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Interface types: handle input-only, output-only, and unified interfaces (#1108)
* optional labels * added prop * Added IOComponent class * get component fix * fixed component function * fixed test components * formatting * fixed output tests * working on blocks tests * fixed test blocks * cleanup * merged * unrender * add article * formatting * fixed render() * added demo * formatting * merge main * add interface types * added output only * added input only * formatting * added demos * formatting * removed unnecessary import * updated demos * fixed duplication * fix for state
This commit is contained in:
Abubakar Abid
GitHub
parent
f47e65b14d
commit
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32
demo/fake_gan_no_input/run.py
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32
demo/fake_gan_no_input/run.py
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@ -0,0 +1,32 @@
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# This demo needs to be run from the repo folder.
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# python demo/fake_gan/run.py
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import random
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import time
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import gradio as gr
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def fake_gan():
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time.sleep(1)
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image = random.choice(
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[
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"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
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"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
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"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
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"https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
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"https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80",
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]
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)
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return image
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demo = gr.Interface(
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fn=fake_gan,
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inputs=None,
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outputs=gr.Image(label="Generated Image"),
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title="FD-GAN",
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description="This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.",
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)
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if __name__ == "__main__":
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demo.launch()
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14
demo/gpt_j_unified/run.py
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14
demo/gpt_j_unified/run.py
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@ -0,0 +1,14 @@
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import gradio as gr
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component = gr.Textbox(lines=5, label="Text")
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api = gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
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demo = gr.Interface(
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fn=lambda x: x[:-50] + api(x[-50:]),
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inputs=component,
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outputs=component,
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title="GPT-J-6B",
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)
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if __name__ == "__main__":
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demo.launch()
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@ -129,7 +129,7 @@ def get_huggingface_interface(model_name, api_key, alias):
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},
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"text-classification": {
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"inputs": components.Textbox(label="Input"),
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"outputs": components.Label(type="confidences", label="Classification"),
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"outputs": components.Label(label="Classification"),
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"preprocess": lambda x: {"inputs": x},
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"postprocess": lambda r: {
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i["label"].split(", ")[0]: i["score"] for i in r.json()[0]
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@ -159,7 +159,7 @@ def get_huggingface_interface(model_name, api_key, alias):
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components.Textbox(label="Possible class names (" "comma-separated)"),
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components.Checkbox(label="Allow multiple true classes"),
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],
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"outputs": components.Label(type="confidences", label="Classification"),
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"outputs": components.Label(label="Classification"),
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"preprocess": lambda i, c, m: {
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"inputs": i,
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"parameters": {"candidate_labels": c, "multi_class": m},
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@ -13,6 +13,7 @@ import random
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import re
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import warnings
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import weakref
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from enum import Enum, auto
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from typing import TYPE_CHECKING, Any, Callable, List, Optional, Tuple
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from markdown_it import MarkdownIt
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@ -48,6 +49,12 @@ class Interface(Blocks):
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# stores references to all currently existing Interface instances
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instances: weakref.WeakSet = weakref.WeakSet()
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class InterfaceTypes(Enum):
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STANDARD = auto()
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INPUT_ONLY = auto()
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OUTPUT_ONLY = auto()
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UNIFIED = auto()
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@classmethod
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def get_instances(cls) -> List[Interface]:
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"""
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@ -103,8 +110,8 @@ class Interface(Blocks):
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def __init__(
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self,
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fn: Callable | List[Callable],
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inputs: str | Component | List[str | Component] = None,
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outputs: str | Component | List[str | Component] = None,
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inputs: Optional[str | Component | List[str | Component]] = None,
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outputs: Optional[str | Component | List[str | Component]] = None,
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examples: Optional[List[Any] | List[List[Any]] | str] = None,
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cache_examples: Optional[bool] = None,
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examples_per_page: int = 10,
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@ -157,10 +164,15 @@ class Interface(Blocks):
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analytics_enabled=analytics_enabled, mode="interface", **kwargs
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)
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if inputs is None:
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inputs = []
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if outputs is None:
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self.interface_type = self.InterfaceTypes.STANDARD
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if inputs is None and outputs is None:
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raise ValueError("Must provide at least one of `inputs` or `outputs`")
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elif outputs is None:
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outputs = []
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self.interface_type = self.InterfaceTypes.INPUT_ONLY
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elif inputs is None:
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inputs = []
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self.interface_type = self.InterfaceTypes.OUTPUT_ONLY
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if not isinstance(fn, list):
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fn = [fn]
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@ -195,10 +207,20 @@ class Interface(Blocks):
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self.input_components = [get_component_instance(i).unrender() for i in inputs]
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self.output_components = [get_component_instance(o).unrender() for o in outputs]
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for o in self.output_components:
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o.interactive = (
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False # Force output components to be treated as non-interactive
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)
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if len(self.input_components) == len(self.output_components):
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same_components = [
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i is o for i, o in zip(self.input_components, self.output_components)
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]
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if all(same_components):
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self.interface_type = self.InterfaceTypes.UNIFIED
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if self.interface_type in [
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self.InterfaceTypes.STANDARD,
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self.InterfaceTypes.OUTPUT_ONLY,
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]:
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for o in self.output_components:
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o.interactive = False # Force output components to be non-interactive
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if repeat_outputs_per_model:
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self.output_components *= len(fn)
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@ -441,49 +463,85 @@ class Interface(Blocks):
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if self.description:
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Markdown(self.description)
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with Row():
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with Column(
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css={
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"background-color": "rgb(249,250,251)",
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"padding": "0.5rem",
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"border-radius": "0.5rem",
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}
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):
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input_component_column = Column()
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with input_component_column:
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for component in self.input_components:
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component.render()
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if self.interpretation:
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interpret_component_column = Column(visible=False)
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interpretation_set = []
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with interpret_component_column:
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if self.interface_type in [
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self.InterfaceTypes.STANDARD,
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self.InterfaceTypes.INPUT_ONLY,
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self.InterfaceTypes.UNIFIED,
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]:
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with Column(
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css={
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"background-color": "rgb(249,250,251)",
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"padding": "0.5rem",
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"border-radius": "0.5rem",
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}
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):
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input_component_column = Column()
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if self.interface_type in [
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self.InterfaceTypes.INPUT_ONLY,
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self.InterfaceTypes.UNIFIED,
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]:
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status_tracker = StatusTracker(cover_container=True)
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with input_component_column:
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for component in self.input_components:
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interpretation_set.append(Interpretation(component))
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with Row():
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clear_btn = Button("Clear")
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if not self.live:
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submit_btn = Button("Submit")
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with Column(
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css={
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"background-color": "rgb(249,250,251)",
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"padding": "0.5rem",
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"border-radius": "0.5rem",
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}
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):
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status_tracker = StatusTracker(cover_container=True)
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for component in self.output_components:
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component.render()
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with Row():
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if self.allow_flagging == "manual":
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flag_btn = Button("Flag")
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flag_btn._click_no_preprocess(
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lambda *flag_data: self.flagging_callback.flag(
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flag_data
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),
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inputs=self.input_components + self.output_components,
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outputs=[],
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)
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component.render()
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if self.interpretation:
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interpretation_btn = Button("Interpret")
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interpret_component_column = Column(visible=False)
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interpretation_set = []
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with interpret_component_column:
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for component in self.input_components:
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interpretation_set.append(Interpretation(component))
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with Row():
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if self.interface_type in [
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self.InterfaceTypes.STANDARD,
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self.InterfaceTypes.INPUT_ONLY,
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]:
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clear_btn = Button("Clear")
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if not self.live:
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submit_btn = Button("Submit")
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elif self.interface_type == self.InterfaceTypes.UNIFIED:
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clear_btn = Button("Clear")
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submit_btn = Button("Submit")
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if self.allow_flagging == "manual":
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flag_btn = Button("Flag")
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flag_btn._click_no_preprocess(
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lambda *flag_data: self.flagging_callback.flag(
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flag_data
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),
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inputs=self.input_components,
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outputs=[],
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)
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if self.interface_type in [
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self.InterfaceTypes.STANDARD,
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self.InterfaceTypes.OUTPUT_ONLY,
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]:
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with Column(
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css={
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"background-color": "rgb(249,250,251)",
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"padding": "0.5rem",
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"border-radius": "0.5rem",
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}
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):
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status_tracker = StatusTracker(cover_container=True)
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for component in self.output_components:
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component.render()
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with Row():
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if self.interface_type == self.InterfaceTypes.OUTPUT_ONLY:
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clear_btn = Button("Clear")
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submit_btn = Button("Generate")
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if self.allow_flagging == "manual":
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flag_btn = Button("Flag")
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flag_btn._click_no_preprocess(
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lambda *flag_data: self.flagging_callback.flag(
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flag_data
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),
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inputs=self.input_components
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+ self.output_components,
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outputs=[],
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)
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if self.interpretation:
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interpretation_btn = Button("Interpret")
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submit_fn = (
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lambda *args: self.run_prediction(args)[0]
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if len(self.output_components) == 1
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@ -503,20 +561,41 @@ class Interface(Blocks):
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)
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clear_btn.click(
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(
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lambda: [
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component.default_value
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if hasattr(component, "default_value")
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else None
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for component in self.input_components + self.output_components
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]
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+ [True]
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+ ([False] if self.interpretation else [])
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lambda: utils.resolve_singleton(
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[
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component.default_value
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if hasattr(component, "default_value")
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else None
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for component in self.input_components
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+ self.output_components
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]
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+ (
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[True]
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if self.interface_type
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in [
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self.InterfaceTypes.STANDARD,
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self.InterfaceTypes.INPUT_ONLY,
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self.InterfaceTypes.UNIFIED,
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]
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else []
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)
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+ ([False] if self.interpretation else [])
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)
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),
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[],
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(
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self.input_components
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+ self.output_components
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+ [input_component_column]
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+ (
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[input_component_column]
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if self.interface_type
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in [
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self.InterfaceTypes.STANDARD,
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self.InterfaceTypes.INPUT_ONLY,
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self.InterfaceTypes.UNIFIED,
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]
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else []
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)
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+ ([interpret_component_column] if self.interpretation else [])
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),
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)
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@ -614,17 +693,15 @@ class Interface(Blocks):
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for predict_fn in self.predict:
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prediction = predict_fn(*processed_input)
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if len(self.output_components) == len(self.predict):
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if len(self.output_components) == len(self.predict) or prediction is None:
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prediction = [prediction]
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if self.api_mode: # Serialize the input
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prediction_ = copy.deepcopy(prediction)
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prediction = []
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for (
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pred
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) in (
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prediction_
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): # Done this way to handle both single interfaces with multiple outputs and Parallel() interfaces
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# Done this way to handle both single interfaces with multiple outputs and Parallel() interfaces
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for pred in prediction_:
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prediction.append(
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self.output_components[output_component_counter].deserialize(
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pred
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@ -369,3 +369,10 @@ def delete_none(_dict):
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_dict = type(_dict)(delete_none(item) for item in _dict if item is not None)
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return _dict
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def resolve_singleton(_list):
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if len(_list) == 1:
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return _list[0]
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else:
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return _list
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