Upload initial model files

README.md

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----
-license: apache-2.0
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+# Streamlit ML Frontend
+
+This project is a Streamlit application that serves as the front end for a machine learning model designed to predict medical outcomes based on synthetic patient data. The application allows users to input patient features and receive predictions along with relevant mappings.
+
+## Project Structure
+
+```
+streamlit-ml-frontend
+├── src
+│   ├── app.py          # Main entry point for the Streamlit application
+│   └── utils.py        # Utility functions for data processing and model interaction
+├── requirements.txt     # List of dependencies for the project
+└── README.md            # Documentation for the project
+```
+
+## Installation
+
+To set up the project, follow these steps:
+
+1. Clone the repository:
+   ```
+   git clone <repository-url>
+   cd streamlit-ml-frontend
+   ```
+
+2. Create a virtual environment (optional but recommended):
+   ```
+   python -m venv venv
+   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
+   ```
+
+3. Install the required dependencies:
+   ```
+   pip install -r requirements.txt
+   ```
+
+## Running the Application
+
+To run the Streamlit application, execute the following command in your terminal:
+
+```
+streamlit run src/app.py
+```
+
+This will start the Streamlit server and open the application in your default web browser.
+
+## Usage
+
+1. Input the required patient features in the provided fields.
+2. Click on the "Predict" button to receive predictions from the machine learning model.
+3. The results will be displayed on the screen, including the predicted disease and any relevant mappings.
+
+## Model Information
+
+The underlying machine learning model has been trained on synthetic medical data and is designed to provide predictions based on the input features. Please note that this model is for development and testing purposes only and should not be used for real medical decisions.
+
+## License
+
+This project is licensed under the MIT License. See the LICENSE file for more details.

synthetic_v2_disease_mappings.joblib

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synthetic_v2_pipeline.joblib

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synthetic_v2_target_encoders.joblib

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