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import numpy as np
import tensorflow as tf
import io, base64, requests
from pydantic import BaseModel
# SCHEMA
class Schema(BaseModel):
resized_img_base64:str = None,
img_url:str = None
# Request Handler
def face_analytics(req):
resized_img_base64 = req.resized_img_base64
img_url = req.img_url
output = predict(resized_img_base64, img_url)
return output
model_path = "./src/face_analytics/model.h5"
def predict(img_data, img_url):
if img_url == None:
content = img_data.replace(" ", "+")
converted = bytes(content, "utf-8")
img = base64.decodebytes(converted)
else:
img = requests.get(img_url).content
model = tf.keras.models.load_model(model_path)
img = io.BytesIO(img)
img = tf.keras.preprocessing.image.load_img(img, target_size=model.input_shape[1:])
img = np.array(img)
img = img.reshape(1, *img.shape)
img = tf.keras.applications.inception_v3.preprocess_input(img)
pred = model.predict(img)
return [[round(j, 3) for j in i] for i in np.hstack([(1-pred).T, pred.T]).tolist()]
return [
[0.3, 0.7],
[0.2, 0.8],
[0.9, 0.1],
] |