app.py

from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model
import os
os.environ["KERAS_BACKEND"] = "tensorflow"
	

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

ANIMALS = ['Cat', 'Dog', 'Panda'] # Animal names here, these represent the labels of the images that we trained our model on.

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


model = load_model("hf://nathansegers/masterclass-2025")

@app.post('/upload/image')
async def uploadImage(img: UploadFile = File(...)):
    original_image = Image.open(img.file) # Read the bytes and process as an image
    resized_image = original_image.resize((64, 64)) # Resize
    images_to_predict = np.expand_dims(np.array(resized_image), axis=0) # Our AI Model wanted a list of images, but we only have one, so we expand it's dimension
    predictions = model.predict(images_to_predict) # The result will be a list with predictions in the one-hot encoded format: [ [0 1 0] ]
    prediction_probabilities = predictions
    classifications = prediction_probabilities.argmax(axis=1) # We try to fetch the index of the highest value in this list [ [1] ]

    return ANIMALS[classifications.tolist()[0]] # Fetch the first item in our classifications array, format it as a list first, result will be e.g.: "Dog"