Interfaces for your ML Models
Gradio allows you to quickly create customizable UI components around your TensorFlow or PyTorch models, or even arbitrary Python functions. Mix and match components to support any combination of inputs and outputs. Our core library is free and open-source!
Below are a few demos. Check the Getting Started for full code examples.
import gradio as gr
def recognize_digit(img):
# ... implement digit recognition model on input array
# ... return dictionary of labels and confidences
gr.Interface(fn=recognize_digit, inputs="sketchpad", outputs="label").launch()import gradio as gr
def answer_question(paragraph, question):
# ... implement Q&A model
# ... return answer to question
gr.Interface(fn=answer_question, inputs=["textbox", "text"], outputs="text").launch()import gradio as gr
def face_segmentation(img):
# ... implement face segmentation model on input 200x200 numpy array
# ... return segmentation mask as numpy array
webcam = gr.inputs.Image(shape=(200, 200), source="webcam")
gr.Interface(fn=face_segmentation, inputs=webcam, outputs="image").launch()import gradio as gr, matplotlib.pyplot as plt
def outbreak_forecast(r, month, countries, social_distancing):
# ... run model to forecast outbreak and generate plots
# ... return plt
r = gr.inputs.Slider(1, 5)
month = gr.inputs.Dropdown(["May", "June", "July"])
countries = gr.inputs.CheckboxGroup(["USA", "Canada", "Mexico", "UK"])
gr.Interface(fn=outbreak_forecast,
inputs=[r, month, countries, "checkbox"], outputs="plot").launch()Fast, easy setup
Gradio can be installed directly through pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function.
More on Getting Started >>
Present and share
Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that lets them interact with the model on your computer remotely from their own devices.
More on Sharing >>
Permanent hosting
Once you've created an interface, you can point Gradio towards the GitHub repository where it is contained. Gradio will host the interface on its servers and provide you with a link you can share.
More on Hosting >>
Dear
#MachineLearning twitter,
If you haven't typed:
$ πππ πππππππ ππππππ
yet, now would be a damn good time.
Especially if you are working in computer vision & deploying models in the real world. twitter.com/abidlabs/statu...
In other news, proud to share what the
@GradioML team has been working on for the past few weeks! Just released 1.3.0, which has 3 big updates:1. INTERPRETATIONS: You can get out-of-the-box interpretations for any regression / classification model in 1 line of code:
Having
@GradioML deploy your model is like a test-of-time award, except itβs test-of-now. Open implementation, clear code, and actionable dataset has to skew towards better papers. twitter.com/GradioML/statu...We took 5 of our favorite
#NeurIPS2020 papers and created web demos from their public code using@GradioML. So that anyone can use the models, right from your browser!#accessibility#reproducibility
The models & links to the web demos are below:
Pretty neat that
@GradioML!
+ Generate an easy-to-use UI for your#ML model, function, or#API with only a few lines of code!
+ Integrate directly into your@ProjectJupyter notebook
+ or share a link with anyone
h/t@VincentTerrasi#MachineLearning
www.gradio.app
Check out this cool python library!
@GradioML lets you easily create web-based interfaces for your ML models twitter.com/abidlabs/statu...I recorded a quick tutorial covering all of the core features of the
@GradioML Python library
- creating GUIs to demo ML models
- deploying via share links
- visualizing interpretations
- seeing embeddings
What other features would you like to see?
www.youtube.com/watch?v=O_3FINRtwhs
Contact Us.
Have questions, or want to integrate Gradio in a large team? Get in touch.
Running into any technical issues? Open an issue at our github repo.