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Blocks-Components
- move Timeseries component
This commit is contained in:
Ömer Faruk Özdemir
parent
fbca310ccb
commit
7c50d2600d
@ -1857,3 +1857,92 @@ class Dataframe(Component):
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+ self.type
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+ ". Please choose from: 'pandas', 'numpy', 'array'."
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)
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class Timeseries(Component):
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"""
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Component accepts pandas.DataFrame uploaded as a timeseries csv file or as an output.
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Input type: pandas.DataFrame
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Output type: pandas.DataFrame
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Demos: fraud_detector
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"""
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def __init__(
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self,
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default=None,
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*,
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x: Optional[str] = None,
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y: str | List[str] = None,
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label: Optional[str] = None,
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**kwargs,
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):
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"""
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Parameters:
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default: IGNORED
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x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
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y (Union[str, List[str]]): Column name of y series, or list of column names if multiple series. None if csv has no headers, in which case every column after first is a y series.
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label (str): component name in interface.
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"""
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self.x = x
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if isinstance(y, str):
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y = [y]
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self.y = y
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super().__init__(label=label, **kwargs)
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def get_template_context(self):
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return {
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"x": self.x,
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"y": self.y,
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**super().get_template_context(),
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}
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@classmethod
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def get_shortcut_implementations(cls):
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return {
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"timeseries": {},
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}
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def preprocess_example(self, x):
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return {"name": x, "is_example": True}
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def preprocess(self, x: Dict | None) -> pd.DataFrame | None:
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"""
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Parameters:
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x (Dict[data: List[List[Union[str, number, bool]]], headers: List[str], range: List[number]]): Dict with keys 'data': 2D array of str, numeric, or bool data, 'headers': list of strings for header names, 'range': optional two element list designating start of end of subrange.
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Returns:
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(pandas.DataFrame): Dataframe of timeseries data
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"""
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if x is None:
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return x
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elif x.get("is_example"):
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dataframe = pd.read_csv(x["name"])
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else:
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dataframe = pd.DataFrame(data=x["data"], columns=x["headers"])
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if x.get("range") is not None:
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dataframe = dataframe.loc[dataframe[self.x or 0] >= x["range"][0]]
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dataframe = dataframe.loc[dataframe[self.x or 0] <= x["range"][1]]
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return dataframe
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def save_flagged(self, dir, label, data, encryption_key):
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"""
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Returns: (List[List[Union[str, float]]]) 2D array
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"""
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return json.dumps(data)
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def restore_flagged(self, dir, data, encryption_key):
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return json.loads(data)
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def generate_sample(self):
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return {"data": [[1] + [2] * len(self.y)] * 4, "headers": [self.x] + self.y}
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# Output Functionalities
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def postprocess(self, y):
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"""
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Parameters:
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y (pandas.DataFrame): timeseries data
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Returns:
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(Dict[headers: List[str], data: List[List[Union[str, number]]]]): JSON object with key 'headers' for list of header names, 'data' for 2D array of string or numeric data
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"""
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return {"headers": y.columns.values.tolist(), "data": y.values.tolist()}
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108
gradio/inputs.py
108
gradio/inputs.py
@ -31,6 +31,7 @@ from gradio.components import (
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Radio,
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Slider,
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Textbox,
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Timeseries,
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)
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if TYPE_CHECKING: # Only import for type checking (is False at runtime).
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@ -421,6 +422,10 @@ class Dataframe(Dataframe):
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label (str): component name in interface.
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optional (bool): this parameter is ignored.
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"""
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warnings.warn(
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"Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
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DeprecationWarning,
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)
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super().__init__(
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headers=headers,
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row_count=row_count,
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@ -434,6 +439,34 @@ class Dataframe(Dataframe):
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)
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class Timeseries(Timeseries):
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"""
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Component accepts pandas.DataFrame uploaded as a timeseries csv file.
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Input type: pandas.DataFrame
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Demos: fraud_detector
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"""
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def __init__(
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self,
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x: Optional[str] = None,
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y: str | List[str] = None,
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label: Optional[str] = None,
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optional: bool = False,
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):
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"""
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Parameters:
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x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
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y (Union[str, List[str]]): Column name of y series, or list of column names if multiple series. None if csv has no headers, in which case every column after first is a y series.
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label (str): component name in interface.
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optional (bool): If True, the interface can be submitted with no uploaded csv file, in which case the input value is None.
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"""
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warnings.warn(
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"Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
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DeprecationWarning,
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)
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super().__init__(x=x, y=y, label=label, optional=optional)
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class InputComponent(Component):
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"""
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Input Component. All input components subclass this.
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@ -517,81 +550,6 @@ class InputComponent(Component):
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}
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class Timeseries(InputComponent):
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"""
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Component accepts pandas.DataFrame uploaded as a timeseries csv file.
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Input type: pandas.DataFrame
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Demos: fraud_detector
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"""
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def __init__(
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self,
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x: Optional[str] = None,
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y: str | List[str] = None,
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label: Optional[str] = None,
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optional: bool = False,
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):
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"""
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Parameters:
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x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
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y (Union[str, List[str]]): Column name of y series, or list of column names if multiple series. None if csv has no headers, in which case every column after first is a y series.
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label (str): component name in interface.
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optional (bool): If True, the interface can be submitted with no uploaded csv file, in which case the input value is None.
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"""
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self.x = x
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if isinstance(y, str):
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y = [y]
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self.y = y
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super().__init__(label, optional=optional)
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def get_template_context(self):
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return {
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"x": self.x,
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"y": self.y,
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"optional": self.optional,
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**super().get_template_context(),
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}
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@classmethod
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def get_shortcut_implementations(cls):
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return {
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"timeseries": {},
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}
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def preprocess_example(self, x):
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return {"name": x, "is_example": True}
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def preprocess(self, x: Dict | None) -> pd.DataFrame | None:
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"""
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Parameters:
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x (Dict[data: List[List[Union[str, number, bool]]], headers: List[str], range: List[number]]): Dict with keys 'data': 2D array of str, numeric, or bool data, 'headers': list of strings for header names, 'range': optional two element list designating start of end of subrange.
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Returns:
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(pandas.DataFrame): Dataframe of timeseries data
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"""
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if x is None:
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return x
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elif x.get("is_example"):
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dataframe = pd.read_csv(x["name"])
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else:
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dataframe = pd.DataFrame(data=x["data"], columns=x["headers"])
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if x.get("range") is not None:
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dataframe = dataframe.loc[dataframe[self.x or 0] >= x["range"][0]]
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dataframe = dataframe.loc[dataframe[self.x or 0] <= x["range"][1]]
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return dataframe
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def save_flagged(self, dir, label, data, encryption_key):
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"""
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Returns: (List[List[Union[str, float]]]) 2D array
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"""
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return json.dumps(data)
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def restore_flagged(self, dir, data, encryption_key):
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return json.loads(data)
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def generate_sample(self):
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return {"data": [[1] + [2] * len(self.y)] * 4, "headers": [self.x] + self.y}
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class State(InputComponent):
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"""
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Special hidden component that stores state across runs of the interface.
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@ -21,7 +21,16 @@ import PIL
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from ffmpy import FFmpeg
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from gradio import processing_utils
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from gradio.components import Audio, Component, Dataframe, File, Image, Textbox, Video
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from gradio.components import (
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Audio,
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Component,
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Dataframe,
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File,
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Image,
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Textbox,
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Timeseries,
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Video,
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)
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if TYPE_CHECKING: # Only import for type checking (is False at runtime).
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from gradio import Interface
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@ -163,6 +172,29 @@ class Dataframe(Dataframe):
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)
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class Timeseries(Timeseries):
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"""
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Component accepts pandas.DataFrame.
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Output type: pandas.DataFrame
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Demos: fraud_detector
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"""
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def __init__(
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self, x: str = None, y: str | List[str] = None, label: Optional[str] = None
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):
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"""
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Parameters:
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x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
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y (Union[str, List[str]]): Column name of y series, or list of column names if multiple series. None if csv has no headers, in which case every column after first is a y series.
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label (str): component name in interface.
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"""
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warnings.warn(
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"Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
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DeprecationWarning,
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)
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super().__init__(x=x, y=y, label=label)
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class OutputComponent(Component):
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"""
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Output Component. All output components subclass this.
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@ -529,56 +561,6 @@ class Carousel(OutputComponent):
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]
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class Timeseries(OutputComponent):
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"""
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Component accepts pandas.DataFrame.
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Output type: pandas.DataFrame
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Demos: fraud_detector
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"""
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def __init__(
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self, x: str = None, y: str | List[str] = None, label: Optional[str] = None
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):
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"""
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Parameters:
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x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
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y (Union[str, List[str]]): Column name of y series, or list of column names if multiple series. None if csv has no headers, in which case every column after first is a y series.
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label (str): component name in interface.
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"""
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self.x = x
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if isinstance(y, str):
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y = [y]
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self.y = y
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super().__init__(label)
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def get_template_context(self):
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return {"x": self.x, "y": self.y, **super().get_template_context()}
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@classmethod
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def get_shortcut_implementations(cls):
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return {
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"timeseries": {},
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}
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def postprocess(self, y):
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"""
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Parameters:
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y (pandas.DataFrame): timeseries data
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Returns:
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(Dict[headers: List[str], data: List[List[Union[str, number]]]]): JSON object with key 'headers' for list of header names, 'data' for 2D array of string or numeric data
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"""
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return {"headers": y.columns.values.tolist(), "data": y.values.tolist()}
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def save_flagged(self, dir, label, data, encryption_key):
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"""
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Returns: (List[List[Union[str, float]]]) 2D array
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"""
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return json.dumps(data)
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def restore_flagged(self, dir, data, encryption_key):
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return json.loads(data)
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class Chatbot(OutputComponent):
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"""
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Component displays a chatbot output showing both user submitted messages and responses
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