from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import HTMLResponse
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("/", response_class=HTMLResponse)
def test_upload():
html_content = """
Animal Image Classifier
🐾 Animal Image Classifier
Upload an image of a Cat, Dog, or Panda to classify it!
Classifying...
"""
return HTMLResponse(content=html_content)
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"