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Remove usage of deprecated gr.inputs and gr.outputs from website (#1796)

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* remove deprecated api from docs

* Remove usage of 'state' shortcut

* Back out state changes

* Back out state changes in creating_a_chatbot

* Link chatbot guide to Variable docs
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Freddy Boulton 2022-07-19 11:53:01 -04:00 committed by GitHub
parent 0dbc8bfcf3
commit 4a1879edaf
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11 changed files with 35 additions and 29 deletions
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2
demo/chatbot_demo/run.py
View File

@ -17,7 +17,7 @@ def chat(message, history):
return history, history
chatbot = gr.Chatbot(color_map=("green", "pink")).style()
chatbot = gr.Chatbot().style(color_map=("green", "pink"))
demo = gr.Interface(
chat,
["text", "state"],

2
demo/image_mod/run.py
View File

@ -6,7 +6,7 @@ def image_mod(image):
return image.rotate(45)
demo = gr.Interface(image_mod, gr.inputs.Image(type="pil"), "image",
demo = gr.Interface(image_mod, gr.Image(type="pil"), "image",
flagging_options=["blurry", "incorrect", "other"], examples=[
os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
os.path.join(os.path.dirname(__file__), "images/lion.jpg"),

2
guides/Gradio_and_ONNX_on_Hugging_Face.md
View File

@ -129,7 +129,7 @@ def inference(img):
title = "EfficientNet-Lite4"
description = "EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite model. 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."
examples = [['catonnx.jpg']]
gr.Interface(inference, gr.inputs.Image(type="filepath"), "label", title=title, description=description, examples=examples).launch()
gr.Interface(inference, gr.Image(type="filepath"), "label", title=title, description=description, examples=examples).launch()
```

10
guides/advanced_interface_features.md
View File

@ -68,7 +68,7 @@ gr.Interface.load("huggingface/gpt2").launch();
```python
gr.Interface.load("huggingface/EleutherAI/gpt-j-6B",
inputs=gr.inputs.Textbox(lines=5, label="Input Text") # customizes the input component
inputs=gr.Textbox(lines=5, label="Input Text") # customizes the input component
).launch()
```
@ -123,7 +123,13 @@ This sets up a queue of workers to handle the predictions and return the respons
Your function may use data that persists beyond a single function call. If the data is something accessible to all function calls and all users, you can create a variable outside the function call and access it inside the function. For example, you may load a large model outside the function and use it inside the function so that every function call does not need to reload the model.
Another type of data persistence Gradio supports is session **state**, where data persists across multiple submits within a page load. However, data is *not* shared between different users of your model. To store data in a session state, you need to do three things: (1) Pass in an extra parameter into your function, which represents the state of the interface. (2) At the end of the function, return the updated value of the state as an extra return value (3) Add the `'state'` input and `'state'` output components when creating your `Interface`. See the chatbot example below:
Another type of data persistence Gradio supports is session **state**, where data persists across multiple submits within a page load. However, data is *not* shared between different users of your model. To store data in a session state, you need to do three things:
1. Pass in an extra parameter into your function, which represents the state of the interface.
2. At the end of the function, return the updated value of the state as an extra return value.
3. Add the `'state'` input and `'state'` output components when creating your `Interface`
See the chatbot example below:
$code_chatbot_demo
$demo_chatbot_demo

10
guides/create_your_own_friends_with_a_gan.md
View File

@ -109,7 +109,7 @@ import gradio as gr
gr.Interface(
predict,
inputs=[
gr.inputs.Slider(0, 1000, label='Seed', default=42),
gr.Slider(0, 1000, label='Seed', default=42),
],
outputs="image",
).launch()
@ -127,8 +127,8 @@ Generating 4 punks at a time is a good start, but maybe we'd like to control how
gr.Interface(
predict,
inputs=[
gr.inputs.Slider(0, 1000, label='Seed', default=42),
gr.inputs.Slider(4, 64, label='Number of Punks', step=1, default=10), # Adding another slider!
gr.Slider(0, 1000, label='Seed', default=42),
gr.Slider(4, 64, label='Number of Punks', step=1, default=10), # Adding another slider!
],
outputs="image",
).launch()
@ -215,8 +215,8 @@ def predict(seed, num_punks):
gr.Interface(
predict,
inputs=[
gr.inputs.Slider(0, 1000, label='Seed', default=42),
gr.inputs.Slider(4, 64, label='Number of Punks', step=1, default=10),
gr.Slider(0, 1000, label='Seed', default=42),
gr.Slider(4, 64, label='Number of Punks', step=1, default=10),
],
outputs="image",
examples=[[123, 15], [42, 29], [456, 8], [1337, 35]],

2
guides/creating_a_chatbot.md
View File

@ -2,7 +2,7 @@
Related spaces: https://huggingface.co/spaces/abidlabs/chatbot-minimal, https://huggingface.co/spaces/ThomasSimonini/Chat-with-Gandalf-GPT-J6B, https://huggingface.co/spaces/gorkemgoknar/moviechatbot, https://huggingface.co/spaces/Kirili4ik/chat-with-Kirill
Tags: NLP, TEXT, HTML
Docs: textbox, state
Docs: textbox, variable
## Introduction

4
guides/image_classification_in_pytorch.md
View File

@ -76,8 +76,8 @@ Finally, we'll add one more parameter, the `examples`, which allows us to prepop
import gradio as gr
gr.Interface(fn=predict,
inputs=gr.inputs.Image(type="pil"),
outputs=gr.outputs.Label(num_top_classes=3),
inputs=gr.Image(type="pil"),
outputs=gr.Label(num_top_classes=3),
examples=["lion.jpg", "cheetah.jpg"]).launch()
```

4
guides/image_classification_in_tensorflow.md
View File

@ -74,8 +74,8 @@ Finally, we'll add one more parameter, the `examples`, which allows us to prepop
import gradio as gr
gr.Interface(fn=classify_image,
inputs=gr.inputs.Image(shape=(224, 224)),
outputs=gr.outputs.Label(num_top_classes=3),
inputs=gr.Image(shape=(224, 224)),
outputs=gr.Label(num_top_classes=3),
examples=["banana.jpg", "car.jpg"]).launch()
```

20
guides/real_time_speech_recognition.md
View File

@ -2,7 +2,7 @@
Related spaces: https://huggingface.co/spaces/abidlabs/streaming-asr-paused, https://huggingface.co/spaces/abidlabs/full-context-asr
Tags: ASR, SPEECH, STREAMING
Docs: audio, state, textbox
Docs: audio, variable, textbox
## Introduction
@ -54,7 +54,7 @@ def transcribe(audio):
gr.Interface(
fn=transcribe,
inputs=gr.inputs.Audio(source="microphone", type="filepath"),
inputs=gr.Audio(source="microphone", type="filepath"),
outputs="text").launch()
```
@ -76,23 +76,23 @@ When adding state to a Gradio demo, you need to do a total of 3 things:
* Add a `state` parameter to the function
* Return the updated `state` at the end of the function
* Add the `"state"` components to the `inputs` and `outputs` in `Interface`
* Add the `"state"` components to the `inputs` and `outputs` in `Interface`
Here's what the code looks like:
```python
import gradio as gr
def transcribe(audio, state=""):
text = p(audio)["text"]
state += text + " "
return state, state
# Set the starting state to an empty string
gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath"),
"state"
gr.Audio(source="microphone", type="filepath", streaming=True),
"state"
],
outputs=[
"textbox",
@ -126,7 +126,7 @@ def transcribe(audio, state=""):
gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="filepath"),
gr.Audio(source="microphone", type="filepath", streaming=True),
"state"
],
outputs=[
@ -211,8 +211,8 @@ import gradio as gr
gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type="numpy"),
"state"
gr.Audio(source="microphone", type="numpy"),
"state"
],
outputs= [
"text",

6
guides/using_flagging.md
View File

@ -55,7 +55,7 @@ def calculator(num1, operation, num2):
iface = gr.Interface(
calculator,
["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"],
["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
"number",
allow_flagging="manual"
)
@ -104,7 +104,7 @@ If we go back to the calculator example, the following code will create the inte
```python
iface = gr.Interface(
calculator,
["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"],
["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
"number",
allow_flagging="manual",
flagging_options=["wrong sign", "off by one", "other"]
@ -137,7 +137,7 @@ hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-calculator-demo")
iface = gr.Interface(
calculator,
["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"],
["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
"number",
allow_flagging="manual",
flagging_options=["wrong sign", "off by one", "other"],

2
guides/using_hugging_face_integrations.md
View File

@ -102,7 +102,7 @@ import gradio as gr
gr.Interface.load(
"spaces/eugenesiow/remove-bg",
inputs=[gr.inputs.Image(label="Input Image", source="webcam")]
inputs=[gr.Image(label="Input Image", source="webcam")]
).launch()
```