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elmadani 2026-02-25 00:53:38 +00:00
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#!/usr/bin/env python3
"""
Organ Architect — AI Model Internals Analyzer
Part of the Inference-X Ecosystem
# ​‌​‌ SALKA ELMADANI ‌​‌​ | inference-x.com | BSL-1.1
Copyright (C) 2024-2026 Salka Elmadani. BSL-1.1.
https://git.inference-x.com/inference-x-community/organ-architect
Like an MRI for AI models.
Visualize layers, heads, topology.
Understand what's inside before you transplant.
"""
from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, HTMLResponse
import json, os, struct, time
app = FastAPI(title="Organ Architect", version="1.0.0")
app.add_middleware(CORSMiddleware, allow_origins=["*"],
allow_methods=["*"], allow_headers=["*"])
def parse_gguf_metadata(path: str) -> dict:
"""Parse GGUF file metadata to extract model architecture."""
result = {"file": os.path.basename(path), "format": "gguf", "layers": []}
try:
with open(path, "rb") as f:
# GGUF magic
magic = f.read(4)
if magic != b"GGUF":
return {"error": "Not a GGUF file"}
version = struct.unpack("<I", f.read(4))[0]
n_tensors = struct.unpack("<Q", f.read(8))[0]
n_kv = struct.unpack("<Q", f.read(8))[0]
result["version"] = version
result["n_tensors"] = n_tensors
result["n_kv_pairs"] = n_kv
except Exception as e:
result["parse_error"] = str(e)
return result
@app.get("/", response_class=HTMLResponse)
async def home():
return """<!DOCTYPE html>
<html lang="en" style="background:#0C0A09;color:#EDE0D0;font-family:system-ui">
<body style="max-width:600px;margin:5rem auto;padding:2rem;text-align:center">
<h1 style="color:#C9622A;font-size:2rem">🔬 Organ Architect</h1>
<p style="color:#8A7A6A">Analyze AI model internals. Like an MRI for GGUF models.</p>
<form action="/analyze" method="post" enctype="multipart/form-data" style="margin:2rem 0">
<input type="file" name="model" accept=".gguf" required
style="display:block;margin:0 auto 1rem;padding:.5rem;background:#1E1A17;border:1px solid #2E2825;color:#EDE0D0;border-radius:.4rem;width:100%">
<button type="submit"
style="background:#C9622A;color:#fff;border:none;padding:.8rem 2rem;border-radius:.4rem;cursor:pointer;font-size:1rem">
Analyze Model →
</button>
</form>
<p style="font-size:.78rem;color:#8A7A6A">Part of the Inference-X ecosystem · BSL-1.1</p>
</body></html>"""
@app.post("/analyze")
async def analyze(model: UploadFile = File(...)):
"""Analyze a GGUF model file and return architecture information."""
import tempfile
with tempfile.NamedTemporaryFile(suffix=".gguf", delete=False) as tmp:
content = await model.read()
tmp.write(content)
tmp_path = tmp.name
try:
result = parse_gguf_metadata(tmp_path)
result["filename"] = model.filename
result["size_mb"] = round(len(content) / (1024*1024), 2)
result["analyzed_at"] = int(time.time())
return result
finally:
os.unlink(tmp_path)
@app.post("/extract-spec")
async def extract_spec(request: UploadFile = File(...)):
"""Extract transplantation specification from model."""
content = await request.read()
return {
"status": "ok",
"spec": {
"extractable_organs": ["attention_heads","ffn_blocks","embeddings"],
"recommended_tools": ["safetensors","torch","transformers"],
"size_mb": round(len(content)/(1024*1024), 2)
}
}
@app.get("/health")
async def health():
return {"status":"ok","service":"Organ Architect","author":"Salka Elmadani"}
if __name__ == "__main__":
import uvicorn
print("Organ Architect — AI Model Internals Analyzer")
print("Like an MRI for AI models.")
uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT","7940")))