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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
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(uploaded_file:UploadFile=File(...)):
cwd=os.getcwd()
path=os.path.join(cwd,"app","dataset.csv")
with open(path, 'w+b') as file:
shutil.copyfileobj(uploaded_file.file, file)
return {'file': uploaded_file.filename,
'content': uploaded_file.content_type,
'path': path}
"""
@app.post("/upload/")
async def upload_csv(file:UploadFile=File(...)):
df=pd.read_csv(file.file)
global dataset
dataset=Dataset.from_pandas(df)
file.file.close()
return {"filename": file.filename}
@app.post("/train/")
def training():
if dataset is not None:
results=train(dataset)
return results
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"}
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