main.py

from fastapi import FastAPI, Request, Form
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates

from pydantic import BaseModel

from typing import Optional, Annotated
from classification_model import predict

import uvicorn

app = FastAPI()
app.mount("/static/", StaticFiles(directory="static"), name="static")

templates = Jinja2Templates(directory="templates")


@app.get("/", response_class=HTMLResponse)
async def index(request: Request):
    return templates.TemplateResponse(
        "index.html",
        {
            "request": request,
            "params": ["petal_length", "petal_width", "sepal_length", "sepal_width"],
        },
    )


@app.get("/valid-float", response_class=HTMLResponse)
async def validate_floats(request: Request):
    param, val = list(request.query_params.items())[0]
    if not val.isalpha():
        return templates.TemplateResponse(
            "param-field.html", {"request": request,
                                 "param": param, "error": None, "value": val}
        )
    else:
        return templates.TemplateResponse(
            "param-field.html",
            {"request": request, "param": param,
                "error": "This param must be a float64 value"},
        )


@app.post("/classify", response_class=HTMLResponse)
async def classify_endpoint(
    request: Request,
    petal_length: Annotated[str, Form()],
    petal_width: Annotated[str, Form()],
    sepal_length: Annotated[str, Form()],
    sepal_width: Annotated[str, Form()],
):
    predicted_class = predict(
        [petal_length, petal_width, sepal_length, sepal_width])
    return templates.TemplateResponse(
        "classification.html", {"request": request, "class": predicted_class}
    )


if __name__ == "__main__":
    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)