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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"}
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