text stringlengths 0 2k | heading1 stringlengths 3 79 | source_page_url stringclasses 190
values | source_page_title stringclasses 190
values |
|---|---|---|---|
<iframe width="560" height="315" src="https://www.youtube.com/embed/KZnpH7msPq0?start=176" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>
[![Open In Colab](https://colab.research.google.c... | 4. Logging Model Inferences to Comet | https://gradio.app/guides/Gradio-and-Comet | Other Tutorials - Gradio And Comet Guide |
"""Returns an APIExperiment object that is thread safe
and can be used to log inferences to a single Experiment
"""
try:
api = comet_ml.API()
workspace = api.get_default_workspace()
project_name = comet_ml.config.get_config()["comet.project_name"]
experiment = comet_ml.API... | 4. Logging Model Inferences to Comet | https://gradio.app/guides/Gradio-and-Comet | Other Tutorials - Gradio And Comet Guide |
887c-065aca14dd30.mp4"></source>
</video>
| 4. Logging Model Inferences to Comet | https://gradio.app/guides/Gradio-and-Comet | Other Tutorials - Gradio And Comet Guide |
We hope you found this guide useful and that it provides some inspiration to help you build awesome model evaluation workflows with Comet and Gradio.
| Conclusion | https://gradio.app/guides/Gradio-and-Comet | Other Tutorials - Gradio And Comet Guide |
- Create an account on Hugging Face [here](https://huggingface.co/join).
- Add Gradio Demo under your username, see this [course](https://huggingface.co/course/chapter9/4?fw=pt) for setting up Gradio Demo on Hugging Face.
- Request to join the Comet organization [here](https://huggingface.co/Comet).
| How to contribute Gradio demos on HF spaces on the Comet organization | https://gradio.app/guides/Gradio-and-Comet | Other Tutorials - Gradio And Comet Guide |
- [Comet Documentation](https://www.comet.com/docs/v2/?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs)
| Additional Resources | https://gradio.app/guides/Gradio-and-Comet | Other Tutorials - Gradio And Comet Guide |
By default, every Gradio demo includes a built-in queuing system that scales to thousands of requests. When a user of your app submits a request (i.e. submits an input to your function), Gradio adds the request to the queue, and requests are processed in order, generally speaking (this is not exactly true, as discussed... | Overview of Gradio's Queueing System | https://gradio.app/guides/setting-up-a-demo-for-maximum-performance | Other Tutorials - Setting Up A Demo For Maximum Performance Guide |
-single-worker** model by default. This means that each worker thread is only assigned a single function from among all of the functions that could be part of your Gradio app. This ensures that you do not see, for example, out-of-memory errors, due to multiple workers calling a machine learning model at the same time. ... | Overview of Gradio's Queueing System | https://gradio.app/guides/setting-up-a-demo-for-maximum-performance | Other Tutorials - Setting Up A Demo For Maximum Performance Guide |
sts are processed in parallel, each request will consume memory to store the data and weights for processing. This means that you might get out-of-memory errors if you increase the `default_concurrency_limit` too high. You may also start to get diminishing returns if the `default_concurrency_limit` is too high because ... | Overview of Gradio's Queueing System | https://gradio.app/guides/setting-up-a-demo-for-maximum-performance | Other Tutorials - Setting Up A Demo For Maximum Performance Guide |
is [guide](https://fastapi.tiangolo.com/async/) is a good primer on the concept.
The `max_size` parameter in `queue()`
A more blunt way to reduce the wait times is simply to prevent too many people from joining the queue in the first place. You can set the maximum number of requests that the queue processes using th... | Overview of Gradio's Queueing System | https://gradio.app/guides/setting-up-a-demo-for-maximum-performance | Other Tutorials - Setting Up A Demo For Maximum Performance Guide |
e passed into `gr.Interface()` or to an event in Blocks such as `.click()`.
While setting a batch is conceptually similar to having workers process requests in parallel, it is often _faster_ than setting `default_concurrency_limit` for deep learning models. The downside is that you might need to adapt your function a ... | Overview of Gradio's Queueing System | https://gradio.app/guides/setting-up-a-demo-for-maximum-performance | Other Tutorials - Setting Up A Demo For Maximum Performance Guide |
If you have done everything above, and your demo is still not fast enough, you can upgrade the hardware that your model is running on. Changing the model from running on CPUs to running on GPUs will usually provide a 10x-50x increase in inference time for deep learning models.
It is particularly straightforward to upg... | Upgrading your Hardware (GPUs, TPUs, etc.) | https://gradio.app/guides/setting-up-a-demo-for-maximum-performance | Other Tutorials - Setting Up A Demo For Maximum Performance Guide |
Congratulations! You know how to set up a Gradio demo for maximum performance. Good luck on your next viral demo!
| Conclusion | https://gradio.app/guides/setting-up-a-demo-for-maximum-performance | Other Tutorials - Setting Up A Demo For Maximum Performance Guide |
In this Guide, we'll walk you through:
- Introduction of Gradio, and Hugging Face Spaces, and Wandb
- How to setup a Gradio demo using the Wandb integration for JoJoGAN
- How to contribute your own Gradio demos after tracking your experiments on wandb to the Wandb organization on Hugging Face
| Introduction | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
Weights and Biases (W&B) allows data scientists and machine learning scientists to track their machine learning experiments at every stage, from training to production. Any metric can be aggregated over samples and shown in panels in a customizable and searchable dashboard, like below:
<img alt="Screen Shot 2022-08-01... | What is Wandb? | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
Gradio
Gradio lets users demo their machine learning models as a web app, all in a few lines of Python. Gradio wraps any Python function (such as a machine learning model's inference function) into a user interface and the demos can be launched inside jupyter notebooks, colab notebooks, as well as embedded in your own... | What are Hugging Face Spaces & Gradio? | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
Now, let's walk you through how to do this on your own. We'll make the assumption that you're new to W&B and Gradio for the purposes of this tutorial.
Let's get started!
1. Create a W&B account
Follow [these quick instructions](https://app.wandb.ai/login) to create your free account if you don’t have one already.... | Setting up a Gradio Demo for JoJoGAN | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
: storage)
discriminator.load_state_dict(ckpt["d"], strict=False)
reset generator
del generator
generator = deepcopy(original_generator)
g_optim = optim.Adam(generator.parameters(), lr=2e-3, betas=(0, 0.99))
Which layers to swap for generating a family of plausible real images -> fake image
if p... | Setting up a Gradio Demo for JoJoGAN | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
ave, Download, and Load Model
Here's how to save and download your model.
```python
from PIL import Image
import torch
torch.backends.cudnn.benchmark = True
from torchvision import transforms, utils
from util import *
import math
import random
import numpy as np
from torch import nn,... | Setting up a Gradio Demo for JoJoGAN | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
.5, 0.5)),
]
)
def inference(img):
img.save('out.jpg')
aligned_face = align_face('out.jpg')
my_w = e4e_projection(aligned_face, "out.pt", device).unsqueeze(0)
with torch.no_grad():
my_sample = generator(my_w, input_is_latent=True)
npimage = my_sampl... | Setting up a Gradio Demo for JoJoGAN | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
``python
import gradio as gr
def wandb_report(url):
iframe = f'<iframe src={url} style="border:none;height:1024px;width:100%">'
return gr.HTML(iframe)
with gr.Blocks() as demo:
report_url = 'https://wandb.ai/_scott/pytorch-sweeps-demo/reports/loss-22-10-07-16-00-17---VmlldzoyNzU2Nz... | Setting up a Gradio Demo for JoJoGAN | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
We hope you enjoyed this brief demo of embedding a Gradio demo to a W&B report! Thanks for making it to the end. To recap:
- Only one single reference image is needed for fine-tuning JoJoGAN which usually takes about 1 minute on a GPU in colab. After training, style can be applied to any input image. Read more in the ... | Conclusion | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
- Create an account on Hugging Face [here](https://huggingface.co/join).
- Add Gradio Demo under your username, see this [course](https://huggingface.co/course/chapter9/4?fw=pt) for setting up Gradio Demo on Hugging Face.
- Request to join wandb organization [here](https://huggingface.co/wandb).
- Once approved transfe... | How to contribute Gradio demos on HF spaces on the Wandb organization | https://gradio.app/guides/Gradio-and-Wandb-Integration | Other Tutorials - Gradio And Wandb Integration Guide |
App-level parameters have been moved from `Blocks` to `launch()`
The `gr.Blocks` class constructor previously contained several parameters that applied to your entire Gradio app, specifically:
* `theme`: The theme for your Gradio app
* `css`: Custom CSS code as a string
* `css_paths`: Paths to custom CSS files
* `js`... | App-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
he "Built with Gradio" link
- `"settings"`: Shows the settings link
**Before (Gradio 5.x):**
```python
import gradio as gr
with gr.Blocks() as demo:
gr.Textbox(label="Input")
demo.launch(show_api=False)
```
**After (Gradio 6.x):**
```python
import gradio as gr
with gr.Blocks() as demo:
gr.Textbox(label... | App-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
o as gr
with gr.Blocks() as demo:
btn = gr.Button("Click me")
output = gr.Textbox()
btn.click(fn=lambda: "Hello", outputs=output, show_api=False)
demo.launch()
```
Or to completely disable the API:
```python
btn.click(fn=lambda: "Hello", outputs=output, api_name=False)
```
**After (Gradio 6.x)... | App-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
.ChatInterface` now use the name of the function you pass in as the default API endpoint name
- This makes the API more descriptive and consistent with `gr.Blocks` behavior
E.g. if your Gradio app is:
```python
import gradio as gr
def generate_text(prompt):
return f"Generated: {prompt}"
demo = gr.Interface(fn=g... | App-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
```python
chatbot = gr.Chatbot(value=[["Hello", "Hi there!"]], type="tuples")
```
**After (Gradio 6.x):**
```python
import gradio as gr
Must use messages format
chatbot = gr.Chatbot(
value=[
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi there!"}
],
type="mes... | App-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
": "text", "text": "What is the capital of France?"}]},
{"role": "assistant", "content": [{"type": "text", "text": "Paris"}]}
]
```
**With files:**
When files are uploaded in the chat, they are represented as content blocks with `"type": "file"`. All content blocks (files and text) are grouped together in the sam... | App-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
Interface(
fn=predict,
inputs="text",
outputs="text",
examples=["Hello", "World"],
cache_examples=True,
cache_mode="lazy"
)
```
If you previously used `cache_examples=True` (which implied eager caching), no changes are required, as `cache_mode` defaults to `"eager"`.
| App-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
`gr.Video` no longer accepts tuple values for video and subtitles
The tuple format for returning video with subtitles has been deprecated. Instead of returning a tuple `(video_path, subtitle_path)`, you should now use the `gr.Video` component directly with the `subtitles` parameter.
**In Gradio 5.x:**
- You could ret... | Component-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
present in Gradio 5.x, explicitly set `padding=True`:
```python
html = gr.HTML("<div>Content</div>", padding=True)
```
`gr.Dataframe` `row_count` and `col_count` parameters restructured
The `row_count` and `col_count` parameters in `gr.Dataframe` have been restructured to provide more flexibility and clarity. The t... | Component-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
r.Dataframe(row_count=5, row_limits=None, column_count=3, column_limits=None)
```
Or with min/max constraints:
```python
Rows between 3 and 10, columns between 2 and 5
df = gr.Dataframe(row_count=5, row_limits=(3, 10), column_count=3, column_limits=(2, 5))
```
**Migration examples:**
- `row_count=(5, "fixed")` → `r... | Component-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
e`:
```python
import gradio as gr
chatbot = gr.Chatbot(allow_tags=False)
```
**Note:** You can also specify a list of specific tags to allow:
```python
chatbot = gr.Chatbot(allow_tags=["thinking", "tool_call"])
```
This will only preserve `<thinking>` and `<tool_call>` tags while removing all other custom tags.
... | Component-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
fore (Gradio 5.x):**
```python
audio = gr.Audio(min_length=1, max_length=10)
```
**After (Gradio 6.x):**
```python
audio = gr.Audio()
audio.upload(
fn=process_audio,
validator=lambda audio: gr.validators.is_audio_correct_length(audio, min_length=1, max_length=10),
inputs=audio
)
```
**`show_download_butt... | Component-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
``python
image = gr.Image(buttons=["download", "share", "fullscreen"])
```
`gr.Video` removed parameters
**`mirror_webcam`** - This parameter has been removed. Use `webcam_options` with `gr.WebcamOptions` instead.
**Before (Gradio 5.x):**
```python
video = gr.Video(mirror_webcam=True)
```
**After (Gradio 6.x):**
``... | Component-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
`gr.LogoutButton`** - This component has been removed. Use `gr.LoginButton` instead, which handles both login and logout processes.
**Before (Gradio 5.x):**
```python
logout_btn = gr.LogoutButton()
```
**After (Gradio 6.x):**
```python
login_btn = gr.LoginButton()
```
Native plot components removed parameters
The f... | Component-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
buttons=["copy"])
```
`gr.Markdown` removed parameters
**`show_copy_button`** - This parameter has been removed. Use the `buttons` parameter instead.
**Before (Gradio 5.x):**
```python
markdown = gr.Markdown(show_copy_button=True)
```
**After (Gradio 6.x):**
```python
markdown = gr.Markdown(buttons=["copy"])
```
`... | Component-level Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
`gradio sketch` command removed
The `gradio sketch` command-line tool has been deprecated and completely removed in Gradio 6. This tool was used to create Gradio apps through a visual interface.
**In Gradio 5.x:**
- You could run `gradio sketch` to launch an interactive GUI for building Gradio apps
- The tool would g... | CLI Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
`hf_token` parameter renamed to `token` in `Client`
The `hf_token` parameter in the `Client` class has been renamed to `token` for consistency and simplicity.
**Before (Gradio 5.x):**
```python
from gradio_client import Client
client = Client("abidlabs/my-private-space", hf_token="hf_...")
```
**After (Gradio 6.x)... | Python Client Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
"username/space-name")
result = client.predict("/predict", inputs)
except AppError as e:
Explicitly catch AppError
print(f"App error: {e}")
except ValueError as e:
This will no longer catch AppError
print(f"Value error: {e}")
```
| Python Client Changes | https://gradio.app/guides/gradio-6-migration-guide | Other Tutorials - Gradio 6 Migration Guide Guide |
Named-entity recognition (NER), also known as token classification or text tagging, is the task of taking a sentence and classifying every word (or "token") into different categories, such as names of people or names of locations, or different parts of speech.
For example, given the sentence:
> Does Chicago have any ... | Introduction | https://gradio.app/guides/named-entity-recognition | Other Tutorials - Named Entity Recognition Guide |
index': 5,
'word': 'Pakistani',
'start': 22,
'end': 31}]
```
If you have such a model, it is very easy to hook it up to Gradio's `HighlightedText` component. All you need to do is pass in this **list of entities**, along with the **original text** to the model, together as dictionary, with the keys being `"entit... | Introduction | https://gradio.app/guides/named-entity-recognition | Other Tutorials - Named Entity Recognition Guide |
3D models are becoming more popular in machine learning and make for some of the most fun demos to experiment with. Using `gradio`, you can easily build a demo of your 3D image model and share it with anyone. The Gradio 3D Model component accepts 3 file types including: _.obj_, _.glb_, & _.gltf_.
This guide will show ... | Introduction | https://gradio.app/guides/how-to-use-3D-model-component | Other Tutorials - How To Use 3D Model Component Guide |
Let's take a look at how to create the minimal interface above. The prediction function in this case will just return the original 3D model mesh, but you can change this function to run inference on your machine learning model. We'll take a look at more complex examples below.
```python
import gradio as gr
import os
... | Taking a Look at the Code | https://gradio.app/guides/how-to-use-3D-model-component | Other Tutorials - How To Use 3D Model Component Guide |
Below is a demo that uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object. Take a look at the [app.py](https://huggingface.co/spaces/gradio/dpt-depth-estimation-3d-obj/blob/main/app.py) file for a peek into the code and the model prediction function.
<gradio-app space="... | Exploring a more complex Model3D Demo: | https://gradio.app/guides/how-to-use-3D-model-component | Other Tutorials - How To Use 3D Model Component Guide |
Tabular data science is the most widely used domain of machine learning, with problems ranging from customer segmentation to churn prediction. Throughout various stages of the tabular data science workflow, communicating your work to stakeholders or clients can be cumbersome; which prevents data scientists from focusin... | Introduction | https://gradio.app/guides/using-gradio-for-tabular-workflows | Other Tutorials - Using Gradio For Tabular Workflows Guide |
We will take a look at how we can create a simple UI that predicts failures based on product information.
```python
import gradio as gr
import pandas as pd
import joblib
import datasets
inputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(4,"dynamic"), label="Input Data", interactive=1)]
outputs = [gr.Data... | Let's Create a Simple Interface! | https://gradio.app/guides/using-gradio-for-tabular-workflows | Other Tutorials - Using Gradio For Tabular Workflows Guide |
app space="gradio/tabular-playground"></gradio-app>
```python
import gradio as gr
import pandas as pd
import datasets
import seaborn as sns
import matplotlib.pyplot as plt
df = datasets.load_dataset("merve/supersoaker-failures")
df = df["train"].to_pandas()
df.dropna(axis=0, inplace=True)
def plot(df):
plt.scatter... | Let's Create a Simple Interface! | https://gradio.app/guides/using-gradio-for-tabular-workflows | Other Tutorials - Using Gradio For Tabular Workflows Guide |
`skops` is a library built on top of `huggingface_hub` and `sklearn`. With the recent `gradio` integration of `skops`, you can build tabular data interfaces with one line of code!
```python
import gradio as gr
title and description are optional
title = "Supersoaker Defective Product Prediction"
description = "This mo... | Easily load tabular data interfaces with one line of code using skops | https://gradio.app/guides/using-gradio-for-tabular-workflows | Other Tutorials - Using Gradio For Tabular Workflows Guide |
This guide explains how you can run background tasks from your gradio app.
Background tasks are operations that you'd like to perform outside the request-response
lifecycle of your app either once or on a periodic schedule.
Examples of background tasks include periodically synchronizing data to an external database or
... | Introduction | https://gradio.app/guides/running-background-tasks | Other Tutorials - Running Background Tasks Guide |
We will be creating a simple "Google-forms-style" application to gather feedback from users of the gradio library.
We will use a local sqlite database to store our data, but we will periodically synchronize the state of the database
with a [HuggingFace Dataset](https://huggingface.co/datasets) so that our user reviews ... | Overview | https://gradio.app/guides/running-background-tasks | Other Tutorials - Running Background Tasks Guide |
Our application will store the name of the reviewer, their rating of gradio on a scale of 1 to 5, as well as
any comments they want to share about the library. Let's write some code that creates a database table to
store this data. We'll also write some functions to insert a review into that table and fetch the latest ... | Step 1 - Write your database logic 💾 | https://gradio.app/guides/running-background-tasks | Other Tutorials - Running Background Tasks Guide |
Now that we have our database logic defined, we can use gradio create a dynamic web page to ask our users for feedback!
```python
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
name = gr.Textbox(label="Name", placeholder="What is your name?")
review = gr.Radio(label="How... | Step 2 - Create a gradio app ⚡ | https://gradio.app/guides/running-background-tasks | Other Tutorials - Running Background Tasks Guide |
We could call `demo.launch()` after step 2 and have a fully functioning application. However,
our data would be stored locally on our machine. If the sqlite file were accidentally deleted, we'd lose all of our reviews!
Let's back up our data to a dataset on the HuggingFace hub.
Create a dataset [here](https://huggingf... | Step 3 - Synchronize with HuggingFace Datasets 🤗 | https://gradio.app/guides/running-background-tasks | Other Tutorials - Running Background Tasks Guide |
print("updating db")
repo.push_to_hub(blocking=False, commit_message=f"Updating data at {datetime.datetime.now()}")
scheduler = BackgroundScheduler()
scheduler.add_job(func=backup_db, trigger="interval", seconds=60)
scheduler.start()
```
| Step 3 - Synchronize with HuggingFace Datasets 🤗 | https://gradio.app/guides/running-background-tasks | Other Tutorials - Running Background Tasks Guide |
You can use the HuggingFace [Spaces](https://huggingface.co/spaces) platform to deploy this application for free ✨
If you haven't used Spaces before, follow the previous guide [here](/using_hugging_face_integrations).
You will have to use the `HUB_TOKEN` environment variable as a secret in the Guides.
| Step 4 (Bonus) - Deployment to HuggingFace Spaces | https://gradio.app/guides/running-background-tasks | Other Tutorials - Running Background Tasks Guide |
Congratulations! You know how to run background tasks from your gradio app on a schedule ⏲️.
Checkout the application running on Spaces [here](https://huggingface.co/spaces/freddyaboulton/gradio-google-forms).
The complete code is [here](https://huggingface.co/spaces/freddyaboulton/gradio-google-forms/blob/main/app.py... | Conclusion | https://gradio.app/guides/running-background-tasks | Other Tutorials - Running Background Tasks Guide |
Make sure you have Gradio installed along with a recent version of `huggingface_hub`:
```bash
pip install --upgrade gradio huggingface_hub
```
> `huggingface_hub >= 1.4.0` is required for the skills command.
| Prerequisites | https://gradio.app/guides/Gradio-skills-for-ai-coding-assistants | Other Tutorials - Gradio Skills For Ai Coding Assistants Guide |
The general Gradio skill gives your assistant comprehensive knowledge of the Gradio API — components, event listeners, layout patterns, and working examples.
To install for Codex, Cursor, OpenCode, and other assistants that use the shared `.agents/skills` directory:
```bash
gradio skills add
```
This downloads the G... | Installing the General Gradio Skill | https://gradio.app/guides/Gradio-skills-for-ai-coding-assistants | Other Tutorials - Gradio Skills For Ai Coding Assistants Guide |
By default, skills are installed **locally** in your current project directory. This means the assistant only has Gradio knowledge when working in that project.
To install **globally** (user-level, available in all projects):
```bash
gradio skills add --global
```
Or using the short flag:
```bash
gradio skills add ... | Project-Level vs. Global Installation | https://gradio.app/guides/Gradio-skills-for-ai-coding-assistants | Other Tutorials - Gradio Skills For Ai Coding Assistants Guide |
One of the most powerful features is generating a skill for any public HuggingFace Space. This gives your assistant full knowledge of that Space's API — endpoints, parameters, return types, and ready-to-use code snippets.
```bash
gradio skills add abidlabs/english-translator
```
This connects to the Space, extracts i... | Generating a Skill for a Specific HuggingFace Space | https://gradio.app/guides/Gradio-skills-for-ai-coding-assistants | Other Tutorials - Gradio Skills For Ai Coding Assistants Guide |
If a skill is already installed, the command will exit with an error. To overwrite it:
```bash
gradio skills add --force
```
| Overwriting Existing Skills | https://gradio.app/guides/Gradio-skills-for-ai-coding-assistants | Other Tutorials - Gradio Skills For Ai Coding Assistants Guide |
General Gradio Skill
| File | Contents |
|------|----------|
| `SKILL.md` | Core API reference — component signatures, event listeners, layout patterns, ChatInterface, and links to detailed guides |
| `examples.md` | Complete working Gradio apps covering common patterns (forms, chatbots, streaming, image processing, e... | What Gets Installed | https://gradio.app/guides/Gradio-skills-for-ai-coding-assistants | Other Tutorials - Gradio Skills For Ai Coding Assistants Guide |
Here's a typical workflow using skills with Claude Code:
1. **Install the skill** in your project:
```bash
cd my-project
gradio skills add --claude
```
2. **Start Claude Code** and ask it to build a Gradio app:
```
> Build me a Gradio app with an image input that applies a sepia filter
and disp... | Example Workflow | https://gradio.app/guides/Gradio-skills-for-ai-coding-assistants | Other Tutorials - Gradio Skills For Ai Coding Assistants Guide |
When you are building a Gradio demo, particularly out of Blocks, you may find it cumbersome to keep re-running your code to test your changes.
To make it faster and more convenient to write your code, we've made it easier to "reload" your Gradio apps instantly when you are developing in a **Python IDE** (like VS Code,... | Why Hot Reloading? | https://gradio.app/guides/developing-faster-with-reload-mode | Other Tutorials - Developing Faster With Reload Mode Guide |
If you are building Gradio Blocks using a Python IDE, your file of code (let's name it `run.py`) might look something like this:
```python
import gradio as gr
with gr.Blocks() as demo:
gr.Markdown("Greetings from Gradio!")
inp = gr.Textbox(placeholder="What is your name?")
out = gr.Textbox()
inp.chan... | Python IDE Reload 🔥 | https://gradio.app/guides/developing-faster-with-reload-mode | Other Tutorials - Developing Faster With Reload Mode Guide |
emo as the 2nd parameter in your code. So if your `run.py` file looked like this:
```python
import gradio as gr
with gr.Blocks() as my_demo:
gr.Markdown("Greetings from Gradio!")
inp = gr.Textbox(placeholder="What is your name?")
out = gr.Textbox()
inp.change(fn=lambda x: f"Welcome, {x}!",
... | Python IDE Reload 🔥 | https://gradio.app/guides/developing-faster-with-reload-mode | Other Tutorials - Developing Faster With Reload Mode Guide |
second.
As a small aside, this auto-reloading happens if you change your `run.py` source code or the Gradio source code. Meaning that this can be useful if you decide to [contribute to Gradio itself](https://github.com/gradio-app/gradio/blob/main/CONTRIBUTING.md) ✅
| Python IDE Reload 🔥 | https://gradio.app/guides/developing-faster-with-reload-mode | Other Tutorials - Developing Faster With Reload Mode Guide |
By default, reload mode will re-run your entire script for every change you make.
But there are some cases where this is not desirable.
For example, loading a machine learning model should probably only happen once to save time. There are also some Python libraries that use C or Rust extensions that throw errors when t... | Controlling the Reload 🎛️ | https://gradio.app/guides/developing-faster-with-reload-mode | Other Tutorials - Developing Faster With Reload Mode Guide |
You can also enable Gradio's **Vibe Mode**, which, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. To enable this, simply run use the `--vibe` flag with Gradio, e.g. `gradio --vibe app.py`.
Vibe Mode lets you describe commands using natural language and have ... | Vibe Mode | https://gradio.app/guides/developing-faster-with-reload-mode | Other Tutorials - Developing Faster With Reload Mode Guide |
What about if you use Jupyter Notebooks (or Colab Notebooks, etc.) to develop code? We got something for you too!
We've developed a **magic command** that will create and run a Blocks demo for you. To use this, load the gradio extension at the top of your notebook:
`%load_ext gradio`
Then, in the cell that you are d... | Jupyter Notebook Magic 🔮 | https://gradio.app/guides/developing-faster-with-reload-mode | Other Tutorials - Developing Faster With Reload Mode Guide |
Now that you know how to develop quickly using Gradio, start building your own!
If you are looking for inspiration, try exploring demos other people have built with Gradio, [browse public Hugging Face Spaces](http://hf.space/) 🤗
| Next Steps | https://gradio.app/guides/developing-faster-with-reload-mode | Other Tutorials - Developing Faster With Reload Mode Guide |
First of all, we need some data to visualize. Following this [excellent guide](https://supabase.com/blog/loading-data-supabase-python), we'll create fake commerce data and put it in Supabase.
1\. Start by creating a new project in Supabase. Once you're logged in, click the "New Project" button
2\. Give your project a... | Create a table in Supabase | https://gradio.app/guides/creating-a-dashboard-from-supabase-data | Other Tutorials - Creating A Dashboard From Supabase Data Guide |
The next step is to write data to a Supabase dataset. We will use the Supabase Python library to do this.
6\. Install `supabase` by running the following command in your terminal:
```bash
pip install supabase
```
7\. Get your project URL and API key. Click the Settings (gear icon) on the left pane and click 'API'. T... | Write data to Supabase | https://gradio.app/guides/creating-a-dashboard-from-supabase-data | Other Tutorials - Creating A Dashboard From Supabase Data Guide |
Finally, we will read the data from the Supabase dataset using the same `supabase` Python library and create a realtime dashboard using `gradio`.
Note: We repeat certain steps in this section (like creating the Supabase client) in case you did not go through the previous sections. As described in Step 7, you will need... | Visualize the Data in a Real-Time Gradio Dashboard | https://gradio.app/guides/creating-a-dashboard-from-supabase-data | Other Tutorials - Creating A Dashboard From Supabase Data Guide |
That's it! In this tutorial, you learned how to write data to a Supabase dataset, and then read that data and plot the results as bar plots. If you update the data in the Supabase database, you'll notice that the Gradio dashboard will update within a minute.
Try adding more plots and visualizations to this example (or... | Conclusion | https://gradio.app/guides/creating-a-dashboard-from-supabase-data | Other Tutorials - Creating A Dashboard From Supabase Data Guide |
In this Guide, we'll walk you through:
- Introduction of ONNX, ONNX model zoo, Gradio, and Hugging Face Spaces
- How to setup a Gradio demo for EfficientNet-Lite4
- How to contribute your own Gradio demos for the ONNX organization on Hugging Face
Here's an [example](https://onnx-efficientnet-lite4.hf.space/) of an ON... | Introduction | https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face | Other Tutorials - Gradio And Onnx On Hugging Face Guide |
Open Neural Network Exchange ([ONNX](https://onnx.ai/)) is an open standard format for representing machine learning models. ONNX is supported by a community of partners who have implemented it in many frameworks and tools. For example, if you have trained a model in TensorFlow or PyTorch, you can convert it to ONNX ea... | What is the ONNX Model Zoo? | https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face | Other Tutorials - Gradio And Onnx On Hugging Face Guide |
Gradio
Gradio lets users demo their machine learning models as a web app all in python code. Gradio wraps a python function into a user interface and the demos can be launched inside jupyter notebooks, colab notebooks, as well as embedded in your own website and hosted on Hugging Face Spaces for free.
Get started [he... | What are Hugging Face Spaces & Gradio? | https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face | Other Tutorials - Gradio And Onnx On Hugging Face Guide |
There are a lot of Jupyter notebooks in the ONNX Model Zoo for users to test models. Previously, users needed to download the models themselves and run those notebooks locally for testing. With Hugging Face, the testing process can be much simpler and more user-friendly. Users can easily try certain ONNX Model Zoo mode... | How did Hugging Face help the ONNX Model Zoo? | https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face | Other Tutorials - Gradio And Onnx On Hugging Face Guide |
ONNX Runtime is a cross-platform inference and training machine-learning accelerator. It makes live Gradio demos with ONNX Model Zoo model on Hugging Face possible.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorF... | What is the role of ONNX Runtime? | https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face | Other Tutorials - Gradio And Onnx On Hugging Face Guide |
EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite models. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU.... | Setting up a Gradio Demo for EfficientNet-Lite4 | https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face | Other Tutorials - Gradio And Onnx On Hugging Face Guide |
the image with a proportional scale
def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):
height, width, _ = img.shape
new_height = int(100. * out_height / scale)
new_width = int(100. * out_width / scale)
if height > width:
w = new_width
h = int... | Setting up a Gradio Demo for EfficientNet-Lite4 | https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face | Other Tutorials - Gradio And Onnx On Hugging Face Guide |
examples=examples).launch()
```
| Setting up a Gradio Demo for EfficientNet-Lite4 | https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face | Other Tutorials - Gradio And Onnx On Hugging Face Guide |
- Add model to the [onnx model zoo](https://github.com/onnx/models/blob/main/.github/PULL_REQUEST_TEMPLATE.md)
- Create an account on Hugging Face [here](https://huggingface.co/join).
- See list of models left to add to ONNX organization, please refer to the table with the [Models list](https://github.com/onnx/modelsmo... | How to contribute Gradio demos on HF spaces using ONNX models | https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face | Other Tutorials - Gradio And Onnx On Hugging Face Guide |
To use Gradio with BigQuery, you will need to obtain your BigQuery credentials and use them with the [BigQuery Python client](https://pypi.org/project/google-cloud-bigquery/). If you already have BigQuery credentials (as a `.json` file), you can skip this section. If not, you can do this for free in just a couple of mi... | Setting up your BigQuery Credentials | https://gradio.app/guides/creating-a-dashboard-from-bigquery-data | Other Tutorials - Creating A Dashboard From Bigquery Data Guide |
Once you have the credentials, you will need to use the BigQuery Python client to authenticate using your credentials. To do this, you will need to install the BigQuery Python client by running the following command in the terminal:
```bash
pip install google-cloud-bigquery[pandas]
```
You'll notice that we've instal... | Using the BigQuery Client | https://gradio.app/guides/creating-a-dashboard-from-bigquery-data | Other Tutorials - Creating A Dashboard From Bigquery Data Guide |
Once you have a function to query the data, you can use the `gr.DataFrame` component from the Gradio library to display the results in a tabular format. This is a useful way to inspect the data and make sure that it has been queried correctly.
Here is an example of how to use the `gr.DataFrame` component to display th... | Building the Real-Time Dashboard | https://gradio.app/guides/creating-a-dashboard-from-bigquery-data | Other Tutorials - Creating A Dashboard From Bigquery Data Guide |
Let's go through a simple example to understand how to containerize a Gradio app using Docker.
Step 1: Create Your Gradio App
First, we need a simple Gradio app. Let's create a Python file named `app.py` with the following content:
```python
import gradio as gr
def greet(name):
return f"Hello {name}!"
iface = ... | How to Dockerize a Gradio App | https://gradio.app/guides/deploying-gradio-with-docker | Other Tutorials - Deploying Gradio With Docker Guide |
When running Gradio applications in Docker, there are a few important things to keep in mind:
Running the Gradio app on `"0.0.0.0"` and exposing port 7860
In the Docker environment, setting `GRADIO_SERVER_NAME="0.0.0.0"` as an environment variable (or directly in your Gradio app's `launch()` function) is crucial for ... | Important Considerations | https://gradio.app/guides/deploying-gradio-with-docker | Other Tutorials - Deploying Gradio With Docker Guide |
it's a gradio app that reads a graph out of a json file and gives you a canvas. the whole thing is this:
```python
import gradio as gr
def reverse(text: str) -> str:
return (text or "")[::-1]
demo = gr.Workflow(graph="workflow.json", bind={"reverse": reverse})
demo.launch()
```
`bind` is you handing the canvas ... | what gr.Workflow actually is | https://gradio.app/guides/gradio-workflows-vs-n8n-vs-comfyui | Other Tutorials - Gradio Workflows Vs N8N Vs Comfyui Guide |
here's the bit that actually changes how you work.
the sidebar ships with a curated set of spaces and models so it isn't an empty search box on day one, and searching it searches that set. everything else on the hub you reach by pasting: drop `owner/repo` or a url into the sidebar's space box, or into the picker on a ... | the hub is the node library | https://gradio.app/guides/gradio-workflows-vs-n8n-vs-comfyui | Other Tutorials - Gradio Workflows Vs N8N Vs Comfyui Guide |
the execution logic that runs when you click through the canvas also exists in python, so your graph runs fine with nobody watching it.
which means the thing you dragged together is also an api.
build a graph where a text box feeds a local function, a model, and a space, all landing on outputs. gradio knows they're c... | the workflow is an api | https://gradio.app/guides/gradio-workflows-vs-n8n-vs-comfyui | Other Tutorials - Gradio Workflows Vs N8N Vs Comfyui Guide |
n8n is automation. cron jobs. webhooks. it watches your inbox. it has a credentials store hooked into a few hundred services, it replays failed runs, it has fallback workflows for when something dies at 4am and you're asleep.
"when a stripe charge goes through, write a row to postgres and post in sales" — n8n solved t... | vs n8n | https://gradio.app/guides/gradio-workflows-vs-n8n-vs-comfyui | Other Tutorials - Gradio Workflows Vs N8N Vs Comfyui Guide |
comfy lives a floor below this.
its nodes are the guts of image generation, samplers and schedulers and loras and all of it. you want to change how one step of a twenty step generation behaves? comfy. and that custom node ecosystem is genuinely wild, people have built insane things in there.
but getting a new node in... | vs comfyui | https://gradio.app/guides/gradio-workflows-vs-n8n-vs-comfyui | Other Tutorials - Gradio Workflows Vs N8N Vs Comfyui Guide |
everything flows one way. no cycles, no while loops, no loop-until-it's-good-enough.
in the canvas, independent branches run at the same time. called as an api, the python executor walks the graph one node at a time — same results, no parallelism.
saving is gated. locally you need the write-access link gradio prints ... | before you start | https://gradio.app/guides/gradio-workflows-vs-n8n-vs-comfyui | Other Tutorials - Gradio Workflows Vs N8N Vs Comfyui Guide |
**can i use any hugging face model?**
yes, any model you can run through hf inference. it's in the sidebar if it's curated, otherwise paste the repo id into a node's picker. ports come from the model's pipeline tag.
**does every gradio space work as a node?**
most do. it needs to be awake and it needs an api endpoint ... | quick answers to stuff people ask | https://gradio.app/guides/gradio-workflows-vs-n8n-vs-comfyui | Other Tutorials - Gradio Workflows Vs N8N Vs Comfyui Guide |
**[OpenAPI](https://www.openapis.org/)** is a widely adopted standard for describing RESTful APIs in a machine-readable format, typically as a JSON file.
You can create a Gradio UI from an OpenAPI Spec **in 1 line of Python**, instantly generating an interactive web interface for any API, making it accessible for de... | Introduction | https://gradio.app/guides/from-openapi-spec | Other Tutorials - From Openapi Spec Guide |
Gradio now provides a convenient function, `gr.load_openapi`, that can automatically generate a Gradio app from an OpenAPI v3 specification. This function parses the spec, creates UI components for each endpoint and parameter, and lets you interact with the API directly from your browser.
Here's a minimal example:
``... | How it works | https://gradio.app/guides/from-openapi-spec | Other Tutorials - From Openapi Spec Guide |
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