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-# LSTM Autoencoder for Time Series Anomaly Detection
-
-[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/rajatsingh0702/LSTMAE)
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1h62dcS5nWos4wczenkG8iDKTJiHRZIqk)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) <!-- Choose your license -->
-
-This repository contains the implementation of an LSTM (Long Short-Term Memory) Autoencoder for detecting anomalies in time series data. You can either use a pre-trained model provided here or train a new model on your own CSV dataset.
-
-An interactive demo is available on Hugging Face Spaces, and a Google Colab notebook is provided for experimentation.
-
-## Key Features
-
-*   **LSTM Autoencoder:** Built using PyTorch.
-*   **Two Modes:**
-    1.  **Use Pre-trained Model:** Quickly analyze time series data using the included model.
-    2.  **Train on Custom Data:** Upload your own CSV file to train a new LSTM Autoencoder tailored to your specific data.
-*   **Comprehensive Output:** Generates insightful plots and artifacts:
-    *   Andrews Curves Plot
-    *   Training Loss Curve
-    *   Anomaly Score Distribution
-    *   Evaluation Curve (e.g., ROC Curve, Precision-Recall Curve, or your custom "ANDRE" curve - *please clarify if "ANDRE" is a custom metric*)
-*   **Downloadable Results:** Packages the trained model, data scalers, and all generated plots into a convenient ZIP file for download.
-*   **Interactive Demo:** Hugging Face Space for easy interaction without local setup.
-*   **Colab Notebook:** Experiment with the code, training, and evaluation in a Google Colab environment.
-
-## How it Works
-
-An LSTM Autoencoder is trained on 'normal' time series data.
-1.  The **Encoder** (an LSTM network) learns to compress the input time series into a lower-dimensional latent representation.
-2.  The **Decoder** (another LSTM network) learns to reconstruct the original time series from this latent representation.
-3.  During inference, the model tries to reconstruct new, unseen time series sequences.
-4.  If a sequence is similar to the normal data seen during training, the reconstruction error (the difference between the input and the reconstructed output) will be low.
-5.  If a sequence contains anomalies (patterns not seen during training), the model struggles to reconstruct it accurately, resulting in a high reconstruction error.
-6.  By setting a threshold on the reconstruction error, we can classify sequences as normal or anomalous.
-
-## Installation (Local Setup)
-
-1.  **Clone the repository:**
-    ```bash
-    git clone https://github.com/Rajatsingh24/LSTM-based-Autoencoder.git
-    cd LSTM-based-Autoencoder
-    ```
-2.  **Create a virtual environment (recommended):**
-    ```bash
-    python -m venv venv
-    source venv/bin/activate  # On Windows use `venv\Scripts\activate`
-    ```
-3.  **Install dependencies:**
-    *Make sure you have a `requirements.txt` file in your repository.*
-    ```bash
-    pip install -r requirements.txt
-    ```
-    
-## Usage
-
-You can interact with the model primarily through the Hugging Face Space or the Colab Notebook.
 
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 title: LSTMAE