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@@ -115,6 +115,33 @@ _This plot shows the separability of the positive and negative classes in a redu
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  ![Placeholder for CLS Embeddings Visualization](umap.png)
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  _This plot shows the separability of the positive and negative classes in a reduced-dimension space using UMAP._
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  ## Licensing
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  This model is licensed under the **GNU Lesser General Public License v2.1 (LGPL-2.1)**.
 
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  ![Placeholder for CLS Embeddings Visualization](umap.png)
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  _This plot shows the separability of the positive and negative classes in a reduced-dimension space using UMAP._
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+ ## Web Interface for Easy Access
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+ For a user-friendly way to interact with the model, you can use the Streamlit web application. This interface allows you to predict on a single protein sequence or upload a multi-sequence FASTA file for batch processing. The application is also designed to automatically handle hardware constraints, switching to CPU if a CUDA device is not available or if it runs out of memory.
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+ To run the web interface, follow these steps:
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+ 1. **Clone the repository:** Open your terminal and clone the `AVP-ESM` repository from GitHub.
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+ ```bash
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+ git clone [https://github.com/kssrikar4/AVP-ESM.git](https://github.com/kssrikar4/AVP-ESM.git)
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+ cd AVP-ESM
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+ ```
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+ 2. **Install dependencies:** The application requires several Python libraries. You can install them by creating a `requirements.txt` file
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+ ```bash
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+ python -m venv py
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+ source py/bin/activate # On Windows: `py\Scripts\activate`
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+ pip install -r requirements.txt
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+ ```
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+ 3. **Run the application:** Once the dependencies are installed, you can launch the web interface with the following command:
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+ ```bash
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+ streamlit run app.py
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+ ```
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+ Your default web browser should open automatically, displaying the Protein Virulence Predictor application.
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  ## Licensing
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  This model is licensed under the **GNU Lesser General Public License v2.1 (LGPL-2.1)**.