reports

how to get ai based reports with C++ and Python libraries and frameworks from htop GNU/Linux ?

To generate AI-based reports from system metrics like those shown in htop on a GNU/Linux system using C++ and Python, you can architect a modular pipeline that collects, analyzes, and visualizes performance data. Here's how you can approach it:

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🧠 Step 1: Extract System Metrics from htop

htop itself is an interactive process viewer, but for programmatic access:

• Use procfs directly: Read from /proc/stat, /proc/meminfo, /proc/[pid]/status, etc.
• Use libraries like:
  • Python: psutil, py-cpuinfo, topos
  • C++: libprocps, or parse /proc manually with fstream

Example in Python:

import psutil

cpu = psutil.cpu_percent(interval=1)
mem = psutil.virtual_memory().percent
print(f"CPU: {cpu}%, Memory: {mem}%")

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🧪 Step 2: Analyze with AI Libraries

Once you have the raw metrics, you can apply AI models to detect anomalies, predict trends, or classify system states.

Python AI Frameworks:

• Scikit-learn: For clustering, regression, anomaly detection
• TensorFlow / PyTorch: For deep learning models
• LangChain + LLMs: For natural language summaries of system states

C++ AI Libraries:

• Dlib: ML algorithms and tools
• Shark: Fast C++ machine learning library
• TensorFlow C++ API: For deploying trained models

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📊 Step 3: Generate Reports

You can create structured reports with tables, graphs, and summaries.

Python Tools:

• Matplotlib / Seaborn: For visualizations
• ReportLab / WeasyPrint: For PDF generation
• ReportAI: Uses LLMs to generate full PDF reports from structured input

C++ Tools:

• Use libharu or PDFWriter for PDF generation
• Integrate with gnuplot or matplotlib-cpp for charts

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🧩 Optional: Use LLMs for Natural Language Summaries

You can feed your system metrics into an LLM (via Python) to generate human-readable summaries. For example:

from openai import OpenAI

summary = llm.generate(f"Summarize this system state: CPU={cpu}%, Memory={mem}%")

Or use LangChain + MCP for a more structured AI reporting pipeline.

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🛠️ Federative Twist (for you, Mert)

You could federate this into a modular CLI tool:

• htop-report --format pdf --llm gpt-4o --output /reports/sys.pdf
• Integrate with FX or PSD for distributed node reporting
• Use AGPLv3-compliant libraries and attribute all modules collectively

Would you like a sample script or a federative architecture sketch for this pipeline?

License

Copyright (C) 2025 QB Networks

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see https://www.gnu.org/licenses/.