Как запустить модель Python в React Native, используя TensorFlow.js? ⇐ Android

[Anonymous]

Пытаюсь добавить модель resNet50, обученную на Python, в мой проект React Native с использованием TensorFlow.js для прогнозирования изображений, чтобы предоставить 5 похожих изображений на основе входных данных:
import streamlit as st
import os
from PIL import Image
import numpy as np
import pickle
import tensorflow
from tensorflow.keras.layers import GlobalMaxPooling2D
from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input
from sklearn.neighbors import NearestNeighbors
from numpy.linalg import norm
import cv2

feature_list = np.array(pickle.load(open('featurevector.pkl','rb')))
filenames = pickle.load(open('filenames.pkl','rb'))

model = ResNet50(weights='imagenet',include_top=False,input_shape=(224,224,3))
model.trainable = False

model = tensorflow.keras.Sequential([
model,
GlobalMaxPooling2D()
])

st.title('Man & Women Fashion Recommender System')

def save_uploaded_file(uploaded_file):
try:
with open(os.path.join('uploads',uploaded_file.name),'wb') as f:
f.write(uploaded_file.getbuffer())
return 1
except:
return 0

def extract_feature(img_path, model):
img=cv2.imread(img_path)
img=cv2.resize(img, (224,224))
img=np.array(img)
expand_img=np.expand_dims(img, axis=0)
pre_img=preprocess_input(expand_img)
result=model.predict(pre_img).flatten()
normalized=result/norm(result)
return normalized

def recommend(features,feature_list):
neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
neighbors.fit(feature_list)

distances, indices = neighbors.kneighbors([features])

return indices

# steps
# file upload -> save
uploaded_file = st.file_uploader("Choose an image")
print(uploaded_file)
if uploaded_file is not None:
if save_uploaded_file(uploaded_file):
# display the file
display_image = Image.open(uploaded_file)
resized_img = display_image.resize((200, 200))
st.image(resized_img)
# feature extract
features = extract_feature(os.path.join("uploads",uploaded_file.name),model)
#st.text(features)
# recommendention
indices = recommend(features,feature_list)
# show
col1,col2,col3,col4,col5 = st.columns(5)

with col1:
st.image(filenames[indices[0][1]])
with col2:
st.image(filenames[indices[0][2]])
with col3:
st.image(filenames[indices[0][3]])
with col4:
st.image(filenames[indices[0][4]])
with col5:
st.image(filenames[indices[0][5]])
else:
st.header("Some error occured in file upload")



Подробнее здесь: https://stackoverflow.com/questions/781 ... sorflow-js