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| title: Mip Csv Analyser | |
| emoji: 🚀 | |
| colorFrom: yellow | |
| colorTo: gray | |
| sdk: streamlit | |
| sdk_version: 1.28.1 | |
| app_file: app.py | |
| pinned: false | |
| # Batch Run Analyzer | |
| A comprehensive Streamlit application for analyzing batch run results from CSV or XLSX files, visualizing pass/fail statistics, and comparing runs across different environments. | |
| ## Features | |
| - Support for both CSV and XLSX file formats | |
| - Multiple analysis modes: | |
| - **Multi**: Analyze multiple files from different environments | |
| - **Compare**: Compare two files to identify differences in scenario outcomes | |
| - **Weekly**: Generate weekly trend reports | |
| - **Multi-Env Compare**: Compare scenarios across multiple environments | |
| - Detailed statistics on passing and failing scenarios | |
| - Visual charts for failure counts by functional area | |
| - Interactive filtering by functional area and status | |
| - Time spent analysis per functional area | |
| - Error Message analysis | |
| ## Setup and Installation | |
| 1. Clone this repository: | |
| ``` | |
| git clone <repository-url> | |
| cd batch-run-csv-analyser | |
| ``` | |
| 2. Install the required dependencies: | |
| ``` | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Run the application: | |
| ``` | |
| streamlit run app.py | |
| ``` | |
| ## File Format Support | |
| ### CSV Format (Legacy) | |
| The application still supports the original CSV format with the following columns: | |
| - Functional area | |
| - Scenario Name | |
| - Start datetime | |
| - End datetime | |
| - Status | |
| - Error Message | |
| ### XLSX Format (New) | |
| The application now supports XLSX files with step-level data: | |
| - Feature Name | |
| - Scenario Name | |
| - Step | |
| - Result | |
| - Time Stamp | |
| - Duration (ms) | |
| - Error Message | |
| The application will automatically detect the file format based on the file extension and process it accordingly. | |
| ## Usage | |
| 1. Start the application with `streamlit run app.py` | |
| 2. Use the sidebar to select the desired analysis mode | |
| 3. Upload the necessary files based on the selected mode | |
| 4. Follow the on-screen instructions for filtering and analysis | |
| ## Analysis Modes | |
| ### Multi Mode | |
| Upload files from multiple environments for individual analysis. View statistics, filter by functional area, and see charts of failing scenarios. | |
| ### Compare Mode | |
| Upload two files to compare scenario statuses between them. The application will identify: | |
| - Consistent failures (failed in both files) | |
| - New failures (passed in the older file, failed in the newer) | |
| - New passes (failed in the older file, passed in the newer) | |
| ### Weekly Mode | |
| Upload files from multiple dates to see trend reports. Filter by environment and functional area, and view detailed statistics for each day. | |
| ### Multi-Env Compare Mode | |
| Compare scenarios across multiple environments to identify inconsistencies in test coverage. | |
| ## Notes | |
| - Filename format is important for date extraction in Weekly mode. The application will try to extract dates using various patterns like `name_YYYYMMDD_HHMMSS`, `name_YYYYMMDD`, or any 8-digit sequence resembling a date. | |
| - For XLSX files, all steps within a scenario are aggregated to determine the overall scenario status. | |
| ## Troubleshooting | |
| If you encounter issues: | |
| 1. Ensure the file format follows the expected structure | |
| 2. Check the logs for specific error messages | |
| 3. Try processing smaller files first to verify functionality | |
| # Jira Integration for Test Analysis | |
| This application provides a Streamlit interface for analyzing test results and creating Jira tasks for failed scenarios. | |
| ## Setup | |
| 1. Clone the repository | |
| 2. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Create a `.env` file in the root directory with the following variables: | |
| ```env | |
| JIRA_SERVER=your_jira_server_url | |
| GROQ_API_KEY=your_groq_api_key | |
| ``` | |
| ## Environment Variables | |
| - `JIRA_SERVER`: Your Jira server URL (e.g., https://jira.yourdomain.com) | |
| - `GROQ_API_KEY`: Your Groq API key for AI functionality | |
| ## Running the Application | |
| ```bash | |
| streamlit run jira_integration.py | |
| ``` | |
| ## Features | |
| - Jira authentication and session management | |
| - Test scenario analysis | |
| - Automated Jira task creation | |
| - Sprint statistics tracking | |
| - Functional area mapping | |
| - Customer field mapping | |
| ## Deployment | |
| This application is designed to be deployed on Huggingface Spaces. When deploying: | |
| 1. Add the environment variables in the Huggingface Spaces settings | |
| 2. Ensure all dependencies are listed in requirements.txt | |
| 3. The application will automatically use the environment variables from Huggingface Spaces | |
| ## Security Notes | |
| - Never commit the `.env` file to version control | |
| - Keep your Jira credentials secure | |
| - Use environment variables for all sensitive information |