Initial commit

2026-02-25 00:53:38 +00:00 · 2026-02-25 00:53:38 +00:00 · b3e0955b65
commit b3e0955b65
parent 3310c1d6dd
1 changed files with 97 additions and 0 deletions
--- a/organ_architect.py
+++ 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("<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")))