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christinapeng1 merged 1 commit into from
Apr 21, 2025
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Beautify Expression Measurement Example Project READMEs #165

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12 changes: 7 additions & 5 deletions expression-measurement/batch/next-js-emotional-language/README.md
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# Next.js Emotional Language Example

| API | Models | Framework |
| ------- | ---------- | -------------------- |
| `batch` | `language` | Next.js (TypeScript) |
<div align="center">
<img src="https://storage.googleapis.com/hume-public-logos/hume/hume-banner.png">
<h1>Expression Measurement | Next.js Emotional Language Example</h1>
<p>
<strong>Batch-analyze Language to Uncover Nuanced Human Expression.</strong>
</p>
</div>

[![Open in StackBlitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/fork/github/HumeAI/hume-api-examples/tree/main/typescript-next-api-language)

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4 changes: 2 additions & 2 deletions expression-measurement/batch/python-top-emotions/README.md
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<div align="center">
<img src="https://storage.googleapis.com/hume-public-logos/hume/hume-banner.png">
<h1>Batch Expression Measurement | Sample Python Implementation</h1>
<h1>Expression Measurement | Sample Python Implementation</h1>
<p>
<strong>Jumpstart your development with Hume's Expression Measurement!</strong>
<strong>Batch-analyze Facial Expressions using Hume's Python SDK.</strong>
</p>
</div>

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39 changes: 29 additions & 10 deletions expression-measurement/batch/typescript-raw-text-processor/README.md
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# Hume Raw Text Processor
<div align="center">
<img src="https://storage.googleapis.com/hume-public-logos/hume/hume-banner.png">
<h1>Expression Measurement | TypeScript Raw Text Processor</h1>
<p>
<strong>Batch-analyze Text using Hume's TypeScript SDK.</strong>
</p>
</div>

## Summary
## Overview

This project serves as an example implementation of our Expression Measurement (REST) API processing raw text using our [Typescript SDK](https://www.npmjs.com/package/hume).

## Running Locally

1. Clone the repo with `git clone origin https://github.com/HumeAI/hume-api-examples.git`
1. Clone this examples repository:

2. Navigate to project directory `cd typescript-raw-text-processor`
```shell
git clone https://github.com/humeai/hume-api-examples
cd hume-api-examples/expression-measurement/batch/typescript-raw-text-processor
```

3. Install dependencies with `npm i`
2. Install dependencies:

4. Setup environment variables
```shell
npm install
```

a. Create `.env` file based off of `.env.example`
3. Set up your API key:

b. Copy/paste your API key in from the [Portal](https://beta.hume.ai/settings/keys).
You must authenticate to use the Hume Expression Measurement API. Your API key can be retrieved from the [Hume AI platform](https://platform.hume.ai/settings/keys). For detailed instructions, see our documentation on [getting your api keys](https://dev.hume.ai/docs/introduction/api-key).

5. Set configurations within the `src/index.ts` file.
This example uses [dotenv](https://www.npmjs.com/package/dotenv). Place your API key in a `.env` file at the root of your project.

```shell
echo "HUME_API_KEY='<YOUR API KEY>'" > .env
```

You can copy the `.env.example` file to use as a template.

4. Set configurations within the `src/index.ts` file.

a. Specify which language.

b. Copy and paste the text to be processed.

c. Set Language Model configurations.

6. Run `npm run start` to process the specified text with the specified configurations and log predictions to the console.
5. Run `npm run start` to process the specified text with the specified configurations and log predictions to the console.
12 changes: 9 additions & 3 deletions expression-measurement/streaming/next-js-streaming-example/README.md
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# Sandbox

## About
<div align="center">
<img src="https://storage.googleapis.com/hume-public-logos/hume/hume-banner.png">
<h1>Expression Measurement | Next.js Streaming Example</h1>
<p>
<strong>Real-time Streaming of Facial, Vocal, and Language Expressions.</strong>
</p>
</div>

## Overview

This repo contains a NextJS app for demoing and testing Hume APIs. It implements several streaming interfaces that provide an intuitive validation of model quality.

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