diff --git a/organ_architect.py b/organ_architect.py new file mode 100644 index 0000000..eaedcb6 --- /dev/null +++ b/organ_architect.py @@ -0,0 +1,97 @@ +#!/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(" + + +

🔬 Organ Architect

+

Analyze AI model internals. Like an MRI for GGUF models.

+
+ + +
+

Part of the Inference-X ecosystem · BSL-1.1

+""" + +@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")))