FastAPI post method ends up giving "Method not allowed"

Question

I am trying to deploy an image classification model on server using FastAPI.

As such, I have two issues related to my code.

The first issue is that in the original code (without using FastAPI), I would read an image using OpenCV and then convert it from BGR to RGB. Not doing this conversion would give me inaccurate results at test time.

Using FastAPI, the image is being read as follows:

def read_image(payload):
    stream=BytesIO(payload)
    image=np.asarray(bytearray(stream.read()),dtype="uint8")
    image=cv2.imdecode(image,cv2.IMREAD_COLOR)
    if isinstance(image,np.ndarray):
        img=Image.fromarray(image)
    return img

The second issue I am facing is with the POST method when I run the server, and accessing the URL http:127.0.0.1:9999/, the GET method is running, which prints the following message:

Welcome to classification server

However, when I execute the post method shown below:

@app.post("/classify/")
async def classify_image(file:UploadFile=File(...)):
    #return "File Uploaded."
    image_byte=await file.read()
    return classify(image_byte)

When I go to the link http:127.0.0.1:9999/classify/ I end up recieving the error:

method not allowed

Any reasons on why this is happening and what can be done to fix the error? The full code is listed below. If there are any errors that I am missing in this, please let me know. I am new to FastAPI and as such, I am really confused about this.

from fastapi import FastAPI, UploadFile, File
import uvicorn
import torch
import torchvision
from torchvision import transforms as T
from PIL import Image
from build_effnet import build_model
import torch.nn.functional as F
import io
from io import BytesIO
import numpy as np
import cv2

app = FastAPI()

class_name = ['F_AF', 'F_AS', 'F_CA', 'F_LA', 'M_AF', 'M_AS', 'M_CA', 'M_LA']
idx_to_class = {i: j for i, j in enumerate(class_name)}
class_to_idx = {value: key for key, value in idx_to_class.items()}

test_transform = T.Compose([
                             
                             #T.Resize(size=(224,224)), # Resizing the image to be 224 by 224
                             #T.RandomRotation(degrees=(-20,+20)), #NO need for validation
                             T.ToTensor(), #converting the dimension from (height,weight,channel) to (channel,height,weight) convention of PyTorch
                             T.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225]) # Normalize by 3 means 3 StD's of the image net, 3 channels
])

#Load model
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = build_model()
model.load_state_dict(torch.load(
    'CharacterClass_effnet_SGD.pt', map_location='cpu'))
model.eval()
model.to(device)

def read_image(payload):
    stream=BytesIO(payload)
    image=np.asarray(bytearray(stream.read()),dtype="uint8")
    image=cv2.imdecode(image,cv2.IMREAD_COLOR)
    if isinstance(image,np.ndarray):
        img=Image.fromarray(image)
    return img

def classify(payload):
    img=read_image(payload)
    img=test_transform(img)
    with torch.no_grad():
        ps=model(img.unsqueeze(0))
        ps=F.softmax(ps,dim=1)
        topk,topclass=ps.topk(1,dim=1)
    x=topclass.view(-1).cpu().view()
    return idx_to_class[x[0]]
        
@app.get("/")
def get():
    return "Welcome to classification server."

@app.post("/classify/")
async def classify_image(file:UploadFile=File(...)):
    #return "File Uploaded."
    image_byte=await file.read()
    return classify(image_byte)

Answer 1

Your code has defined the classify route on POST requests. Your browser will only perform GET requests from the url

If you expect the browser to work, use @app.get("/classify/"). However, you'll need a different way to provide the file argument, which as a query parameter and a file path (not recommended for security reasons).

If you want POST requests to work, test your code with curl or Postman