specs-ai 0.1.2

AI-powered hardware upgrade assistant for Windows

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AI-powered hardware upgrade assistant for Windows

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specs-ai reads your machine's hardware via WMI and psutil, then asks a Gemini-powered LLM for specific, actionable upgrade recommendations — knowing exactly what you have.

Collected specs: CPU (model, cores, clock, socket), RAM (capacity, type, speed, slots), GPU (model, VRAM, Integrated/Dedicated), Motherboard, Storage (all drives, NVMe/SATA/HDD), WiFi adapter, Battery health (laptops), System uptime, OS metadata.

The CPU socket tells the LLM whether a CPU is soldered (BGA — cannot upgrade) or swappable (LGA/AM4/etc.). GPU type tells it whether graphics are integrated (part of the CPU die, not upgradeable on a laptop) or a discrete card.

[!WARNING] Windows only. specs-ai relies on WMI (Windows Management Instrumentation) and pywin32 for hardware extraction. It will not run on Linux or macOS. There are no plans to support other platforms at this time.

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Screenshots

Dashboard (specs-ai --dashboard)

CLI Summary (specs-ai)

Detailed Recommendations (specs-ai --explain)

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Install

pip install specs-ai

Or with uv:

uv pip install specs-ai

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API Key Setup

Get a free Gemini API key at https://aistudio.google.com/apikey, then pick one of:

Option A — Shell environment variable:

$env:SPECS_AI_API_KEY = "your-key-here"

Option B — .env file (in the project root or any parent directory):

SPECS_AI_API_KEY=your-key-here

A template is provided at .env.example — copy it to .env and fill in your key.

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Usage

specs-ai [--explain] [--specs-only] [--dashboard] [--model MODEL]

Flag	Description
(none)	Collect specs and show a compact upgrade summary table
--explain	Full detailed recommendations — specific part models, impact-per-dollar
--specs-only	Print hardware specs only; skip the LLM call entirely
--dashboard	Launch the interactive hacker-themed TUI dashboard
--model MODEL	Gemini model to use (default: gemini-2.5-flash)

# Compact summary table (default)
specs-ai

# Detailed recommendations with specific part suggestions
specs-ai --explain

# Interactive TUI dashboard
specs-ai --dashboard

# Just the hardware snapshot, no LLM call
specs-ai --specs-only

Also works via the module entry point:

python -m specs_ai
python -m specs_ai --explain
python -m specs_ai --dashboard

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Dashboard

Launch with specs-ai --dashboard for a full-screen, hacker-themed hardware monitoring dashboard built with Textual and Rich.

Features:

• Big blocky ANSI Shadow SPECS-AI title in neon green
• Matrix-style digital rain animation on left/right gutters
• Live CPU sparkline, RAM usage bar, and per-volume disk usage bars
• Color-coded uptime — green < 5d, amber 5–15d, red > 15d
• AI upgrade recommendations panel (queries Gemini in the background)
• Scrolling event log with timestamped activity
• JSON export of the full hardware snapshot

Key	Action
Q	Quit
R	Re-scan hardware + re-query Gemini
E	Toggle compact table / detailed explain mode
S	Export specs to specs_report.json
H	Toggle help overlay

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How It Works

Your Machine
     │
     ▼
WMI + psutil          ← reads CPU, RAM, GPU, storage, battery, WiFi, uptime
     │
     ▼
Prompt builder        ← formats specs, injects BGA/iGPU/uptime advisories
     │
     ▼
Gemini API            ← gemini-2.5-flash by default; swap with --model
     │
     ▼
Upgrade recommendations (table or detailed narrative)

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Notes

• BGA CPUs (soldered) are correctly flagged as non-upgradeable
• Integrated GPUs on laptops are marked as non-upgradeable; on desktops, adding a discrete card is suggested
• VRAM is read from the Windows registry (not WMI) to avoid the 32-bit DWORD overflow that misreports GPUs with ≥ 4 GB VRAM
• WMI calls run in daemon threads with a 10-second timeout to prevent hangs on broken driver states
• Uptime advisories are injected into the prompt at 5 days (suggest reboot) and 15 days (strongly recommend reboot)

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Development

Requires uv:

git clone https://github.com/vybhav72954/specs-ai
cd specs-ai
uv run pytest tests/ -v

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Contributing & Issues

Bug reports and feature requests are welcome. If you run into a problem or have a suggestion, please open an issue on GitHub with:

• Your Windows version and Python version
• The full error message or unexpected output
• The command you ran

Pull requests are also welcome for bug fixes and improvements.

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License

Apache 2.0