pascalx/pathloss-predictor

1---2title: Pathloss App3emoji: 📡4colorFrom: indigo5colorTo: gray6sdk: docker7license: mit8short_description: utilizes ANN, CNN, DNN to accurately predicting pathloss9---10 11# Pathloss App12 13 A web application for predicting path loss using various machine learning models (ANN, CNN, DNN). The app provides a user-friendly interface for inputting parameters and visualizing results.14 15 ## Features16 17 - Predict path loss using pre-trained models (ANN, CNN, DNN)18 - Simple web interface for user input and result display19 - Model and data pre-processing handled automatically20 - Docker support for easy deployment21 22 ## Project Structure23 24 ```25 app.py                # Main Flask application26 Dockerfile            # Docker configuration27 requirements.txt      # Python dependencies28 models/               # Pre-trained models and preprocessor29 static/style.css      # Custom styles30 templates/            # HTML templates31 ```32 33 ## Getting Started34 35 ### Prerequisites36 37 - Python 3.8+38 - pip39 40 ### Installation41 42 1. Clone the repository:43	 ```44	 git clone <repo-url>45	 cd pathloss-app46	 ```47 48 2. Install dependencies:49	 ```50	 pip install -r requirements.txt51	 ```52 53 3. Run the application:54	 ```55	 python app.py56	 ```57 58 4. Open your browser and go to `http://localhost:5000`59 60 ### Docker61 62 To run with Docker:63 ```64 docker build -t pathloss-app .65 docker run -p 5000:5000 pathloss-app66 ```67 68 ## Usage69 70 - Enter the required parameters in the web form.71 - Select the desired model (ANN, CNN, DNN).72 - View the predicted path loss and related results.73 74 ## File Descriptions75 76 - `app.py`: Main Flask application logic.77 - `models/`: Contains pre-trained models (`.h5`) and preprocessor (`.pkl`).78 - `static/style.css`: Custom CSS for the app.79 - `templates/`: HTML templates for UI.80 81 ## License82 83 This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.84 85 86