Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| import streamlit.components.v1 as components | |
| titulo = "T&T2 - Craft demos of private models" | |
| descripcion = "Build public, shareable models of private models." | |
| fecha = "2022-01-27" | |
| thumbnail = "assets/2_thumbnail.png" | |
| def run_article(): | |
| st.markdown(""" | |
| # 🤗 Tips & Tricks Edition 2 | |
| # Using private models in your public ML demos | |
| Welcome to a new post of 🤗 Tips and Tricks! Each post can be read in <5 minutes and shares features you might not know about that will allow you | |
| to leverage the Hub platform to its full extent. | |
| In today's post, you'll learn how you can create public demos of your private models. This can be useful if you're not ready to share the model | |
| or are worried of ethical concerns, but would still like to share the work with the community to try it out. | |
| **Is this expensive?** | |
| It will cost...nothing! You can host private models on Hugging Face right now in a couple of clicks! Note: This works not just for transformers, | |
| but for any ML library! | |
| **Which is the model?** | |
| The super secret model we won't want to share publicly but still showcase is a super powerful Scikit-learn model for...wine quality classification! | |
| For the purposes of this demo, assume there exists a private model repository with `id=osanseviero/wine-quality`. | |
| """) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown(""" | |
| **🍷Cheers. And what is the demo?** | |
| Let's build it right now! You first need to create a [new Space](https://huggingface.co/new-space). I like to use the Gradio SDK, but you are | |
| also encouraged to try Streamlit and static Spaces. | |
| The second step is to create a [read token](https://huggingface.co/settings/token). A read token allows reading repositories, which is useful when | |
| you don't need to modify them. This token will allow the Space to access the model from the private model repository. | |
| The third step is to create a secret in the Space, which you can do in the settings tab. | |
| **What's a secret?** | |
| If you hardcode your token, other people will be able to access your repository, which is what you're trying to avoid. Remember that the Space is | |
| public so the code of the Space is also public! By using secrets + tokens, you are having a way in which the Space can read a private model repo | |
| without exposing the raw model nor the token. Secrets can be very useful as well if you are making calls to APIs and don't want to expose it. | |
| So you can add a token, with any name you want, and paste the value you should have coppied from your settings. | |
| """) | |
| with col2: | |
| st.image("https://github.com/osanseviero/hf-tips-and-tricks/raw/main/assets/2_gradio_space.png", width=300) | |
| st.image("https://github.com/osanseviero/hf-tips-and-tricks/raw/main/assets/2_token.png", width=300) | |
| st.image("https://github.com/osanseviero/hf-tips-and-tricks/raw/main/assets/2_secret.png", width=300) | |
| st.markdown(""" | |
| **🤯 That's neat! What happens next?** | |
| The secret is made available available to the gradio Space as a. environment variable. Let's write the code for the Gradio demo. | |
| The first step is adding the `requirements.txt` files with used dependencies. | |
| ``` | |
| scikit-learn | |
| joblib | |
| ``` | |
| As always in Spaces, you create a file called `app.py`. Let's go through each section of the file | |
| 1. Imports...nothing special | |
| ```python | |
| import joblib | |
| import os | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| ``` | |
| 2. Downloading model from private repo | |
| You can use `hf_hub_download` from the `huggingface_hub` library to download (and cache) a file from a model repository. Using the | |
| `use_auth_token` param, you can access the secret `TOKEN`, which has the read token you created before. I want to download the file | |
| `sklearn_model.joblib`, which is how `sklearn` encourages to save the models. | |
| ```python | |
| file_path = hf_hub_download("osanseviero/wine-quality", "sklearn_model.joblib", | |
| use_auth_token=os.environ['TOKEN']) | |
| ``` | |
| 3. Loading model | |
| The path right now points to the cached local joblib model. You can easily load it now: | |
| ```python | |
| model = joblib.load(file_path) | |
| ``` | |
| 4. Inference function | |
| One of the most important concepts in Gradio is the inference function. The inference function receives an input and has an output. It can | |
| receive multiple types of inputs (images, videos, audios, text, etc) and multiple outputs. This is a simple sklearn inference | |
| ```python | |
| def predict(data): | |
| return model.predict(data.to_numpy()) | |
| ``` | |
| 5. Build and launch the interface | |
| Building Gradio interfaces is very simple. You need to specify the prediction function, the type of input and output. You can add more things such | |
| as the title and descriptions. In this case, the input is a dataframe since that's the kind of data managed by this model. | |
| ``` | |
| iface = gr.Interface( | |
| predict, | |
| title="Wine Quality predictor with SKLearn", | |
| inputs=gr.inputs.Dataframe( | |
| headers=headers, | |
| default=default, | |
| ), | |
| outputs="numpy", | |
| ) | |
| iface.launch() | |
| ``` | |
| We're done!!!! | |
| You can find the Space at [https://huggingface.co/spaces/osanseviero/wine_quality](https://huggingface.co/spaces/osanseviero/wine_quality) | |
| and try it yourself! It's not great, but the main idea of the article was to showcase a workflow of public demo with private model. This can also | |
| work for datasets! With Gradio, you can create datasets with flagged content from users! | |
| **Wait wait wait! I don't want to click more links!** | |
| Ahm...ok. The link above is cool because you can share it with anyone, but you can also show Spaces-hosted Gradio demos with a couple of | |
| HTML lines in your own website. Here you can see the Gradio Space. | |
| """) | |
| embed_gradio = components.html( | |
| """ | |
| <head> | |
| <link rel="stylesheet" href="https://gradio.s3-us-west-2.amazonaws.com/2.6.2/static/bundle.css"> | |
| </head> | |
| <body> | |
| <div id="target"></div> | |
| <script src="https://gradio.s3-us-west-2.amazonaws.com/2.6.2/static/bundle.js"></script> | |
| <script> | |
| launchGradioFromSpaces("osanseviero/wine_quality", "#target") | |
| </script> | |
| </body> | |
| """, | |
| height=600, | |
| ) | |
| st.markdown(""" | |
| **🤯 Is that...a Gradio Space embedded within a Streamlit Space about creating Spaces?** | |
| Yes, that's right! I hope this was useful! Until the next time! | |
| **A Hacker Llama 🦙** | |
| [osanseviero](https://twitter.com/osanseviero) | |
| """) | |