Update README.md

README.md

@@ -1,164 +1,43 @@
-# Cancer Risk Prediction Model
-
-## Overview
-This project implements a neural network-based model for predicting cancer risk levels based on patient characteristics and lifestyle factors. The model provides risk assessments through an interactive web interface, making it accessible for healthcare professionals.
-
-## Features
-- Neural network model for cancer risk prediction
-- Interactive web interface for real-time predictions
-- Support for multiple patient characteristics and lifestyle factors
-- Pre-loaded example cases for reference
-- Real-time prediction updates
-
-## Prerequisites
-- Python 3.8+
-- Required Python packages:
-  ```bash
-  pip install gradio numpy pandas torch scikit-learn
-  ```
-
-## Project Structure
-```
-├── code/
-│   ├── gradio_interface.py     # Web interface implementation
-│   ├── ann_risk_inference.py   # Model inference code
-│   └── model_training.py       # Model training code
-├── dataset/
-│   └── cancer_patient_data_sets.csv    # Training dataset
-├── models/
-│   └── ann_model.pth          # Trained model weights
-└── README.md
-```
-
-## Installation
-
-1. Clone the repository:
-```bash
-git clone [repository-url]
-cd cancer-risk-prediction
-```
-
-2. Install dependencies:
-```bash
-pip install -r requirements.txt
-```
-
-## Usage
-
-### Running the Web Interface
-
-1. Navigate to the code directory:
-```bash
-cd code
-```
-
-2. Launch the Gradio interface:
-```bash
-python gradio_interface.py
-```
-
-3. Open your web browser and go to the URL displayed in the terminal (typically `http://localhost:7860`)
-
-### Using the Interface
-
-1. Adjust the sliders for each patient characteristic:
-   - Age
-   - Gender
-   - Air Pollution
-   - Alcohol Use
-   - Dust Allergy
-   - Occupational Hazards
-   - Genetic Risk
-   - Chronic Lung Disease
-   - Balanced Diet
-   - Obesity
-
-2. The prediction will update automatically, showing:
-   - Risk Level
-   - Classification Result
-
-3. You can also use the provided example cases to see predictions for real patient data.
-
-## Input Features
-
-| Feature | Range | Description |
-|---------|--------|-------------|
-| Age | Integer value | Patient's age |
-| Gender | 0-1 | Patient's gender |
-| Air Pollution | 0-1 | Exposure level to air pollution |
-| Alcohol Use | 0-1 | Level of alcohol consumption |
-| Dust Allergy | 0-1 | Severity of dust allergy |
-| Occupational Hazards | 0-1 | Level of occupational risk exposure |
-| Genetic Risk | 0-1 | Genetic risk factor |
-| Chronic Lung Disease | 0-1 | Presence and severity of lung disease |
-| Balanced Diet | 0-1 | Assessment of dietary habits |
-| Obesity | 0-1 | Body mass index classification |
-
-## Model Output
-
-The model provides two key pieces of information:
-1. Risk Level: A categorical assessment of cancer risk
-2. Class: The numerical classification of risk level
-
-## Technical Details
-
-- The model is implemented using PyTorch
-- Interface is built using Gradio
-- Data preprocessing is handled using Pandas and NumPy
-- Model architecture: Artificial Neural Network (ANN)
-
-## Dataset
-
-The model is trained on a comprehensive dataset of cancer patient characteristics, including:
-- Demographic information
-- Environmental factors
-- Lifestyle choices
-- Medical history
-- Genetic factors
-
-
-=======
----
-title: Cancer Risk Prediction Model
-emoji: 🏥
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.50.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-# Cancer Risk Prediction Model
-
-This Gradio web application predicts cancer risk levels (Low, Medium, High) based on patient characteristics using a neural network model.
-
-## Model Details
-
-The model is a neural network trained on cancer patient data with the following features:
-- Input: 23 patient characteristics
-- Output: Risk classification (Low, Medium, High)
-- Architecture: Multi-layer perceptron
-
-## Usage
-
-1. Adjust the sliders to input patient characteristics
-2. The model will automatically predict the risk level
-3. Example cases are provided for reference
-
-## Technical Stack
-
-- PyTorch: Neural network framework
-- Gradio: Web interface
-- Pandas & NumPy: Data processing
-- Scikit-learn: Data preprocessing
-
-## Dataset
-
-The model uses the Cancer Patient Data Sets, which includes various patient characteristics and their corresponding risk levels.
-
-## License
-
-This project is licensed under the MIT License. 
-
+---
+title: Cancer Risk Prediction Model
+emoji: 🏥
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 3.50.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# Cancer Risk Prediction Model
+
+This Gradio web application predicts cancer risk levels (Low, Medium, High) based on patient characteristics using a neural network model.
+
+## Model Details
+
+The model is a neural network trained on cancer patient data with the following features:
+- Input: 23 patient characteristics
+- Output: Risk classification (Low, Medium, High)
+- Architecture: Multi-layer perceptron
+
+## Usage
+
+1. Adjust the sliders to input patient characteristics
+2. The model will automatically predict the risk level
+3. Example cases are provided for reference
+
+## Technical Stack
+
+- PyTorch: Neural network framework
+- Gradio: Web interface
+- Pandas & NumPy: Data processing
+- Scikit-learn: Data preprocessing
+
+## Dataset
+
+The model uses the Cancer Patient Data Sets, which includes various patient characteristics and their corresponding risk levels.
+
+## License
+
+This project is licensed under the MIT License.