Updated README.md with template metadata

README.md

@@ -1,2 +1,109 @@
-# anomily_detection_RAG
-find anomalies in classifications of documents using AI
+# DocVerifyRAG: Document Verification and Anomaly Detection
+
+## Application Description
+
+DocVerifyRAG is a revolutionary tool designed to streamline document verification processes in hospitals. It utilizes AI to classify documents and identify mistakes in metadata, ensuring accurate and efficient document management. Inspired by the need for improved data accuracy in healthcare, DocVerifyRAG provides automated anomaly detection to identify misclassifications and errors in document metadata, enhancing data integrity and compliance with regulatory standards.
+
+## Table of Contents
+
+<details>
+<summary>Table of Contents</summary>
+
+- [DocVerifyRAG: Document Verification and Anomaly Detection](#docverifyrag-document-verification-and-anomaly-detection)
+- [Application Description](#application-description)
+- [Table of Contents](#table-of-contents)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Contributing](#contributing)
+- [Authors](#authors)
+- [License](#license)
+
+</details>
+
+## Video Demo
+
+[link](https://link.com)
+
+## Web App
+
+[link](https://link.com)
+
+## Screenshots
+
+[Add screenshots here]
+
+## Technology Stack
+
+| Technology | Description                 |
+| ---------- | --------------------------- |
+| AI/ML      | Artificial Intelligence and Machine Learning |
+| Python     | Programming Language        |
+| Flask      | Web Framework               |
+| Docker     | Containerization            |
+| Tech Name    | Short description                    |
+
+### Features
+
+1. **Document Classification:**
+    - Utilizes AI/ML algorithms to classify documents based on content and metadata.
+    - Provides accurate and efficient document categorization for improved data management.
+
+2. **Anomaly Detection:**
+    - Identifies mistakes and misclassifications in document metadata through automated anomaly detection.
+    - Enhances data integrity and accuracy by flagging discrepancies in document metadata.
+
+3. **User-Friendly Interface:**
+    - Offers a user-friendly web interface for easy document upload, classification, and verification.
+    - Simplifies the document management process for hospital staff, reducing manual effort and errors.
+
+### Install locally
+
+1. Clone the repository:
+    ```bash
+    $ git clone https://github.com/your-username/DocVerifyRAG.git
+    ```
+
+2. Navigate to the project directory:
+    ```bash
+    $ cd DocVerifyRAG
+    ```
+
+3. Install dependencies:
+    ```bash
+    $ pip install -r requirements.txt
+    ```
+
+### Install using Docker
+
+To deploy DocVerifyRAG using Docker, follow these steps:
+
+1. Build a Docker image for the application:
+
+    ```bash
+    $ docker build -t docverifyrag .
+    ```
+
+2. Run the Docker container:
+
+    ```bash
+    $ docker run -d -p 5000:5000 docverifyrag
+    ```
+
+### Usage
+
+Access the web interface and follow the prompts to upload documents, classify them, and verify metadata. The AI-powered anomaly detection system will automatically flag any discrepancies or errors in the document metadata, providing accurate and reliable document management solutions for hospitals.
+
+## Authors
+
+| Name           | Link                                      |
+| -------------- | ----------------------------------------- |
+| Sandra Ashipala | [GitHub](https://github.com/sandramsc) |
+| Elia Wäfler | [GitHub](https://github.com/eliawaefler) |
+| Carlos Salgado | [GitHub](https://github.com/salgadev) |
+| Your Name | [GitHub](https://github.com/name) |
+
+## License
+
+[![GitLicense](https://img.shields.io/badge/License-MIT-lime.svg)](https://github.com/eliawaefler/DocVerifyRAG/blob/main/LICENSE)
+____
+