73
+ ```bash
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+ python app.py
75
+ ```
76
+
77
+ This will launch the web interface in your browser.
78
+ Upload an image of a tea leaf to get a health classification.
79
+
80
+ ---
81
+
82
+ ## Configuration / Options
83
+
84
+ - UI and model configuration can be adjusted in the source files.
85
+ - For advanced settings (e.g., model path, thresholds), edit the relevant Python files.
86
+
87
+ ---
88
+
89
+ ## Contributing
90
+
91
+ Contributions are welcome!
92
+ - Open issues for bugs or feature requests.
93
+ - Submit pull requests for improvements.
94
+ - Please follow standard Python code style and include tests where possible.
95
+
96
+ ---
97
+
98
+ ## License
99
+
100
+ This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.
101
+
102
+ ---
103
+
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+ ## Acknowledgements / Credits
105
+
106
+ - Developed by Eslam Tarek.
107
+ - Thanks to the open-source community for libraries and inspiration.
108
+