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Removing LGTM #58

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25 changes: 12 additions & 13 deletions README.md
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Expand Up @@ -2,14 +2,13 @@
[![CircleCI](https://circleci.com/gh/RedisAI/redisai-js/tree/master.svg?style=svg)](https://circleci.com/gh/RedisAI/redisai-js/tree/master)
[![npm version](https://badge.fury.io/js/redisai-js.svg)](https://badge.fury.io/js/redisai-js)
[![codecov](https://codecov.io/gh/RedisAI/redisai-js/branch/master/graph/badge.svg)](https://codecov.io/gh/RedisAI/redisai-js)
[![Language grade: JavaScript](https://img.shields.io/lgtm/grade/javascript/g/RedisAI/redisai-js.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/RedisAI/redisai-js/context:javascript)
[![Known Vulnerabilities](https://snyk.io/test/github/RedisAI/redisai-js/badge.svg?targetFile=package.json)](https://snyk.io/test/github/RedisAI/redisai-js?targetFile=package.json)

## A high performance node.js RedisAI client
[![Forum](https://img.shields.io/badge/Forum-RedisAI-blue)](https://forum.redislabs.com/c/modules/redisai)
[![Discord](https://img.shields.io/discord/697882427875393627?style=flat-square)](https://discord.gg/rTQm7UZ)

You can use this client to query RedisAI using plain Javascript, or in a type-safe manner using Typescript, since redisai-js comes with its own type definitions built-in.
You can use this client to query RedisAI using plain Javascript, or in a type-safe manner using Typescript, since redisai-js comes with its own type definitions built-in.

## Installation

Expand Down Expand Up @@ -127,11 +126,11 @@ Example of AI.SCRIPTSET and AI.SCRIPTRUN

Example of AI.DAGRUN enqueuing multiple SCRIPTRUN and MODELRUN commands

A common pattern is enqueuing multiple SCRIPTRUN and MODELRUN commands within a DAG. The following example uses ResNet-50,to classify images into 1000 object categories.
A common pattern is enqueuing multiple SCRIPTRUN and MODELRUN commands within a DAG. The following example uses ResNet-50,to classify images into 1000 object categories.

Given that our input tensor contains each color represented as a 8-bit integer and that neural networks usually work with floating-point tensors as their input we need to cast a tensor to floating-point and normalize the values of the pixels - for that we will use `pre_process_4ch` function.
Given that our input tensor contains each color represented as a 8-bit integer and that neural networks usually work with floating-point tensors as their input we need to cast a tensor to floating-point and normalize the values of the pixels - for that we will use `pre_process_4ch` function.

To optimize the classification process we can use a post process script to return only the category position with the maximum classification - for that we will use `post_process` script.
To optimize the classification process we can use a post process script to return only the category position with the maximum classification - for that we will use `post_process` script.

Using the DAG capabilities we've removed the necessity of storing the intermediate tensors in the keyspace. You can even run the entire process without storing the output tensor, as follows:

Expand All @@ -157,7 +156,7 @@ var Jimp = require('jimp');
const modelBlob = fs.readFileSync('./tests/test_data/imagenet/resnet50.pb');
const imagenetModel = new redisai.Model(redisai.Backend.TF, 'CPU', ['images'], ['output'], modelBlob);
const resultModelSet = await aiclient.modelset('imagenet_model', imagenetModel);

// AI.MODELSET result: OK
console.log(`AI.MODELSET result: ${resultModelSet}`)

Expand All @@ -166,11 +165,11 @@ var Jimp = require('jimp');
const imageHeight = 224;
const image = inputImage.cover(imageWidth, imageHeight);
const tensor = new redisai.Tensor(redisai.Dtype.uint8, [imageWidth, imageHeight, 4], Buffer.from(image.bitmap.data));

///
// Prepare the DAG enqueuing multiple SCRIPTRUN and MODELRUN commands
const dag = new redisai.Dag();

dag.tensorset('tensor-image', tensor);
dag.scriptrun('data_processing_script', 'pre_process_4ch', ['tensor-image'], ['temp_key1']);
dag.modelrun('imagenet_model', ['temp_key1'], ['temp_key2']);
Expand All @@ -179,10 +178,10 @@ var Jimp = require('jimp');

// Send the AI.DAGRUN command to RedisAI server
const resultDagRun = await aiclient.dagrun_ro(null, dag);
// The 5th element of the reply will be the `classification` tensor

// The 5th element of the reply will be the `classification` tensor
const classTensor = resultDagRun[4];

// Print the category in the position with the max classification
const idx = classTensor.data[0];

Expand All @@ -196,7 +195,7 @@ var Jimp = require('jimp');
### Further examples

The [RedisAI examples repo](https://github.com/RedisAI/redisai-examples) shows more advanced examples
made using redisai-js under [js_client](https://github.com/RedisAI/redisai-examples/tree/master/js_client) folder.
made using redisai-js under [js_client](https://github.com/RedisAI/redisai-examples/tree/master/js_client) folder.


### Supported RedisAI Commands
Expand All @@ -214,7 +213,7 @@ AI.SCRIPTSET | scriptset
AI.SCRIPTGET | scriptget
AI.SCRIPTDEL | scriptdel
AI.SCRIPTRUN | scriptrun
AI._SCRIPTSCAN | N/A
AI._SCRIPTSCAN | N/A
AI.DAGRUN | dagrun
AI.DAGRUN_RO | dagrun_ro
AI.INFO | info and infoResetStat (for resetting stats)
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