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from fastapi import FastAPI, Request, Form
from fastapi.responses import HTMLResponse
from fastapi.templating import Jinja2Templates
from pydantic import BaseModel
from typing import List
import uvicorn
from pipeline.inference_pipeline import load_model, predict_batch
app = FastAPI()
templates = Jinja2Templates(directory="serving/templates")
model = load_model()
class TimeSeriesBatch(BaseModel):
data: List[List[float]]
@app.get("/", response_class=HTMLResponse)
async def form_get(request: Request):
return templates.TemplateResponse("index.html", {"request": request, "result": None})
@app.post("/", response_class=HTMLResponse)
async def form_post(request: Request, series: str = Form(...)):
try:
batch = eval(series) # Replace this with safe parsing for production
result = predict_batch(batch, model)
return templates.TemplateResponse("index.html", {"request": request, "result": result})
except Exception as e:
return templates.TemplateResponse("index.html", {"request": request, "result": f"Error: {str(e)}"})
@app.post("/predict")
async def predict_api(input: TimeSeriesBatch):
return {"prediction": predict_batch(input.data, model)}
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