Blocks-Components

- move Timeseries component

gradio/components.py

@@ -1857,3 +1857,92 @@ class Dataframe(Component):
                 + self.type
                 + ". Please choose from: 'pandas', 'numpy', 'array'."
             )
+
+
+class Timeseries(Component):
+    """
+    Component accepts pandas.DataFrame uploaded as a timeseries csv file or as an output.
+
+    Input type: pandas.DataFrame
+    Output type: pandas.DataFrame
+    Demos: fraud_detector
+    """
+
+    def __init__(
+        self,
+        default=None,
+        *,
+        x: Optional[str] = None,
+        y: str | List[str] = None,
+        label: Optional[str] = None,
+        **kwargs,
+    ):
+        """
+        Parameters:
+        default: IGNORED
+        x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
+        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.
+        label (str): component name in interface.
+        """
+        self.x = x
+        if isinstance(y, str):
+            y = [y]
+        self.y = y
+        super().__init__(label=label, **kwargs)
+
+    def get_template_context(self):
+        return {
+            "x": self.x,
+            "y": self.y,
+            **super().get_template_context(),
+        }
+
+    @classmethod
+    def get_shortcut_implementations(cls):
+        return {
+            "timeseries": {},
+        }
+
+    def preprocess_example(self, x):
+        return {"name": x, "is_example": True}
+
+    def preprocess(self, x: Dict | None) -> pd.DataFrame | None:
+        """
+        Parameters:
+        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.
+        Returns:
+        (pandas.DataFrame): Dataframe of timeseries data
+        """
+        if x is None:
+            return x
+        elif x.get("is_example"):
+            dataframe = pd.read_csv(x["name"])
+        else:
+            dataframe = pd.DataFrame(data=x["data"], columns=x["headers"])
+        if x.get("range") is not None:
+            dataframe = dataframe.loc[dataframe[self.x or 0] >= x["range"][0]]
+            dataframe = dataframe.loc[dataframe[self.x or 0] <= x["range"][1]]
+        return dataframe
+
+    def save_flagged(self, dir, label, data, encryption_key):
+        """
+        Returns: (List[List[Union[str, float]]]) 2D array
+        """
+        return json.dumps(data)
+
+    def restore_flagged(self, dir, data, encryption_key):
+        return json.loads(data)
+
+    def generate_sample(self):
+        return {"data": [[1] + [2] * len(self.y)] * 4, "headers": [self.x] + self.y}
+
+    # Output Functionalities
+
+    def postprocess(self, y):
+        """
+        Parameters:
+        y (pandas.DataFrame): timeseries data
+        Returns:
+        (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
+        """
+        return {"headers": y.columns.values.tolist(), "data": y.values.tolist()}

gradio/inputs.py

@@ -31,6 +31,7 @@ from gradio.components import (
     Radio,
     Slider,
     Textbox,
+    Timeseries,
 )
 
 if TYPE_CHECKING:  # Only import for type checking (is False at runtime).
@@ -421,6 +422,10 @@ class Dataframe(Dataframe):
         label (str): component name in interface.
         optional (bool): this parameter is ignored.
         """
+        warnings.warn(
+            "Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
+            DeprecationWarning,
+        )
         super().__init__(
             headers=headers,
             row_count=row_count,
@@ -434,6 +439,34 @@ class Dataframe(Dataframe):
         )
 
 
+class Timeseries(Timeseries):
+    """
+    Component accepts pandas.DataFrame uploaded as a timeseries csv file.
+    Input type: pandas.DataFrame
+    Demos: fraud_detector
+    """
+
+    def __init__(
+        self,
+        x: Optional[str] = None,
+        y: str | List[str] = None,
+        label: Optional[str] = None,
+        optional: bool = False,
+    ):
+        """
+        Parameters:
+        x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
+        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.
+        label (str): component name in interface.
+        optional (bool): If True, the interface can be submitted with no uploaded csv file, in which case the input value is None.
+        """
+        warnings.warn(
+            "Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
+            DeprecationWarning,
+        )
+        super().__init__(x=x, y=y, label=label, optional=optional)
+
+
 class InputComponent(Component):
     """
     Input Component. All input components subclass this.
@@ -517,81 +550,6 @@ class InputComponent(Component):
         }
 
 
-class Timeseries(InputComponent):
-    """
-    Component accepts pandas.DataFrame uploaded as a timeseries csv file.
-    Input type: pandas.DataFrame
-    Demos: fraud_detector
-    """
-
-    def __init__(
-        self,
-        x: Optional[str] = None,
-        y: str | List[str] = None,
-        label: Optional[str] = None,
-        optional: bool = False,
-    ):
-        """
-        Parameters:
-        x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
-        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.
-        label (str): component name in interface.
-        optional (bool): If True, the interface can be submitted with no uploaded csv file, in which case the input value is None.
-        """
-        self.x = x
-        if isinstance(y, str):
-            y = [y]
-        self.y = y
-        super().__init__(label, optional=optional)
-
-    def get_template_context(self):
-        return {
-            "x": self.x,
-            "y": self.y,
-            "optional": self.optional,
-            **super().get_template_context(),
-        }
-
-    @classmethod
-    def get_shortcut_implementations(cls):
-        return {
-            "timeseries": {},
-        }
-
-    def preprocess_example(self, x):
-        return {"name": x, "is_example": True}
-
-    def preprocess(self, x: Dict | None) -> pd.DataFrame | None:
-        """
-        Parameters:
-        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.
-        Returns:
-        (pandas.DataFrame): Dataframe of timeseries data
-        """
-        if x is None:
-            return x
-        elif x.get("is_example"):
-            dataframe = pd.read_csv(x["name"])
-        else:
-            dataframe = pd.DataFrame(data=x["data"], columns=x["headers"])
-        if x.get("range") is not None:
-            dataframe = dataframe.loc[dataframe[self.x or 0] >= x["range"][0]]
-            dataframe = dataframe.loc[dataframe[self.x or 0] <= x["range"][1]]
-        return dataframe
-
-    def save_flagged(self, dir, label, data, encryption_key):
-        """
-        Returns: (List[List[Union[str, float]]]) 2D array
-        """
-        return json.dumps(data)
-
-    def restore_flagged(self, dir, data, encryption_key):
-        return json.loads(data)
-
-    def generate_sample(self):
-        return {"data": [[1] + [2] * len(self.y)] * 4, "headers": [self.x] + self.y}
-
-
 class State(InputComponent):
     """
     Special hidden component that stores state across runs of the interface.

gradio/outputs.py

@@ -21,7 +21,16 @@ import PIL
 from ffmpy import FFmpeg
 
 from gradio import processing_utils
-from gradio.components import Audio, Component, Dataframe, File, Image, Textbox, Video
+from gradio.components import (
+    Audio,
+    Component,
+    Dataframe,
+    File,
+    Image,
+    Textbox,
+    Timeseries,
+    Video,
+)
 
 if TYPE_CHECKING:  # Only import for type checking (is False at runtime).
     from gradio import Interface
@@ -163,6 +172,29 @@ class Dataframe(Dataframe):
         )
 
 
+class Timeseries(Timeseries):
+    """
+    Component accepts pandas.DataFrame.
+    Output type: pandas.DataFrame
+    Demos: fraud_detector
+    """
+
+    def __init__(
+        self, x: str = None, y: str | List[str] = None, label: Optional[str] = None
+    ):
+        """
+        Parameters:
+        x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
+        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.
+        label (str): component name in interface.
+        """
+        warnings.warn(
+            "Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components",
+            DeprecationWarning,
+        )
+        super().__init__(x=x, y=y, label=label)
+
+
 class OutputComponent(Component):
     """
     Output Component. All output components subclass this.
@@ -529,56 +561,6 @@ class Carousel(OutputComponent):
         ]
 
 
-class Timeseries(OutputComponent):
-    """
-    Component accepts pandas.DataFrame.
-    Output type: pandas.DataFrame
-    Demos: fraud_detector
-    """
-
-    def __init__(
-        self, x: str = None, y: str | List[str] = None, label: Optional[str] = None
-    ):
-        """
-        Parameters:
-        x (str): Column name of x (time) series. None if csv has no headers, in which case first column is x series.
-        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.
-        label (str): component name in interface.
-        """
-        self.x = x
-        if isinstance(y, str):
-            y = [y]
-        self.y = y
-        super().__init__(label)
-
-    def get_template_context(self):
-        return {"x": self.x, "y": self.y, **super().get_template_context()}
-
-    @classmethod
-    def get_shortcut_implementations(cls):
-        return {
-            "timeseries": {},
-        }
-
-    def postprocess(self, y):
-        """
-        Parameters:
-        y (pandas.DataFrame): timeseries data
-        Returns:
-        (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
-        """
-        return {"headers": y.columns.values.tolist(), "data": y.values.tolist()}
-
-    def save_flagged(self, dir, label, data, encryption_key):
-        """
-        Returns: (List[List[Union[str, float]]]) 2D array
-        """
-        return json.dumps(data)
-
-    def restore_flagged(self, dir, data, encryption_key):
-        return json.loads(data)
-
-
 class Chatbot(OutputComponent):
     """
     Component displays a chatbot output showing both user submitted messages and responses