Add application file

README.md

@@ -1,129 +1,14 @@
-# DERMATO STUDIO PLATFORM
-
-<div align="center">
-
-[![License](http://img.shields.io/:license-MIT-green.svg?style=flat)](https://github.com/mrcryptsie)
-[![Portfolio](https://img.shields.io/:blog-mrcryptsie.com-blue.svg?style=flat&logo=wordpress)](https://mrcryptsie.github.io/portfolio/)
-[![Twitter](https://img.shields.io/:follow-@mrcryptsie-blue.svg?style=flat&logo=x)](https://x.com/titofoundation)
-[![GitHub Sponsors](https://img.shields.io/github/sponsors/serengil?logo=GitHub&color=lightgray)](https://github.com/sponsors/mrcryptsie)
-
-</div>
-
-<p align="center"><img src="logo.png" width="200" height="240" alt="Dermato Studio Logo"></p>
-
-### DERMATO STUDIO - COMING SOON
-
----
-
-## Project Overview
-
-**DERMATO STUDIO** is an innovative web application designed for the early detection of skin diseases through the analysis of dermatoscopic images. By leveraging advanced deep learning techniques, this platform aims to assist users in identifying and segmenting skin abnormalities, thus improving access to dermatological diagnostics.
-
-### Objectives
-
-- **Image Upload:** Users can easily upload images of their skin lesions for analysis.
-- **Image Segmentation:** The application utilizes algorithms like U-Net to isolate and identify skin anomalies.
-- **Anomaly Analysis:** Detected anomalies are classified into categories such as melanoma, carcinoma, etc., using machine learning models.
-- **Diagnostic Report:** Users receive a comprehensive report detailing the analysis results and recommended actions.
-- **User-Friendly Interface:** The application features a simple and intuitive interface for seamless navigation.
-
----
-
-## Technologies Used
-
-- **Programming Language:** Python (for the entire application)
-- **User Interface:** Gradio (for creating an interactive interface for image uploads and results visualization)
-- **Frameworks:** TensorFlow or PyTorch (for image segmentation model development)
-- **Database:** SQLite (for optional storage of analysis results)
-- **Hosting:** Hugging Face Spaces (for online deployment of the Gradio application)
-
 ---
-
-## Methodology
-
-1. **Data Collection:**
-   - Utilize the **DermNet Dataset** for training segmentation and classification models.
-   - Assess the quality and diversity of images in the dataset to ensure comprehensive coverage of various skin anomalies.
-
-2. **Model Development:**
-   - **Data Preprocessing:** Clean, resize, and augment images for training.
-   - **Segmentation Model:** Develop and train a segmentation model (e.g., U-Net) to detect skin anomalies.
-   - **Classification Model:** Build a classification model to categorize detected anomalies.
-   - Implement an efficient pipeline, incorporating tools like MLflow and TensorBoard.
-
-3. **Evaluation:**
-   - Use performance metrics (e.g., Dice Coefficient, IoU, precision, recall) to evaluate the segmentation model's effectiveness.
-   - Test the application on a separate dataset to ensure the robustness of results.
-
----
-
-## Deliverables
-
-- A functional web application capable of detecting and segmenting skin diseases.
-- Technical documentation detailing the architecture, usage, and development of the application.
-- Performance evaluation report of both the model and the application.
-
+title: Dermato Studio
+emoji: 🦀
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 5.1.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: DERMATO STUDIO :Medical Image Segmentation
 ---
 
-## Getting Started
-
-### Installation
-
-To run the application locally, follow these steps:
-
-1. Clone the repository:
-   ```bash
-   git clone https://github.com/mrcryptsie/dermato-studio.git
-   cd dermato-studio
-   ```
-
-2. Install the required dependencies:
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Run the application:
-   ```bash
-   python app.py
-   ```
-
-### Usage
-
-- Access the web application through your browser.
-- Upload your dermatoscopic images for analysis.
-- Review the results and generated reports.
-
----
-
-## Resources
-
-For further development and research, the following resources are recommended:
-
-- **Frameworks:**
-  - [TensorFlow Documentation](https://www.tensorflow.org/)
-  - [PyTorch Documentation](https://pytorch.org/)
-- **Segmentation Techniques:**
-  - [U-Net Original Paper](https://arxiv.org/abs/1505.04597)
-  - [U-Net Keras Tutorial](https://towardsdatascience.com/keras-u-net-implementation-for-image-segmentation-8eebdc1d443b)
-- **Gradio Documentation:** [Gradio Docs](https://gradio.app/docs)
-
----
-
-## Contribution
-
-We welcome contributions to enhance the application further. If you'd like to contribute, please fork the repository and submit a pull request.
-
----
-
-## Conclusion
-
-The **DERMATO STUDIO** project aims to provide a cutting-edge solution for early skin disease detection, utilizing advanced machine learning and image analysis techniques. By raising awareness and improving accessibility to dermatological diagnostics, we strive to make a meaningful impact on public health.
-
-For any inquiries, feel free to reach out!
-
----
-
-### Sponsor Us
-
-Your support can make a significant difference! Become a sponsor to help us continue our work. If you become a gold, silver, or bronze tier sponsor, your company's logo will be showcased on this README.
-
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference