Got it 👍 — let me clean this up into a well-formatted README.md that you can just copy–paste. I’ll make sure headings, code blocks, and sections are clear and readable on GitHub.

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🚀 AI-Powered MySQL Observability CLI Tool

A command-line tool to collect MySQL metrics, monitor performance, and detect anomalies using machine learning.

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📦 Installation

1. Clone this repository

   git clone https://github.com/<your-username>/mysql-observability-cli.git
   cd mysql-observability-cli

2. Set up a Python environment

   python3 -m venv venv
   source venv/bin/activate   # macOS/Linux
   venv\Scripts\activate      # Windows

3. Install dependencies

   pip install -r requirements.txt

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🛠️ Configuration

1. Start your MySQL server Make sure MySQL is running locally (or on a host you can connect to).

2. Edit config.json On first run, the tool will auto-create config.json. Update it with your credentials:

   {
     "mysql": {
       "host": "localhost",
       "port": 3306,
       "user": "youruser",
       "password": "yourpassword",
       "database": "mysql"
     },
     "ml": {
       "contamination": 0.1,
       "random_state": 42,
       "min_samples_for_training": 10
     },
     "metrics": {
       "enabled": [
         "Questions",
         "Threads_connected",
         "Threads_running",
         "Slow_queries",
         "Innodb_buffer_pool_size",
         "Innodb_buffer_pool_pages_data",
         "Innodb_buffer_pool_pages_free",
         "Connections",
         "Uptime"
       ]
     }
   }

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▶️ Usage

1. (Optional) Select a database

If you have select_database.py:

python select_database.py

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2. Collect metrics

python p3cli.py --monitor

• Collects 100 samples of MySQL metrics
• Saves them in metrics_history.json
• Displays the last one in a table

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3. Run anomaly detection

python p3cli.py --analyze

• Trains an IsolationForest ML model on historical metrics
• Detects if the most recent datapoint looks anomalous
• Requires at least 10 datapoints (satisfied since --monitor collects 100)

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4. Advanced analysis

• AI-based anomaly detection

  python p3cli.py --ai-analyze

• Query analysis

  python p3cli.py --analyze-queries

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5. Show current configuration

python p3cli.py --config

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📊 Example Workflow

# Step 1: Start MySQL server
brew services start mysql   # macOS
sudo systemctl start mysql  # Linux

# Step 2: Run monitor (collects 100 datapoints)
python p3cli.py --monitor

# Step 3: Run anomaly detection
python p3cli.py --analyze

# Step 4 (optional): Run AI-enhanced anomaly detection
python p3cli.py --ai-analyze

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🗂️ Project Structure

.
├── p3cli.py              # Main CLI tool
├── config.json           # User configuration (auto-created on first run)
├── metrics_history.json  # Saved metrics history
├── recent_analysis.json  # Last analysis results
├── select_database.py    # (Optional) database selector script
├── requirements.txt      # Dependencies
└── README.md             # This guide

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⚠️ Notes

• metrics_history.json and recent_analysis.json should be ignored in Git (add to .gitignore)
• At least 10 datapoints are required for anomaly detection (already handled by default)
• If MySQL is not running, the tool falls back to demo mode (simulated metrics)

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✅ Now your README will look neat and structured on GitHub.

Do you also want me to give you a .gitignore file so you don’t push local JSON files, venv, and cache stuff to GitHub?