如何使用 TensorFlow.js 在 React Native 中运行 Python 模型？

Question

尝试将用 Python 训练的 resNet50 模型添加到我的 React Native 项目中，使用 TensorFlow.js 进行图像预测，以根据输入提供 5 个相似图像：

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")

Answer 1

您可以尝试使用Pyodide（基于WebAssembly的浏览器和Node.js的Python发行版）在js环境中运行python代码。