import shutil
import glob
import os

import numpy as np
import pandas as pd
from fastapi import FastAPI,UploadFile,File
from pydantic import BaseModel,Field

from app.modelling import train
from app.inference import predict

dataset=None
trained_model=None
encoder=None
transform=None

class Item(BaseModel):
    Torque:float=Field(gt=0,default=24.25)
    Hydraulic_Pressure:float=Field(gt=0,default=121.86)
    Cutting:float=Field(gt=0,default=2.89)
    Coolant_Pressure:float=Field(gt=0,default=6.96)
    Spindle_Speed:float=Field(gt=0,default=20504.0)
    Coolant_Temperature:float=Field(gt=0,default=14.9)

app=FastAPI()

@app.get("/")
def home():
    return {"message":"Hello World!"}

@app.post("/upload/")
def upload_csv(file:UploadFile=File(...)):
    dataset=pd.read_csv(file.file)
    file.file.close()
    return {"filename": file.filename}

@app.post("/train/")
def training():
    if dataset is not None:
        results=train(dataset)
        trained_model=results["model"]
        encoder=results["encoder"]
        transform=results["transform"]
        return {"Accuracy":results["Accuracy"],
                "F1_Score":results["F1_Score"]}
    else:
        return {"message":"First Upload Dataset"}

@app.post("/predict/")
def prediction(item:Item):
    cwd=os.getcwd()
    path=os.path.join(cwd,"app","model.pkl")
    if os.path.exists(path):
        arr=[[item.Torque,item.Hydraulic_Pressure,item.Cutting,item.Coolant_Pressure,item.Spindle_Speed,item.Coolant_Temperature]]
        results=predict(arr)
        return results
    else:
        return {"message":"First Train Model"}