Hugging Face + Langchain in 5 mins Access 200k+ FREE AI models for your AI apps
Hugging Face + Langchain in 5 mins | Access 200k+ FREE AI models for your AI apps
If you're building AI apps, it's essential to know how to use Hugging Face. It is one of the top AI companies valued at over 2 billion dollars and has gained a significant following on GitHub with over 16,000 followers. Hugging Face's product is used by major tech companies such as Google, Amazon, Microsoft, and Meta. With over 200,000 different types of AI models, including image-to-text, text-to-speech, and much more, learning how to use Hugging Face is a must for any AI app developer. In this blog post, I will show you how you can use the Hugging Face platform and integrate it with other public libraries like Langchain. Let's get started!
Discovering Models, Datasets, and Space on Hugging Face
Hugging Face is a platform where you can discover and share AI models. The platform is divided into three main parts: models, datasets, and space.
Models
In the models section, you can find a wide variety of models for different tasks. For example, if you're interested in image-to-text models, you can select the image-to-text category on the left side and choose any popular model on the right side. What makes Hugging Face so useful is that you can not only preview and test the AI models directly on their hosted version but also easily deploy them on different servers. If you prefer to run the models locally on your machine, you can use Hugging Face's Transformers library.
Datasets
In the datasets section, you can find a wide range of datasets that you can use to train your own models. While you might not use these datasets extensively unless you're training your own model, they are still valuable resources for data scientists and AI researchers.
Space
The space section of Hugging Face is designed for users to showcase and share the AI apps they have built. You can deploy your own apps easily on the Hugging Face platform. Additionally, you can explore and play with the AI apps built by others, gaining insights into different use cases and models used.
Implementing Langchain with Hugging Face: Step-by-Step Example
In this section, I will take you through a step-by-step example of implementing an AI app with Hugging Face and Langchain. The app will allow users to upload an image, which will then be automatically transformed into an audio story. Let's break down the implementation into three components:
- Image-to-Text Model: To understand the scenario based on the photo
- Language Model: To generate a short story based on the scenario
- Text-to-Speech Model: To generate the audio story
To find the right image-to-text model, you can go to the Hugging Face platform. Filter down to image-to-text models and choose the one that suits your needs. In this example, I will be using the model called "Blip".
To integrate Langchain with Hugging Face, you'll need to create a Hugging Face account and generate an access token for Langchain. Once you have the access token, you can import the necessary libraries and set up the credentials. Then, you can create a pipeline to load the image-to-text model, pass the URL of the image file to the model, and retrieve the text description of the image.
Next, we'll use a large language model (GPT) to generate a short story based on the scenario obtained from the image. You can use an open-source model from Hugging Face or choose another pre-trained model. In this example, I'll be using GPT. After importing the necessary libraries from Langchain, you can define a function to generate a story using the language model.
Finally, we'll use a text-to-speech model to convert the generated story into an audio file. You can find a suitable text-to-speech model on the Hugging Face models page. Alternatively, you can use the Hugging Face API to deploy the model and generate the audio file. In this example, I'll demonstrate how to use the API. Import the necessary libraries and create a function to convert the generated text into speech using the Hugging Face API.
Now, we have all the components ready. We'll connect everything together and give the app a user interface using Streamlit. Import Streamlit library and create a main function to handle the app's functionality. The app will allow users to upload an image file, and then it will generate the scenario, story, and audio file based on the uploaded image. Display the results on the app's interface.
Conclusion
Using Hugging Face and Langchain, you can easily build AI apps that perform tasks like transforming images into audio stories. Hugging Face provides access to a wide range of AI models and datasets, making it a valuable resource for AI developers. Langchain, with its integration with Hugging Face and user-friendly UI, simplifies the process of building AI apps. Whether you use Hugging Face's hosted version or download models using the Transformers library, you'll find the flexibility to create powerful AI applications. If you're interested in more AI experiments and tutorials, subscribe to our channel and continue sharing your thoughts and feedback. Happy coding!
Frequently Asked Questions
1. Can I use Hugging Face models without creating an account?
No, you need to create a Hugging Face account to access and use the models and other features of the platform.
2. Are all the AI models on Hugging Face free to use?
Yes, Hugging Face offers free access to all the AI models on their platform. However, keep in mind that certain models may have usage restrictions or limitations.
3. Can I use Hugging Face models for commercial applications?
Yes, you can use Hugging Face models for commercial applications. However, it is recommended to check the licensing terms or any specific requirements associated with the model you want to use.
4. Are there any limitations or performance considerations when using Hugging Face's hosted models?
Yes, Hugging Face's hosted models might have rate limits or performance considerations based on server availability and usage. If you require faster and more reliable processing, you can consider running the models locally using the Transformers library.
5. Can I contribute my own AI models to the Hugging Face platform?
Yes, Hugging Face welcomes contributions from the AI community. You can share your own AI models, datasets, or even showcase your AI apps on the Hugging Face platform. Visit their website for more information on how to contribute.
