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(...)):
    global dataset
    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)
        
        global trained_model
        trained_model=results["model"]
        
        global encoder
        encoder=results["encoder"]
        
        global transform
        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):
    if trained_model is not None:
        arr=[[item.Torque,item.Hydraulic_Pressure,item.Cutting,item.Coolant_Pressure,item.Spindle_Speed,item.Coolant_Temperature]]
        results=predict(trained_model,encoder,transform,arr)
        return results
    else:
        return {"message":"First Train Model"}