diff --git a/.gitignore b/.gitignore
index c487f23..928d56d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,3 +12,4 @@ run/
 *.un~
 typings/
 .nyc_output/
+api-service.zip
\ No newline at end of file
diff --git a/app.js b/app.js
new file mode 100644
index 0000000..573b2da
--- /dev/null
+++ b/app.js
@@ -0,0 +1,31 @@
+'use strict';
+// eslint-disable-next-line no-unused-vars
+const nsfw = require('nsfwjs');
+// eslint-disable-next-line no-unused-vars
+const tf = require('@tensorflow/tfjs-node');
+
+class AppBootHook {
+  constructor(app) {
+    this.app = app;
+  }
+
+  configWillLoad() {
+    // console.log('config: ', this.app.config.uploadHost);
+  }
+
+  async didLoad() {
+    console.log('nsfwModel 加载中···');
+    this.app.nsfwModel = await nsfw.load('file://./app/model/', { type: 'graph' });
+    // this.app.nsfwModel = await tf.loadGraphModel('file://./app/model/model.json');
+    console.log('nsfwModel 已经加州完毕！');
+  }
+
+  //   async willReady() {
+
+  //   }
+  //   async didReady() {
+
+  //   }
+}
+
+module.exports = AppBootHook;
diff --git a/app/controller/checkImage.js b/app/controller/checkImage.js
new file mode 100644
index 0000000..9ccbada
--- /dev/null
+++ b/app/controller/checkImage.js
@@ -0,0 +1,41 @@
+'use strict';
+
+const Controller = require('egg').Controller;
+// const fs = require('fs');
+const respFormat = require('../util/respFormat');
+
+class CheckImageController extends Controller {
+  async check() {
+    const { ctx } = this;
+    console.log(ctx.request.body);
+    console.log('got %d files', ctx.request.files.length);
+
+    try {
+
+      const filesResult = [];
+      // 遍历处理多个文件
+      for (const file of ctx.request.files) {
+        console.log('field: ' + file.fieldname);
+        console.log('filename: ' + file.filename);
+        console.log('encoding: ' + file.encoding);
+        console.log('mime: ' + file.mime);
+        console.log('tmp filepath: ' + file.filepath);
+        const contentCheck = await ctx.service.indecentContentCheck.contentCheck(file);
+        console.log('contentCheck检测结果: ', contentCheck);
+        const item = {
+          check: contentCheck,
+          filename: file.filename,
+        };
+        filesResult.push(item);
+      }
+
+      ctx.body = respFormat({ data: filesResult });
+      await ctx.cleanupRequestFiles();
+    } finally {
+      // 需要删除临时文件
+      await ctx.cleanupRequestFiles();
+    }
+  }
+}
+
+module.exports = CheckImageController;
diff --git a/app/model/group1-shard1of6.bin b/app/model/group1-shard1of6.bin
new file mode 100644
index 0000000..57e2f57
Binary files /dev/null and b/app/model/group1-shard1of6.bin differ
diff --git a/app/model/group1-shard2of6.bin b/app/model/group1-shard2of6.bin
new file mode 100644
index 0000000..b84cf29
Binary files /dev/null and b/app/model/group1-shard2of6.bin differ
diff --git a/app/model/group1-shard3of6.bin b/app/model/group1-shard3of6.bin
new file mode 100644
index 0000000..58f07d2
Binary files /dev/null and b/app/model/group1-shard3of6.bin differ
diff --git a/app/model/group1-shard4of6.bin b/app/model/group1-shard4of6.bin
new file mode 100644
index 0000000..dec626c
Binary files /dev/null and b/app/model/group1-shard4of6.bin differ
diff --git a/app/model/group1-shard5of6.bin b/app/model/group1-shard5of6.bin
new file mode 100644
index 0000000..9d75f8e
Binary files /dev/null and b/app/model/group1-shard5of6.bin differ
diff --git a/app/model/group1-shard6of6.bin b/app/model/group1-shard6of6.bin
new file mode 100644
index 0000000..573c5ed
Binary files /dev/null and b/app/model/group1-shard6of6.bin differ
diff --git a/app/model/model.json b/app/model/model.json
new file mode 100644
index 0000000..a2d635e
--- /dev/null
+++ b/app/model/model.json
@@ -0,0 +1 @@
+{"format": "graph-model", "generatedBy": "2.1.0", "convertedBy": "TensorFlow.js Converter v1.5.2", "userDefinedMetadata": {"signature": {"inputs": {"input:0": {"name": "input:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "-1"}, {"size": "224"}, {"size": "224"}, {"size": "3"}]}}}, "outputs": {"Identity:0": {"name": "Identity:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "-1"}, {"size": "5"}]}}}}}, "modelTopology": {"node": [{"name": "input", "op": "Placeholder", "attr": {"shape": {"shape": {"dim": [{"size": "-1"}, {"size": "224"}, {"size": "224"}, {"size": "3"}]}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/Conv/Conv2D_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "3"}, {"size": "48"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_14/expand/Conv2D_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1344"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/Conv/Conv2D_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "48"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv/depthwise/depthwise_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "48"}, {"size": "1"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv/depthwise/depthwise_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "48"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv/project/Conv2D_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "48"}, {"size": "24"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv/project/Conv2D_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "24"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_1/expand/Conv2D_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "24"}, {"size": "144"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_14/depthwise/depthwise_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "1344"}, {"size": "1"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_1/expand/Conv2D_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "144"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_1/depthwise/depthwise_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "3"}, {"size": "3"}, {"size": "144"}, {"size": "1"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_1/depthwise/depthwise_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "144"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_1/project/Conv2D_weights", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1"}, {"size": "1"}, {"size": "144"}, {"size": "32"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_14/depthwise/depthwise_bn_offset", "op": "Const", "attr": {"value": {"tensor": {"dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "1344"}]}}}, "dtype": {"type": "DT_FLOAT"}}}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_1/project/Conv2D_bn_offset", "op": "Const", "attr": 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"StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_15/project/Conv2D_bn_offset", "shape": [224], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_8/depthwise/depthwise_weights", "shape": [3, 3, 528, 1], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_8/depthwise/depthwise_bn_offset", "shape": [528], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_8/project/Conv2D_weights", "shape": [1, 1, 528, 88], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_8/project/Conv2D_bn_offset", "shape": [88], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_9/expand/Conv2D_weights", "shape": [1, 1, 88, 528], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_9/expand/Conv2D_bn_offset", "shape": [528], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_16/expand/Conv2D_weights", "shape": [1, 1, 224, 1344], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_9/depthwise/depthwise_weights", "shape": [3, 3, 528, 1], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_9/depthwise/depthwise_bn_offset", "shape": [528], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_9/project/Conv2D_weights", "shape": [1, 1, 528, 88], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_9/project/Conv2D_bn_offset", "shape": [88], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_10/expand/Conv2D_weights", "shape": [1, 1, 88, 528], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_16/expand/Conv2D_bn_offset", "shape": [1344], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_10/expand/Conv2D_bn_offset", "shape": [528], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_10/depthwise/depthwise_weights", "shape": [3, 3, 528, 1], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_10/depthwise/depthwise_bn_offset", "shape": [528], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_10/project/Conv2D_weights", "shape": [1, 1, 528, 136], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_10/project/Conv2D_bn_offset", "shape": [136], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_11/expand/Conv2D_weights", "shape": [1, 1, 136, 816], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_16/depthwise/depthwise_weights", "shape": [3, 3, 1344, 1], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_11/expand/Conv2D_bn_offset", "shape": [816], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_11/depthwise/depthwise_weights", "shape": [3, 3, 816, 1], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_11/depthwise/depthwise_bn_offset", "shape": [816], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_11/project/Conv2D_weights", "shape": [1, 1, 816, 136], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_16/depthwise/depthwise_bn_offset", "shape": [1344], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_11/project/Conv2D_bn_offset", "shape": [136], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/expand/Conv2D_weights", "shape": [1, 1, 136, 816], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/expand/Conv2D_bn_offset", "shape": [816], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/depthwise/depthwise_weights", "shape": [3, 3, 816, 1], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/depthwise/depthwise_bn_offset", "shape": [816], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/project/Conv2D_weights", "shape": [1, 1, 816, 136], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_16/project/Conv2D_weights", "shape": [1, 1, 1344, 448], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_12/project/Conv2D_bn_offset", "shape": [136], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/expand/Conv2D_weights", "shape": [1, 1, 136, 816], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/expand/Conv2D_bn_offset", "shape": [816], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_16/project/Conv2D_bn_offset", "shape": [448], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/depthwise/depthwise_weights", "shape": [3, 3, 816, 1], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/depthwise/depthwise_bn_offset", "shape": [816], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/project/Conv2D_weights", "shape": [1, 1, 816, 224], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_13/project/Conv2D_bn_offset", "shape": [224], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/expanded_conv_14/expand/Conv2D_weights", "shape": [1, 1, 224, 1344], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/Conv_1/Conv2D_weights", "shape": [1, 1, 448, 1792], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/hub_input/Mul/y", "shape": [], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/hub_input/Sub/y", "shape": [], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/Logits/Conv2d_1c_1x1/Conv2D/ReadVariableOp", "shape": [1, 1, 1792, 1001], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/keras_layer/StatefulPartitionedCall/StatefulPartitionedCall/StatefulPartitionedCall/predict/MobilenetV2/Logits/Conv2d_1c_1x1/BiasAdd/ReadVariableOp", "shape": [1001], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/dense/MatMul/ReadVariableOp", "shape": [1001, 5], "dtype": "float32"}, {"name": "StatefulPartitionedCall/sequential/dense/BiasAdd/ReadVariableOp", "shape": [5], "dtype": "float32"}]}]}
\ No newline at end of file
diff --git a/app/router.js b/app/router.js
index 6834a46..6d44c9a 100644
--- a/app/router.js
+++ b/app/router.js
@@ -8,4 +8,5 @@ module.exports = app => {
   // router.get('/', controller.home.index);
 
   require('./router/music')(app);
+  require('./router/nsfw')(app);
 };
diff --git a/app/router/nsfw.js b/app/router/nsfw.js
new file mode 100644
index 0000000..f2c867a
--- /dev/null
+++ b/app/router/nsfw.js
@@ -0,0 +1,8 @@
+'use strict';
+
+module.exports = app => {
+  const { router, controller } = app;
+  // router.get(neteasecloud, controller.music.neteasecloud);
+
+  router.post('/nsfw/images/check', controller.checkImage.check);
+};
diff --git a/app/service/indecentContentCheck.js b/app/service/indecentContentCheck.js
new file mode 100644
index 0000000..1cc3b6a
--- /dev/null
+++ b/app/service/indecentContentCheck.js
@@ -0,0 +1,39 @@
+'use strict';
+
+const Service = require('egg').Service;
+const tf = require('@tensorflow/tfjs-node');
+// const jpeg = require('jpeg-js');
+const { Image } = require('image-js');
+const contentBoundary = require('../util/contentBoundary');
+
+const convert = async img => {
+  // const image = await jpeg.decode(img, true);
+  const image = await Image.load(img.filepath);
+  // console.log('jpeg.decode:', image);
+  const numChannels = 3;
+  const numPixels = image.width * image.height;
+  const values = new Int32Array(numPixels * numChannels);
+
+  for (let i = 0; i < numPixels; i++) {
+    for (let c = 0; c < numChannels; ++c) { values[i * numChannels + c] = image.data[i * 4 + c]; }
+  }
+
+  return tf.tensor3d(values, [ image.height, image.width, numChannels ], 'int32');
+};
+
+
+class ContentCheckService extends Service {
+  async contentCheck(img) {
+    try {
+      const image = await convert(img);
+      const predictions = await this.app.nsfwModel.classify(image);
+      console.log('Predictions: ', predictions);
+      const isSafeContent = contentBoundary(predictions);
+      return isSafeContent;
+    } catch (err) {
+      throw new Error(err);
+    }
+  }
+}
+
+module.exports = ContentCheckService;
diff --git a/app/util/contentBoundary.js b/app/util/contentBoundary.js
new file mode 100644
index 0000000..325ce3e
--- /dev/null
+++ b/app/util/contentBoundary.js
@@ -0,0 +1,18 @@
+'use strict';
+
+const safeContent = [ 'Drawing', 'Neutral' ];
+
+const contentBoundary = predictions => {
+  let safeProbability = 0;
+  for (let index = 0; index < predictions.length; index++) {
+    const item = predictions[index];
+    const className = item.className;
+    const probability = item.probability;
+    if (safeContent.includes(className)) {
+      safeProbability += probability;
+    }
+  }
+  return safeProbability > 0.5;
+};
+
+module.exports = contentBoundary;
diff --git a/config/config.default.js b/config/config.default.js
index 5e5933c..6abdab3 100644
--- a/config/config.default.js
+++ b/config/config.default.js
@@ -23,6 +23,22 @@ module.exports = appInfo => {
     // myAppName: 'egg',
   };
 
+  config.multipart = {
+    mode: 'file',
+    fieldNameSize: 100,
+    fieldSize: '100kb',
+    fields: 9,
+    fileSize: '10mb',
+    files: 9,
+    whitelist: [ '.jpg', '.jpeg', '.png', '.gif', '.webp', '.bmp' ],
+  };
+
+  config.security = {
+    csrf: {
+      enable: false,
+    },
+  };
+
   config.onerror = {
     all(error, ctx) {
       ctx.body = { errcode: 500, desc: error, result: false, data: null };
diff --git a/package.json b/package.json
index bb8b290..b6faef4 100644
--- a/package.json
+++ b/package.json
@@ -8,7 +8,11 @@
   },
   "dependencies": {
     "egg": "^2.15.1",
-    "egg-scripts": "^2.11.0"
+    "egg-scripts": "^2.11.0",
+    "@tensorflow/tfjs": "^2.7.0",
+    "@tensorflow/tfjs-node": "^2.7.0",
+    "image-js": "^0.30.3",
+    "nsfwjs": "^2.3.0"
   },
   "devDependencies": {
     "autod": "^3.0.1",