Hugging Face + Langchain in 5 mins | Access 200k+ FREE AI models for your AI apps
TLDRThis video tutorial introduces Hugging Face, a leading AI company with over 200,000 AI models accessible for free. It showcases how to utilize Hugging Face's platform, which includes models, datasets, and spaces, to build AI applications. The tutorial demonstrates creating an AI app that converts images to audio stories using Hugging Face models and Langchain, guiding viewers through the process step-by-step, from image to text, generating a story, and finally converting text to speech.
Takeaways
- 🌟 Hugging Face is a top AI company with a valuation over 2 billion dollars, offering over 200,000 AI models for various applications.
- 🔍 The platform is utilized by tech giants like Google, Amazon, Microsoft, and Meta, highlighting its industry significance.
- 📚 Hugging Face's platform is divided into three main components: Models, Datasets, and Spaces, each serving a distinct purpose in AI development.
- 🖼️ Models section allows users to discover and test AI models directly through the platform, streamlining the process of finding and implementing the right model.
- 🔗 The platform provides an easy way to deploy models on various servers and offers a free API on Hugging Face Hub, though it comes with rate limits.
- 💾 For local deployment, Hugging Face's Transformers library is recommended, which facilitates the use of models without the need for hosting them personally.
- 📊 Datasets section is a rich resource for training your own AI models, offering a wide array of data sets across different categories.
- 🎨 Spaces is designed for showcasing and sharing AI apps, allowing creators to deploy their apps easily and for others to explore and learn from them.
- 🛠️ The tutorial demonstrates a step-by-step guide on creating an AI app using Hugging Face models, including image to text, language model generation, and text to speech.
- 📝 The process involves creating a .env file to store credentials, utilizing pipelines for model implementation, and employing the Hugging Face API for model functionalities.
- 🎓 The tutorial also touches on the use of low-code AI platforms like Relevance AI, which can simplify the process of building AI applications by providing pre-built models and interfaces.
Q & A
What is the value of Hugging Face as an AI company?
-Hugging Face is valued at more than 2 billion dollars and is one of the top AI companies. It is used by major tech companies like Google, Amazon, Microsoft, and Meta.
How many followers does Hugging Face have on GitHub?
-Hugging Face has more than 16,000 followers on GitHub.
What kind of AI models can be found on Hugging Face platform?
-Hugging Face offers over 200,000 different types of AI models, including image to text, text to speech, text to image, and many more.
What are the three main components of the Hugging Face platform?
-The three main components of the Hugging Face platform are Models, Datasets, and Spaces.
How can users test AI models on Hugging Face without downloading them?
-Users can test AI models directly on Hugging Face's hosted version by dragging and dropping an image or inputting text, which allows immediate testing without the need for local hosting.
What is the purpose of the 'Spaces' feature on Hugging Face?
-Spaces is designed for users to showcase and share the AI apps they build. It allows easy deployment of apps and exploration of other AI apps built by the community.
How can developers use Hugging Face models in their AI apps?
-Developers can use Hugging Face models by accessing the hosted version via the Inference API, downloading models locally using the Transformers library, or deploying models through Spaces.
What is the process to create an access token for using Hugging Face models?
-To create an access token for Hugging Face, users need to sign up for a Hugging Face account, go to settings, access tokens, and then create a new token.
How can the image-to-text model be tested using Hugging Face's platform?
-The image-to-text model can be tested by selecting the model category, choosing a popular model, and then using the drag-and-drop feature to upload an image and see the results.
What is the role of the 'pipeline' in using Hugging Face models?
-The 'pipeline' in Hugging Face allows users to download AI models into their local machine, making it easier to integrate these models into their applications.
How does the tutorial demonstrate the creation of an AI app using Hugging Face models?
-The tutorial demonstrates creating an AI app by using an image-to-text model to understand a photo, a large language model to generate a story, and a text-to-speech model to produce an audio story.
Outlines
🤖 Introduction to Hugging Face for AI App Development
This paragraph introduces Hugging Face as a crucial platform for AI app developers, valued at over 2 billion dollars with a significant presence on GitHub. It highlights Hugging Face's collaboration with tech giants like Google, Amazon, Microsoft, and Meta, and its extensive library of over 200,000 AI models. The speaker emphasizes the importance of learning to use Hugging Face for AI app development and provides an overview of the platform's three main components: models, datasets, and spaces. The paragraph explains how models can be explored and tested directly on the platform, the datasets section for training models, and spaces for showcasing AI apps. It also sets the stage for a step-by-step tutorial on building an AI app using Hugging Face models.
🚀 Building an AI App with Hugging Face Models
The second paragraph delves into the process of building an AI app using Hugging Face models. It outlines a three-component app: an image-to-text model to understand scenarios from photos, a large language model to generate stories, and a text-to-speech model to produce audio stories. The speaker guides through selecting and using the 'blip' image-to-text model, creating an access token, and setting up the local environment. It also covers using the Hugging Face API and Transformers library to implement the image-to-text functionality. The paragraph further discusses generating stories using a language model, preferably GPT, and converting these stories into audio using a text-to-speech model. Finally, it touches on integrating these components with a user interface using Streamlit and provides a brief demonstration of the app's functionality, concluding with a recommendation to explore more about Hugging Face models and tasks on their official website.
Mindmap
Keywords
💡Hugging Face
💡AI models
💡GitHub
💡Image to text
💡Text to speech
💡Transformers library
💡API token
💡Datasets
💡Space
💡Streamlit
💡Inference API
Highlights
Hugging Face is a top AI company valued over 2 billion dollars.
Hugging Face's product is used by major tech companies like Google, Amazon, Microsoft, and Meta.
The platform offers over 200,000 different types of AI models.
Models include image to text, text to speech, text to image, and more.
Hugging Face allows users to discover and share AI models.
The platform is divided into three parts: models, datasets, and spaces.
Models section lets users find and test AI models directly on the hosted version.
Users can deploy models on different servers or use the free hosted API on Hugging Face Hub.
Datasets section provides data for training custom AI models.
Spaces is designed for showcasing and sharing AI apps built by users.
Tutorial demonstrates building an AI app using Hugging Face and Langchain.
App components include image to text, language model for story generation, and text to speech.
Hugging Face account and access token are required to use the platform's features.
The Transformers library is used to download and run models locally.
Image to text model 'blip' is used to understand scenarios from photos.
Langchain is used to generate a short story based on the image scenario.
Text to speech model is used to convert the story into an audio file.
Inference API allows using Hugging Face's hosted version directly.
Hugging Face's website provides resources to learn about different tasks and models.
Relevance AI is a low-code AI platform that offers image to speech models out of the box.
The tutorial concludes with a demonstration of the complete AI app in action.