{"id":15105258,"url":"https://github.com/dash7ou/corporatica_task","last_synced_at":"2025-04-05T12:41:48.019Z","repository":{"id":257454276,"uuid":"857868404","full_name":"dash7ou/Corporatica_Task","owner":"dash7ou","description":"flask, mongodb, docker, k8s","archived":false,"fork":false,"pushed_at":"2024-09-19T09:08:31.000Z","size":319,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-10T23:55:25.796Z","etag":null,"topics":["docker","flask","k8s","mongodb"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dash7ou.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-15T20:10:14.000Z","updated_at":"2024-09-19T09:08:35.000Z","dependencies_parsed_at":"2024-09-16T21:51:50.941Z","dependency_job_id":"3de7cbb6-42c4-4b7b-b17a-a54c0166d2be","html_url":"https://github.com/dash7ou/Corporatica_Task","commit_stats":{"total_commits":10,"total_committers":1,"mean_commits":10.0,"dds":0.0,"last_synced_commit":"e138d90a3dd624600536fa5ea8c3688a2f5f123d"},"previous_names":["dash7ou/corporatica_task"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dash7ou%2FCorporatica_Task","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dash7ou%2FCorporatica_Task/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dash7ou%2FCorporatica_Task/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dash7ou%2FCorporatica_Task/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dash7ou","download_url":"https://codeload.github.com/dash7ou/Corporatica_Task/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247339137,"owners_count":20923009,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["docker","flask","k8s","mongodb"],"created_at":"2024-09-25T20:22:22.486Z","updated_at":"2025-04-05T12:41:48.002Z","avatar_url":"https://github.com/dash7ou.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# ProcessApp - Corporatica\n\n**ProcessApp** is a dynamic Flask-based application designed for processing a variety of data types: tabular datasets, RGB images, and text data. The application exposes a set of RESTful APIs that allow users to upload data, perform processing tasks, and generate visual outputs such as statistics, graphs, and text summaries.\n\n## Features\n\n### 1. Tabular Data Processing\n\n- **Data Upload and Management**: Users can upload tabular datasets (CSV, Excel) for analysis and management.\n- **Statistical Analysis**: APIs are provided for computing key statistics such as:\n  - Mean, Median, Mode\n  - Quartile ranges and outlier detection\n- **Data Visualization**: Generate interactive charts and graphs to visualize data trends dynamically.\n- **Dataset CRUD Operations**: A web-based interface allows users to create, update, delete, and query datasets with ease.\n\n### 2. RGB Image Processing\n\n- **Image Upload and Storage**: Users can upload individual or multiple images, which are stored and made accessible for further manipulation.\n- **Color Analysis \u0026 Segmentation**: The app offers APIs to generate color histograms, segment images, and fine-tune color processing parameters.\n- **Image Editing Tools**: Supports basic image operations such as resizing, cropping, and format conversions via the web interface.\n\n### 3. Text Data Processing\n\n- **Text Analysis**: The app includes functionality for processing text data, including text summarization, keyword extraction, and basic sentiment analysis.\n- **T-SNE Visualizations**: Provides APIs to create dynamic visualizations using T-SNE (t-distributed stochastic neighbor embedding) for dimensionality reduction on text-based datasets.\n- **Text Search \u0026 Categorization**: Users can perform complex text searches, classify text, and execute custom-defined queries.\n\n## Core Libraries and Technologies\n\nThe application is built using a combination of powerful Python libraries and tools to handle diverse data types and processing tasks:\n\n- **Flask**: Core web framework used to create API routes and web services.\n- **Flask-RESTful**: Simplifies building REST APIs within the Flask ecosystem.\n- **pymongo**: Facilitates interactions with MongoDB for storing and retrieving data.\n- **pandas**: Used for efficient handling and analysis of tabular datasets.\n- **plotly**: Enables the creation of dynamic, interactive data visualizations for statistical outputs.\n- **pydantic-settings**: Manages configuration and environment settings.\n- **uuid**: Generates unique IDs for various entities, such as image and data records.\n- **opencv-python**: Provides image processing capabilities for handling RGB images, generating histograms, and segmentation.\n- **pillow**: Additional support for image manipulation and format conversion.\n- **matplotlib**: Used for plotting static graphs and charts for data visualization.\n- **scikit-learn**: Offers machine learning functionalities, including dimensionality reduction techniques like T-SNE.\n- **textblob**: Facilitates text analysis tasks, including sentiment analysis and keyword extraction.\n- **nltk**: Provides natural language processing (NLP) tools for text categorization, tokenization, and other text-based operations.\n\n## Installation\n\n### Prerequisites\n\n- **Python 3.9+** installed\n- **MongoDB** installed and running\n- **Docker** (optional for containerized deployment)\n- **Kubernetes** (Minikube recommended for local Kubernetes setup)\n\n### Setup Steps\n\n1. **Clone the repository**:\n\n   ```bash\n   git clone https://github.com/yourusername/processapp.git\n   cd processapp\n   ```\n2. **Create a virtual environment and install dependencies**:\n\n   ```bash\n   python3.9 -m venv venv\n   source venv/bin/activate\n   pip install -r requirements.txt\n   ```\n3. **Set environment variables**:\n   Create a `.env` file with the following details:\n\n   ```env\n   FLASK_APP=src/app.py\n   FLASK_ENV=development\n   DATABASE_URL=mongodb://localhost:27017/processapp\n   ```\n4. **Run the application**:\n\n   ```bash\n   flask run\n   ```\n\n   Access the application at `http://localhost:5000`.\n\n## K8s Setup\n\n---\n\n### Kubernetes Setup with Minikube - Just Locally\n\nTo deploy the ProcessApp application using Minikube, follow these steps:\n\n1. **Start Minikube**:\n   Ensure Minikube is running:\n\n   ```bash\n   minikube start\n   ```\n2. **Configure Docker for Minikube**:\n   Set up your Docker environment to use Minikube’s Docker daemon:\n\n   ```bash\n   eval $(minikube docker-env)\n   ```\n3. **Build the Docker Image**:\n   Build your Docker image for the application:\n\n   ```bash\n   docker build -t processapp:latest .\n   ```\n4. **Enable Ingress Addon**:\n   Enable the NGINX Ingress controller in Minikube:\n\n   ```bash\n   minikube addons enable ingress\n   ```\n5. **Deploy Kubernetes Resources**:\n   Apply the Kubernetes configuration to deploy the application. Ensure you have a file named `k8s_app.yml` with the necessary Kubernetes manifests:\n\n   ```bash\n   kubectl apply -f k8s_app.yml\n   ```\n6. **Access the Application**:\n   Use Minikube’s service URL to access the application:\n\n   ```bash\n   minikube service processapp-service\n   ```\n   Alternatively, if you have set up Ingress, you may access your application via the configured hostname.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdash7ou%2Fcorporatica_task","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdash7ou%2Fcorporatica_task","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdash7ou%2Fcorporatica_task/lists"}