add: as service

app.py

@@ -0,0 +1,71 @@
+import itertools
+
+from fastapi import FastAPI, BackgroundTasks
+from pydantic import BaseModel
+import numpy as np
+from NeuralSolver import NeuralSolver
+from typing import List, Tuple
+
+app = FastAPI()
+solver: NeuralSolver | None = None
+status: str = "none"
+
+def task():
+    global solver
+    global status
+    status = "training forward"
+    solver.train_forward(1000)
+    status = "training backward"
+    solver.train_backward(1000)
+    status = "trained"
+
+class InitRequest(BaseModel):
+    width: int
+    height: int
+    quotientX: bool = False
+    quotientY: bool = False
+
+@app.post("/initialize")
+def initialize(request: InitRequest, background_tasks: BackgroundTasks):
+    global solver
+    global status
+    if status != "none":
+        return {"status": "instance already existed"}
+    solver = NeuralSolver(request.width, request.height, request.quotientX, request.quotientY)
+    background_tasks.add_task(task)
+    return {"status": "initializing"}
+
+
+class BoardRequest(BaseModel):
+    lives: List[Tuple[int, int]]
+    direction: str
+
+@app.post("/predict")
+def predict(request: BoardRequest):
+    global solver
+    global status
+    if status != "trained":
+        return {"status": "not trained yet"}
+    inputs = np.zeros((1, solver.Width, solver.Height, 1))
+    for (x, y) in request.lives:
+        inputs[0, x, y, 0] = 1.0
+    res = None
+    if request.direction == "forward":
+        res = solver.predict_forward(inputs)
+    else:
+        res = solver.predict_reverse(inputs)
+    lives = set()
+    for (x, y) in itertools.product(range(solver.Width), range(solver.Height)):
+        if res[0, x, y, 0]:
+            lives.add((x, y))
+    return {"prediction": lives}
+
+@app.post("/finish")
+def finish():
+    global solver
+    global status
+    solver = None
+    status = "none"
+
+
+