Disease Classification Model

This repository contains a machine learning model for disease classification, implemented using PyTorch and deployed with Streamlit.

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Repository Structure

• models/: Contains the trained model files.
• train.py: Script to train the model.
• preprocessing.py: Script to preprocess the dataset and create dataloaders.
• engine.py: Contains the training and testing steps.
• prediction.py: Contains the prediction function.
• deployment.py: The Streamlit app script.

Usage

Cloning the Repository

Clone the repository to your local machine:

git clone https://github.com/end-9214/Disease_classification_model.git
cd Disease_classification_model

Installing Dependencies

Install the required dependencies using pip:

pip install -r requirements.txt

Training the Model

python train.py

This script will:

• Load and preprocess the dataset using preprocessing.py.
• Define and train the model using the functions in engine.py.
• Save the trained model to the models/ directory.

Accessing the Streamlit App

The Streamlit app provides an interactive interface to use the disease classification model. To access the app:

Ensure you have Streamlit installed:

pip install streamlit
Run the Streamlit app:

streamlit run deployment.py

Open your web browser and navigate to the provided local URL to interact with the app.

Prediction Function

The prediction.py script contains the function to make predictions using the trained model. It is used within the Streamlit app to display predictions and their probabilities