96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
#!/usr/bin/env python3
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"""
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Organ Store — AI Model Component Marketplace
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Part of the Inference-X Ecosystem
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# SALKA ELMADANI | inference-x.com | BSL-1.1
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Copyright (C) 2024-2026 Salka Elmadani. BSL-1.1.
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https://git.inference-x.com/inference-x-community/organ-store
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Neural networks have anatomy.
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Extract. Share. Transplant.
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The future of open AI.
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"""
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from fastapi import FastAPI, Request, UploadFile, File
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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import sqlite3, json, time, os
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app = FastAPI(title="Organ Store", version="1.0.0")
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app.add_middleware(CORSMiddleware, allow_origins=["*"],
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allow_methods=["*"], allow_headers=["*"])
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DB = os.environ.get("ORGAN_DB", "./organ_store.db")
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ORGAN_TYPES = ["attention_head","ffn_block","expert_block","embedding","lm_head","custom"]
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def db():
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conn = sqlite3.connect(DB); conn.row_factory = sqlite3.Row; return conn
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def init_db():
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with db() as c:
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c.execute("""CREATE TABLE IF NOT EXISTS organs (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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name TEXT NOT NULL, type TEXT NOT NULL,
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source_model TEXT, description TEXT,
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capability TEXT, download_url TEXT,
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size_mb REAL DEFAULT 0, downloads INTEGER DEFAULT 0,
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author TEXT DEFAULT 'anonymous', created_at INTEGER,
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verified INTEGER DEFAULT 0
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)""")
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c.commit()
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init_db()
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@app.get("/catalog")
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async def catalog(type_filter: str = None, limit: int = 50):
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"""Browse available organs in the store."""
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with db() as c:
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q = "SELECT * FROM organs ORDER BY downloads DESC LIMIT ?"
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params = [limit]
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if type_filter and type_filter in ORGAN_TYPES:
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q = "SELECT * FROM organs WHERE type=? ORDER BY downloads DESC LIMIT ?"
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params = [type_filter, limit]
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rows = c.execute(q, params).fetchall()
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return {"organs":[dict(r) for r in rows],"total":len(rows),"types":ORGAN_TYPES}
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@app.post("/publish")
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async def publish(request: Request):
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"""Publish an organ to the community store."""
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data = await request.json()
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if not data.get("name") or not data.get("type"):
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return JSONResponse({"error":"name and type required"}, status_code=400)
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if data["type"] not in ORGAN_TYPES:
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return JSONResponse({"error":f"type must be one of {ORGAN_TYPES}"}, status_code=400)
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with db() as c:
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c.execute("""INSERT INTO organs (name,type,source_model,description,capability,
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download_url,size_mb,author,created_at)
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VALUES (?,?,?,?,?,?,?,?,?)""",
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(data["name"], data["type"], data.get("source_model",""),
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data.get("description",""), data.get("capability",""),
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data.get("download_url",""), float(data.get("size_mb",0)),
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data.get("author","anonymous"), int(time.time())))
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organ_id = c.lastrowid
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c.commit()
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return {"status":"ok","organ_id":organ_id,"message":"Organ published to the community store."}
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@app.get("/organ/{organ_id}")
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async def get_organ(organ_id: int):
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with db() as c:
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organ = c.execute("SELECT * FROM organs WHERE id=?", (organ_id,)).fetchone()
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if organ:
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c.execute("UPDATE organs SET downloads=downloads+1 WHERE id=?", (organ_id,))
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c.commit()
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if not organ:
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return JSONResponse({"error":"Organ not found"}, status_code=404)
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return dict(organ)
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@app.get("/health")
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async def health():
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return {"status":"ok","service":"Organ Store","author":"Salka Elmadani"}
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if __name__ == "__main__":
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import uvicorn
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print("Organ Store — AI Model Component Marketplace")
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print("Extract. Share. Transplant.")
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uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT","7939")))
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