File size: 2,029 Bytes
3822931 22055af 3822931 d7bb799 3822931 d7bb799 3822931 de6c365 3822931 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | from fastapi import FastAPI, File, UploadFile, Form
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import shutil
import tempfile
import os
import json
# Orquestador Médico
from ADNI_Fusion_Engine import PerdixAdniEngine
app = FastAPI(title="Perdix ADNI Medical Node", version="1.0.0")
# Permitir a NextJS conectarse a este motor en Hugging Face
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # En producción cambiar por la URL de vercel
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
print("Inicializando Motor ADNI Neuronal en Hugging Face...")
engine = PerdixAdniEngine()
@app.get("/")
def read_root():
return {"status": "online", "message": "Perdix ADNI Engine is running. Send POST to /diagnose."}
@app.post("/diagnose")
async def diagnose(
age: float = Form(65.0),
mmse_score: float = Form(25.0),
edu_years: float = Form(12.0),
mri_scan: UploadFile = File(...)
):
try:
# Guardar la imagen subida en disco temporalmente
ext = os.path.splitext(mri_scan.filename)[1]
if not ext:
ext = ".png"
with tempfile.NamedTemporaryFile(delete=False, suffix=ext) as tmp:
shutil.copyfileobj(mri_scan.file, tmp)
tmp_path = tmp.name
# Ejecutamos la Inferencia con PyTorch
result = engine.diagnose_patient(
image_path=tmp_path,
age=age,
mmse_score=mmse_score,
edu_years=edu_years
)
# Inyectar ID Clínico
import uuid
result["diagnosis_id"] = "ADNI-" + str(uuid.uuid4())[:8]
# Limpieza de Disco
os.unlink(tmp_path)
return result
except Exception as e:
return {"status": "error", "message": str(e)}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)
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