🚀 Initial upload of my app

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo/demo.mp4 filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 Eslam Tarek
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,12 +1,97 @@
----
-title: LeafNet
-emoji: 📉
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 5.47.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# LeafNet - Healyth vs unhealthy tea classifier
+*A deep learning tool to classify tea leaves as healthy or unhealthy from images.*
+
+![MIT License](https://img.shields.io/badge/license-MIT-green)
+
+---
+
+## Table of Contents
+
+- [Demo](#demo)
+- [Features](#features)
+- [Installation / Setup](#installation--setup)
+- [Usage](#usage)
+- [Configuration / Options](#configuration--options)
+- [Contributing](#contributing)
+- [License](#license)
+- [Acknowledgements / Credits](#acknowledgements--credits)
+
+---
+
+## Demo
+
+![Demo Screenshot](./demo/demo.png)
+*Main interface for uploading and classifying tea leaf images.*
+
+![Demo Video](./demo/demo.mp4)
+*Video walkthrough of the classification workflow.*
+
+---
+
+## Features
+
+- Classifies tea leaf images as healthy or unhealthy using deep learning.
+- Simple, interactive web-based UI for image upload and prediction.
+- Modular codebase for easy extension and retraining.
+- Fast inference for both single and batch image processing.
+
+---
+
+## Installation / Setup
+
+```bash
+# Create a virtual environment
+python -m venv .venv
+
+# Activate it
+# On Linux/Mac:
+source .venv/bin/activate
+# On Windows:
+.venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+---
+
+## Usage
+
+Run the application:
+
+```bash
+python app.py
+```
+
+This will launch the web interface in your browser.  
+Upload an image of a tea leaf to get a health classification.
+
+---
+
+## Configuration / Options
+
+- UI and model configuration can be adjusted in the source files.
+- For advanced settings (e.g., model path, thresholds), edit the relevant Python files.
+
+---
+
+## Contributing
+
+Contributions are welcome!
+- Open issues for bugs or feature requests.
+- Submit pull requests for improvements.
+- Please follow standard Python code style and include tests where possible.
+
+---
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.
+
+---
+
+## Acknowledgements / Credits
+
+- Developed by Eslam Tarek.
+- Thanks to the open-source community for libraries and inspiration.
+

app.py

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+from ui import build_ui
+
+if __name__ == "__main__":
+    demo = build_ui()
+    demo.launch()

demo/demo.mp4

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+size 1371590

models/tea_leaves_model.pth

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+size 94425194

requirements.txt

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+gradio==4.25.0
+torch==2.2.2
+pillow==10.3.0
+# Add any other dependencies found in your ui.py or model code

ui.py

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+import gradio as gr
+from utils import predict_image
+
+def build_ui():
+    with gr.Blocks() as demo:
+        gr.Markdown("# 🍃 Tea Leaf Disease Classifier")
+        gr.Markdown("Upload a tea leaf image and get the predicted disease class.")
+
+        with gr.Row():
+            image_input = gr.Image(type="pil", label="Upload Leaf Image")
+            output_text = gr.Textbox(label="Prediction Result")
+
+        gr.Button("Predict").click(
+            fn=predict_image,
+            inputs=image_input,
+            outputs=output_text
+        )
+
+    return demo

utils.py

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+import torch
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image
+
+# Load class names
+class_names = [
+    "Anthracnose", "algal leaf", "bird eye spot",
+    "brown blight", "gray light", "healthy",
+    "red leaf spot", "white spot"
+]
+
+# Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model (assumes you used torch.save(model, "model.pth"))
+model = torch.load("models/tea_leaves_model.pth", map_location=device, weights_only=False)
+model.eval()
+
+# Image preprocessing
+transform  = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+def predict_image(image: Image.Image) -> str:
+    """
+    Run inference on a single PIL image and return class label.
+    """
+    img_t = transform(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        outputs = model(img_t)
+        _, preds = torch.max(outputs, 1)
+        pred_class = class_names[preds.item()]
+
+    return pred_class