from __future__ import annotations
import json
import warnings
from collections.abc import Callable, Sequence, Set
from typing import (
TYPE_CHECKING,
Any,
Literal,
)
import pandas as pd
from gradio_client.documentation import document
from gradio.components.base import Component
from gradio.data_classes import GradioModel
from gradio.events import Events
if TYPE_CHECKING:
from gradio.components import Timer
class PlotData(GradioModel):
columns: list[str]
data: list[list[Any]]
datatypes: dict[str, Literal["quantitative", "nominal", "temporal"]]
mark: str
class NativePlot(Component):
"""
Creates a native Gradio plot component to display data from a pandas DataFrame. Supports interactivity and updates.
Demos: native_plots
"""
EVENTS = [Events.select, Events.double_click]
def __init__(
self,
value: pd.DataFrame | Callable | None = None,
x: str | None = None,
y: str | None = None,
*,
color: str | None = None,
title: str | None = None,
x_title: str | None = None,
y_title: str | None = None,
color_title: str | None = None,
x_bin: str | float | None = None,
y_aggregate: Literal["sum", "mean", "median", "min", "max", "count"]
| None = None,
color_map: dict[str, str] | None = None,
x_lim: list[float] | None = None,
y_lim: list[float] | None = None,
x_label_angle: float = 0,
y_label_angle: float = 0,
x_axis_labels_visible: bool = True,
caption: str | None = None,
sort: Literal["x", "y", "-x", "-y"] | list[str] | None = None,
height: int | None = None,
label: str | None = None,
show_label: bool | None = None,
container: bool = True,
scale: int | None = None,
min_width: int = 160,
every: Timer | float | None = None,
inputs: Component | Sequence[Component] | Set[Component] | None = None,
visible: bool = True,
elem_id: str | None = None,
elem_classes: list[str] | str | None = None,
render: bool = True,
key: int | str | None = None,
**kwargs,
):
"""
Parameters:
value: The pandas dataframe containing the data to display in the plot.
x: Column corresponding to the x axis. Column can be numeric, datetime, or string/category.
y: Column corresponding to the y axis. Column must be numeric.
color: Column corresponding to series, visualized by color. Column must be string/category.
title: The title to display on top of the chart.
x_title: The title given to the x axis. By default, uses the value of the x parameter.
y_title: The title given to the y axis. By default, uses the value of the y parameter.
color_title: The title given to the color legend. By default, uses the value of color parameter.
x_bin: Grouping used to cluster x values. If x column is numeric, should be number to bin the x values. If x column is datetime, should be string such as "1h", "15m", "10s", using "s", "m", "h", "d" suffixes.
y_aggregate: Aggregation function used to aggregate y values, used if x_bin is provided or x is a string/category. Must be one of "sum", "mean", "median", "min", "max".
color_map: Mapping of series to color names or codes. For example, {"success": "green", "fail": "#FF8888"}.
height: The height of the plot in pixels.
x_lim: A tuple or list containing the limits for the x-axis, specified as [x_min, x_max]. If x column is datetime type, x_lim should be timestamps.
y_lim: A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
x_label_angle: The angle of the x-axis labels in degrees offset clockwise.
y_label_angle: The angle of the y-axis labels in degrees offset clockwise.
x_axis_labels_visible: Whether the x-axis labels should be visible. Can be hidden when many x-axis labels are present.
caption: The (optional) caption to display below the plot.
sort: The sorting order of the x values, if x column is type string/category. Can be "x", "y", "-x", "-y", or list of strings that represent the order of the categories.
height: The height of the plot in pixels.
label: The (optional) label to display on the top left corner of the plot.
show_label: Whether the label should be displayed.
container: If True, will place the component in a container - providing some extra padding around the border.
scale: relative size compared to adjacent Components. For example if Components A and B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide as B. Should be an integer. scale applies in Rows, and to top-level Components in Blocks where fill_height=True.
min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
every: Continously calls `value` to recalculate it if `value` is a function (has no effect otherwise). Can provide a Timer whose tick resets `value`, or a float that provides the regular interval for the reset Timer.
inputs: Components that are used as inputs to calculate `value` if `value` is a function (has no effect otherwise). `value` is recalculated any time the inputs change.
visible: Whether the plot should be visible.
elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
render: If False, component will not render be rendered in the Blocks context. Should be used if the intention is to assign event listeners now but render the component later.
key: if assigned, will be used to assume identity across a re-render. Components that have the same key across a re-render will have their value preserved.
"""
self.x = x
self.y = y
self.color = color
self.title = title
self.x_title = x_title
self.y_title = y_title
self.color_title = color_title
self.x_bin = x_bin
self.y_aggregate = y_aggregate
self.color_map = color_map
self.x_lim = x_lim
self.y_lim = y_lim
self.x_label_angle = x_label_angle
self.y_label_angle = y_label_angle
self.x_axis_labels_visible = x_axis_labels_visible
self.caption = caption
self.sort = sort
self.height = height
if label is None and show_label is None:
show_label = False
super().__init__(
value=value,
label=label,
show_label=show_label,
container=container,
scale=scale,
min_width=min_width,
visible=visible,
elem_id=elem_id,
elem_classes=elem_classes,
render=render,
key=key,
every=every,
inputs=inputs,
)
for key, val in kwargs.items():
if key == "color_legend_title":
self.color_title = val
if key in [
"stroke_dash",
"overlay_point",
"tooltip",
"x_label_angle",
"y_label_angle",
"interactive",
"show_actions_button",
"color_legend_title",
"width",
]:
warnings.warn(
f"Argument '{key}' has been deprecated.", DeprecationWarning
)
def get_block_name(self) -> str:
return "nativeplot"
def get_mark(self) -> str:
return "native"
def preprocess(self, payload: PlotData | None) -> PlotData | None:
"""
Parameters:
payload: The data to display in a line plot.
Returns:
The data to display in a line plot.
"""
return payload
def postprocess(self, value: pd.DataFrame | dict | None) -> PlotData | None:
"""
Parameters:
value: Expects a pandas DataFrame containing the data to display in the line plot. The DataFrame should contain at least two columns, one for the x-axis (corresponding to this component's `x` argument) and one for the y-axis (corresponding to `y`).
Returns:
The data to display in a line plot, in the form of an AltairPlotData dataclass, which includes the plot information as a JSON string, as well as the type of plot (in this case, "line").
"""
# if None or update
if value is None or isinstance(value, dict):
return value
def get_simplified_type(dtype):
if pd.api.types.is_numeric_dtype(dtype):
return "quantitative"
elif pd.api.types.is_string_dtype(
dtype
) or pd.api.types.is_categorical_dtype(dtype):
return "nominal"
elif pd.api.types.is_datetime64_any_dtype(dtype):
return "temporal"
else:
raise ValueError(f"Unsupported data type: {dtype}")
split_json = json.loads(value.to_json(orient="split", date_unit="ms"))
datatypes = {
col: get_simplified_type(value[col].dtype) for col in value.columns
}
return PlotData(
columns=split_json["columns"],
data=split_json["data"],
datatypes=datatypes,
mark=self.get_mark(),
)
def example_payload(self) -> Any:
return None
def example_value(self) -> Any:
import pandas as pd
return pd.DataFrame({self.x: [1, 2, 3], self.y: [4, 5, 6]})
def api_info(self) -> dict[str, Any]:
return {"type": {}, "description": "any valid json"}
@document()
class BarPlot(NativePlot):
"""
Creates a bar plot component to display data from a pandas DataFrame.
Demos: bar_plot_demo
"""
def get_block_name(self) -> str:
return "nativeplot"
def get_mark(self) -> str:
return "bar"
@document()
class LinePlot(NativePlot):
"""
Creates a line plot component to display data from a pandas DataFrame.
Demos: line_plot_demo
"""
def get_block_name(self) -> str:
return "nativeplot"
def get_mark(self) -> str:
return "line"
@document()
class ScatterPlot(NativePlot):
"""
Creates a scatter plot component to display data from a pandas DataFrame.
Demos: scatter_plot_demo
"""
def get_block_name(self) -> str:
return "nativeplot"
def get_mark(self) -> str:
return "point"