app.py

import tensorflow as tf
from PIL import Image
import numpy as np
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import io
import base64
import uvicorn
from fastapi.middleware.cors import CORSMiddleware
import pydantic

app = FastAPI()

# Add CORS middleware
app.add_middleware(
	CORSMiddleware,
	allow_origins=["*"],
	allow_methods=["*"],
	allow_headers=["*"]
)

app.mount("/static", StaticFiles(directory="static"), name="static")

@app.get("/")
def read_root():
	return FileResponse("static/index.html")

# Load the model
model = tf.keras.models.load_model('model/k49_model.h5')

class ImageRequest(pydantic.BaseModel):
	image: str
	label: int

@app.post("/api/predict")
def predict(request: dict):
	image = request["image"]
	label = request["label"]
		
	# Decode base64-encoded image
	image_bytes = base64.b64decode(image["image"].split(",")[1])

	# Convert image to preprocessed numpy array and save image to server
	X = preprocess(image_bytes)
	image = Image.fromarray(X[0].reshape(28, 28) * 255).convert("RGB")
	
	image.save("image.png")

	# Make prediction using the model
	prediction = model.predict(X)
	# Convert prediction to integer
	prediction = int(np.argmax(prediction))

	print("label: ", label)
	print("prediction: ", prediction)

	return {'prediction': prediction == int(label)}


def preprocess(image_bytes):
	# Decode base64-encoded image and convert to PIL Image object
	img = Image.open(io.BytesIO(image_bytes))
	# Convert transparent background to white
	if img.mode == 'RGBA':
		img.load()
		background = Image.new('RGB', img.size, (255, 255, 255))
		background.paste(img, mask=img.split()[3])
		img = background
	# Convert to grayscale
	img = img.convert('L')
	# Invert colors to black background with white numbers
	img = np.invert(np.array(img))
	# Resize image to 28x28 pixels
	img = Image.fromarray(img).resize((28, 28))
	# Convert image to numpy array and normalize pixel values
	X = np.array(img).reshape(1, 28, 28, 1) / 255
	return X

if __name__ == '__main__':
	uvicorn.run('app:app', host='localhost', port=8000, reload=True)