Update README.md

README.md

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 app_file: app.py
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-
-# Optimized Log Classification Using LLMs
----
-A comprehensive framework for hybrid log classification that integrates multiple analytical techniques to effectively process and categorize log data.
-This system leverages different methods to handle simple, complex, and sparsely labeled log patterns.
- ---
-
-## Overview
-
-This project combines three primary classification strategies:
-
-- **Regex-based Classification**
-  Captures predictable patterns using predefined regular expressions.
-
-- **Embedding-based Classification**
-  Uses Sentence Transformers to generate embeddings followed by Logistic Regression for nuanced pattern recognition.
-
-- **LLM-assisted Classification**
-  Employs large language models to classify data when traditional methods struggle due to limited labeled samples.
-
-![System Architecture](resources/arch.png)
-
----
-
-## Directory Structure
-
-- **`training/`**
-  Contains notebooks and scripts for training the models and experimenting with different approaches.
-
-- **`models/`**
-  Stores pre-trained models such as the logistic regression classifier and embedding models.
-
-- **`resources/`**
-  Holds auxiliary files like CSV datasets, output samples, and images.
-
-- **Root Directory**
-  Includes the main API server (`server.py`) and the command-line classification utility (`classify.py`).
-
----
-
-## Installation & Setup
-
-1. **Clone the Repository**
-   ```bash
-   git clone <your_repository_url>
-   ```
-
-2. **Install Dependencies**
-   Ensure Python is installed and run:
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. **Train the Model (if needed)**
-   Open and run the training notebook:
-   ```bash
-   jupyter notebook training/log_classification.ipynb
-   ```
-
-4. **Run the API Server**
-   Start the server using one of the following methods:
-   - Direct execution:
-     ```bash
-     python server.py
-     ```
-   - With Uvicorn:
-     ```bash
-     uvicorn server:app --reload
-     ```
-   Access the API documentation at:
-   - Main Endpoint: [http://127.0.0.1:8000/](http://127.0.0.1:8000/)
-   - Swagger UI: [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
-   - Redoc: [http://127.0.0.1:8000/redoc](http://127.0.0.1:8000/redoc)
-
-5. **Running the Streamlit App**
-   To start the Streamlit application for log classification:
-   ```bash
-   streamlit run app.py
-   ```
-   This command will launch the app in your browser at a URL like http://localhost:8501.
----
-
-## Usage Instructions
-
-- **Input Data**
-  Upload a CSV file with the following columns:
-  - `source`
-  - `log_message`
-
-- **Output**
-  The system processes the logs and returns a CSV file with an additional `target_label` column indicating the classification result.
-
----
-
-## Customization
-
-Feel free to modify and extend the classification logic in the following modules:
-- `processor_bert.py`
-- `processor_llm.py`
-- `processor_regex.py`
-
-These modules are designed to be flexible, allowing you to tailor the classification approaches to your specific needs.
-
----
-
-## Contributions
-Contributions, feedback, and feature requests are welcome.
-Please open an issue or submit a pull request in your GitHub repository.