import torch
from fastapi import FastAPI
from pydantic import BaseModel
class Config:
model = "gemini-2.5"
batch_size = 32
lr = 3e-4
epochs = 100
app = FastAPI()
@app.post("/predict")
async def predict(req):
features = extract(
req.data,
pipeline="v3"
)
return model(features)
# ── data pipeline ──
SELECT
region,
COUNT(*) as trips,
AVG(duration) as avg_t
FROM rides
WHERE status = 'done'
GROUP BY region
ORDER BY trips DESC;
async def train():
for epoch in range(N):
loss = step(batch)
if loss < best:
save(model)
best = loss
# ── config ──────
[fikia]
name = "nzela-api"
region = "af-east-1"
runtime = "python3.11"
env:
DB_HOST: postgres
REDIS: cache:6379
MODEL_PATH: /v3/
def transform(df):
df = df.dropna()
df["lat"] = to_rad(
df.latitude
)
return normalize(df)
# ── deploy ──────
stages:
- build
- test
- ship
class Agent:
def __init__(self):
self.memory = []
self.tools = load()
async def run(self,
task, ctx=None
):
plan = reason(task)
for step in plan:
r = execute(step)
self.memory.add(r)
return synthesize(
self.memory
)
# ── metrics ─────
latency_p99: 142ms
throughput: 2.4k rps
accuracy: 0.94
uptime: 99.97%
router.post("/api/v2")
async def handler(body):
validated = parse(body)
result = await pipe(
validated,
steps=[
clean,
embed,
classify
]
)
return {"ok": True}
