Small fixes for multiple demos compatible with 3.0 (#1257)

* add required param but None * import torch req, add chunk_length_s import torch requirement for transformers enable inference for longer audio files * fix compononte initialization * input number is float, force int to multipy string * no need for Templates, fix class init * expects array * add requirements.txt for demo * update with cleaner syntax * add sample csv to fraud demo * adapt to new syntax * temp fix for Slider arguments * add dep to requirements * remove gr.Markdown from Interface init * fix default value param name * upgrade deepspeech, download models onstart * use path resolution consistent with other demos * remove redundant demo * add example to interface * fixed plotting issues * plots * deprecated carousel Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
2025-03-31 12:20:26 +08:00 · 2022-05-13 22:45:44 -07:00 · 2022-05-13 22:45:44 -07:00 · 73e98ddf15
commit 73e98ddf15
parent 63d0a28c08
30 changed files with 79 additions and 407 deletions
--- a/demo/blocks_kinematics/run.py
+++ b/demo/blocks_kinematics/run.py
@ -1,3 +1,5 @@
+import matplotlib
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 import numpy as np

@ -28,8 +30,8 @@ with demo:
    )

    with gr.Row():
-        speed = gr.Slider(25, min=1, max=30, label="Speed")
-        angle = gr.Slider(45, min=0, max=90, label="Angle")
+        speed = gr.Slider(25, minimum=1, maximum=30, label="Speed")
+        angle = gr.Slider(45, minimum=0, maximum=90, label="Angle")
    output = gr.Image(type="plot")
    btn = gr.Button("Run")
    btn.click(plot, [speed, angle], output)
--- a/demo/blocks_outputs/run.py
+++ b/demo/blocks_outputs/run.py
@ -40,9 +40,6 @@ with gr.Blocks() as demo:
        gr.Dataframe(
            interactive=True, headers=["One", "Two", "Three", "Four"], col_count=4
        )
-        # layout components are static only
-        # carousel doesn't work like other components
-        # carousel = gr.Carousel()


 if __name__ == "__main__":
--- a/demo/blocks_speech_text_length/requirements.txt
+++ b/demo/blocks_speech_text_length/requirements.txt
@ -0,0 +1,2 @@
+torch
+transformers
--- a/demo/blocks_speech_text_length/run.py
+++ b/demo/blocks_speech_text_length/run.py
@ -1,18 +1,18 @@
-# from transformers import pipeline
+from transformers import pipeline

 import gradio as gr

-# asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-# classifier = pipeline("text-classification")
+asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
+classifier = pipeline("text-classification")


-# def speech_to_text(speech):
-#     text = asr(speech)["text"]
-#     return text
+def speech_to_text(speech):
+    text = asr(speech)["text"]
+    return text


-# def text_to_sentiment(text):
-#     return classifier(text)[0]["label"]
+def text_to_sentiment(text):
+    return classifier(text)[0]["label"]


 demo = gr.Blocks()
@ -25,8 +25,8 @@ with demo:
    b1 = gr.Button("Recognize Speech")
    b2 = gr.Button("Classify Sentiment")

-    # b1.click(speech_to_text, inputs=audio_file, outputs=text)
-    # b2.click(text_to_sentiment, inputs=text, outputs=label)
+    b1.click(speech_to_text, inputs=audio_file, outputs=text)
+    b2.click(text_to_sentiment, inputs=text, outputs=label)

 if __name__ == "__main__":
    demo.launch()
--- a/demo/blocks_textbox_max_lines/run.py
+++ b/demo/blocks_textbox_max_lines/run.py
@ -1,12 +1,12 @@
 import gradio as gr


-def greet(name: str, repeat: int):
-    return "Hello " + name * repeat + "!!"
+def greet(name: str, repeat: float):
+    return "Hello " + name * int(repeat) + "!!"


 demo = gr.Interface(
-    fn=greet, inputs=[gr.Textbox(lines=2, max_lines=4), gr.Number()], outputs=gr.component("textarea")
+    fn=greet, inputs=[gr.Textbox(lines=2, max_lines=4), gr.Number()], outputs="textarea"
 )

 if __name__ == "__main__":
--- a/demo/blocks_webcam/run.py
+++ b/demo/blocks_webcam/run.py
@ -7,7 +7,7 @@ def snap(image):
    return np.flipud(image)


-demo = gr.Interface(snap, gr.component("webcam"), gr.component("image"))
+demo = gr.Interface(snap, "webcam", "image")

 if __name__ == "__main__":
    demo.launch()
--- a/demo/blocks_xray/run.py
+++ b/demo/blocks_xray/run.py
@ -5,12 +5,12 @@ import time

 def xray_model(diseases, img):
    time.sleep(4)
-    return {disease: random.random() for disease in diseases}
+    return [{disease: random.random() for disease in diseases}]


 def ct_model(diseases, img):
    time.sleep(3)
-    return {disease: 0.1 for disease in diseases}
+    return [{disease: 0.1 for disease in diseases}]


 with gr.Blocks() as demo:
--- a/demo/disease_report/requirements.txt
+++ b/demo/disease_report/requirements.txt
@ -1 +0,0 @@
-fpdf
--- a/demo/disease_report/run.py
+++ b/demo/disease_report/run.py
@ -1,49 +0,0 @@
-import os
-import tempfile
-
-import numpy as np
-from fpdf import FPDF
-
-import gradio as gr
-
-
-def disease_report(img, scan_for, generate_report):
-    results = []
-    for i, mode in enumerate(["Red", "Green", "Blue"]):
-        color_filter = np.array([0, 0, 0])
-        color_filter[i] = 1
-        results.append([mode, img * color_filter])
-    tmp_dir = tempfile.gettempdir()
-    report = os.path.join(tmp_dir, "report.pdf")
-    if generate_report:
-        pdf = FPDF()
-        pdf.add_page()
-        pdf.set_font("Arial", size=15)
-        pdf.cell(200, 10, txt="Disease Report", ln=1, align="C")
-        pdf.cell(200, 10, txt="A Gradio Demo.", ln=2, align="C")
-        pdf.output(report)
-    return results, report if generate_report else None
-
-
-demo = gr.Interface(
-    disease_report,
-    [
-        "image",
-        gr.CheckboxGroup(
-            ["Cancer", "Rash", "Heart Failure", "Stroke", "Diabetes", "Pneumonia"]
-        ),
-        "checkbox",
-    ],
-    [
-        gr.Carousel(["text", "image"], label="Disease"),
-        gr.File(label="Report"),
-    ],
-    title="Disease Report",
-    description="Upload an Xray and select the diseases to scan for.",
-    theme="grass",
-    flagging_options=["good", "bad", "etc"],
-    allow_flagging="auto",
-)
-
-if __name__ == "__main__":
-    demo.launch()
--- a/demo/disease_report/screenshot.png
+++ b/demo/disease_report/screenshot.png
--- a/demo/fake_gan_complex/run.py
+++ b/demo/fake_gan_complex/run.py
@ -1,43 +0,0 @@
-# This demo needs to be run from the repo folder.
-# python demo/fake_gan/run.py
-import random
-import time
-
-import gradio as gr
-
-
-def fake_gan(*args):
-    time.sleep(1)
-    image = random.choice(
-        [
-            "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
-            "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
-            "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
-            "https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
-            "https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80",
-        ]
-    )
-    return image
-
-
-demo = gr.Interface(
-    fn=fake_gan,
-    inputs=[
-        gr.Image(label="Initial Image (optional)"),
-        gr.Markdown("**Parameters**"),
-        gr.Slider(25, minimum=0, maximum=50, label="TV_scale (for smoothness)"),
-        gr.Slider(25, minimum=0, maximum=50, label="Range_Scale (out of range RBG)"),
-        gr.Number(label="Respacing"),
-        gr.Markdown("**Parameters Two**"),
-        gr.Slider(25, minimum=0, maximum=50, label="Range_Scale (out of range RBG)"),
-        gr.Number(label="Respacing"),
-        gr.Markdown("**Parameters Three**"),
-        gr.Textbox(label="Respacing"),
-    ],
-    outputs=gr.Image(label="Generated Image"),
-    title="FD-GAN",
-    description="This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.",
-)
-
-if __name__ == "__main__":
-    demo.launch()
--- a/demo/form_graph/requirements.txt
+++ b/demo/form_graph/requirements.txt
@ -1 +0,0 @@
-matplotlib
--- a/demo/form_graph/run.py
+++ b/demo/form_graph/run.py
@ -1,38 +0,0 @@
-import matplotlib.pyplot as plt
-import numpy as np
-
-import gradio as gr
-
-
-def plot_forecast(final_year, companies, noise, show_legend, point_style):
-    start_year = 2020
-    x = np.arange(start_year, final_year + 1)
-    year_count = x.shape[0]
-    plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style]
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-    for i, company in enumerate(companies):
-        series = np.arange(0, year_count, dtype=float)
-        series = series**2 * (i + 1)
-        series += np.random.rand(year_count) * noise
-        ax.plot(x, series, plt_format)
-    if show_legend:
-        plt.legend(companies)
-    plt.close()
-    return fig
-
-
-demo = gr.Interface(
-    plot_forecast,
-    [
-        gr.Radio([2025, 2030, 2035, 2040], label="Project to:"),
-        gr.CheckboxGroup(["Google", "Microsoft", "Gradio"], label="Company Selection"),
-        gr.Slider(minimum=1, maximum=100, label="Noise Level"),
-        gr.Checkbox(label="Show Legend"),
-        gr.Dropdown(["cross", "line", "circle"], label="Style"),
-    ],
-    gr.Image(plot=True, label="forecast"),
-)
-
-if __name__ == "__main__":
-    demo.launch()
--- a/demo/fraud_detector/fraud.csv
+++ b/demo/fraud_detector/fraud.csv
@ -0,0 +1,11 @@
+time,retail,food,other
+0,109,145,86
+1,35,87,43
+2,49,117,34
+3,127,66,17
+4,39,82,17
+5,101,56,79
+6,100,129,67
+7,17,88,97
+8,76,85,145
+9,111,106,35
--- a/demo/fraud_detector/run.py
+++ b/demo/fraud_detector/run.py
@ -1,5 +1,5 @@
 import random
-
+import os
 import gradio as gr


@ -31,6 +31,9 @@ demo = gr.Interface(
        gr.Timeseries(x="time", y=["retail", "food", "other"]),
        gr.Label(label="Fraud Level"),
    ],
+    examples=[
+        [os.path.join(os.path.dirname(__file__), "fraud.csv"), ["retail", "food", "other"], 1.0],
+    ],
 )
 if __name__ == "__main__":
    demo.launch()
--- a/demo/generate_tone/run.py
+++ b/demo/generate_tone/run.py
@ -18,9 +18,9 @@ def generate_tone(note, octave, duration):
 demo = gr.Interface(
    generate_tone,
    [
-        gr.inputs.Dropdown(notes, type="index"),
-        gr.inputs.Slider(4, 6, step=1),
-        gr.inputs.Textbox(type="number", default=1, label="Duration in seconds"),
+        gr.Dropdown(notes, type="index"),
+        gr.Slider(value=4, minimum=4, maximum=6, step=1),
+        gr.Textbox(value=1, type="number", label="Duration in seconds"),
    ],
    "audio",
 )
--- a/demo/kitchen_sink/run.py
+++ b/demo/kitchen_sink/run.py
@ -80,17 +80,6 @@ def fn(
        os.path.join(os.path.dirname(__file__), "files/titanic.csv"),
        df1,  # Dataframe
        np.random.randint(0, 10, (4, 4)),  # Dataframe
-        [
-            im
-            for im in [
-                im1,
-                im2,
-                im3,
-                im4,
-                os.path.join(os.path.dirname(__file__), "files/cheetah1.jpg"),
-            ]
-            if im is not None
-        ],  # Carousel
        df2,  # Timeseries
    )

@ -135,7 +124,6 @@ demo = gr.Interface(
        gr.File(label="File"),
        gr.Dataframe(label="Dataframe"),
        gr.Dataframe(label="Numpy"),
-        gr.Carousel(components="image", label="Carousel"),
        gr.Timeseries(x="time", y=["price", "value"], label="Timeseries"),
    ],
    examples=[
--- a/demo/model3D/run.py
+++ b/demo/model3D/run.py
@ -1,18 +1,27 @@
 import time
 import gradio as gr
+import os
+

 def load_mesh(mesh_file_name):
    time.sleep(2)
    return mesh_file_name

+
 inputs = gr.Model3D()
 outputs = gr.Model3D(clear_color=[0.8, 0.2, 0.2, 1.0])

 demo = gr.Interface(
-    fn=load_mesh, 
-    inputs=inputs, 
+    fn=load_mesh,
+    inputs=inputs,
    outputs=outputs,
-    examples=[["files/Bunny.obj"], ["files/Duck.glb"], ["files/Fox.gltf"],["files/face.obj"]], cache_examples=True
+    examples=[
+        [os.path.join(os.path.dirname(__file__), "files/Bunny.obj")],
+        [os.path.join(os.path.dirname(__file__), "files/Duck.glb")],
+        [os.path.join(os.path.dirname(__file__), "files/Fox.gltf")],
+        [os.path.join(os.path.dirname(__file__), "files/face.obj")],
+    ],
+    cache_examples=True,
 )

 if __name__ == "__main__":
--- a/demo/no_input/run.py
+++ b/demo/no_input/run.py
@ -12,5 +12,5 @@ def random_sentence():
    return sentence_list[random.randint(0, 2)]


-demo = gr.Interface(fn=random_sentence, outputs="text")
+demo = gr.Interface(fn=random_sentence, inputs=None, outputs="text")
 demo.launch()
--- a/demo/outbreak_forecast/requirements.txt
+++ b/demo/outbreak_forecast/requirements.txt
@ -1,3 +1,4 @@
 numpy
 matplotlib
 bokeh
+plotly
--- a/demo/outbreak_forecast/run.py
+++ b/demo/outbreak_forecast/run.py
@ -57,7 +57,7 @@ inputs = [
                         value=["USA", "Canada"]),
        gr.Checkbox(label="Social Distancing?"),
    ]
-outputs = gr.Plot(type="auto")
+outputs = gr.Plot()

 demo = gr.Interface(fn=outbreak, inputs=inputs, outputs=outputs)

--- a/demo/sales_projections/run.py
+++ b/demo/sales_projections/run.py
@ -28,7 +28,7 @@ demo = gr.Interface(
    sales_projections,
    gr.Dataframe(
        headers=["Name", "Jan Sales", "Feb Sales", "Mar Sales"],
-        default=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]],
+        value=[["Jon", 12, 14, 18], ["Alice", 14, 17, 2], ["Sana", 8, 9.5, 12]],
    ),
    ["dataframe", "plot", "numpy"],
    description="Enter sales figures for employees to predict sales trajectory over year.",
--- a/demo/stock_forecast/run.py
+++ b/demo/stock_forecast/run.py
@ -1,10 +1,12 @@
+import matplotlib
+matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 import numpy as np

 import gradio as gr


-def stock_forecast(final_year, companies, noise, show_legend, point_style):
+def plot_forecast(final_year, companies, noise, show_legend, point_style):
    start_year = 2020
    x = np.arange(start_year, final_year + 1)
    year_count = x.shape[0]
@ -18,20 +20,19 @@ def stock_forecast(final_year, companies, noise, show_legend, point_style):
        ax.plot(x, series, plt_format)
    if show_legend:
        plt.legend(companies)
-    plt.close()
    return fig


 demo = gr.Interface(
-    stock_forecast,
+    plot_forecast,
    [
        gr.Radio([2025, 2030, 2035, 2040], label="Project to:"),
-        gr.CheckboxGroup(["Google", "Microsoft", "Gradio"]),
-        gr.Slider(minimum=1, maximum=100),
-        "checkbox",
+        gr.CheckboxGroup(["Google", "Microsoft", "Gradio"], label="Company Selection"),
+        gr.Slider(minimum=1, maximum=100, label="Noise Level"),
+        gr.Checkbox(label="Show Legend"),
        gr.Dropdown(["cross", "line", "circle"], label="Style"),
    ],
-    gr.Image(plot=True, label="forecast"),
+    gr.Plot(label="forecast"),
 )

 if __name__ == "__main__":
--- a/demo/streaming_stt/requirements.txt
+++ b/demo/streaming_stt/requirements.txt
@ -1 +1 @@
-deepspeech==0.8.2
+deepspeech==0.9.3
--- a/demo/streaming_stt/run.py
+++ b/demo/streaming_stt/run.py
@ -1,9 +1,15 @@
 from deepspeech import Model
 import gradio as gr
 import numpy as np
+import urllib.request 
+
+model_file_path = "deepspeech-0.9.3-models.pbmm"
+lm_file_path = "deepspeech-0.9.3-models.scorer"
+url = "https://github.com/mozilla/DeepSpeech/releases/download/v0.9.3/"
+
+urllib.request.urlretrieve(url + model_file_path, filename=model_file_path)
+urllib.request.urlretrieve(url + lm_file_path, filename=lm_file_path)

-model_file_path = "deepspeech-0.8.2-models.pbmm"
-lm_file_path = "deepspeech-0.8.2-models.scorer"
 beam_width = 100
 lm_alpha = 0.93
 lm_beta = 1.18
@ -42,4 +48,4 @@ def transcribe(speech, stream):
 demo = gr.Interface(transcribe, ["microphone", "state"], ["text", "state"], live=True)

 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(debug=True)
--- a/gradio/init.py
+++ b/gradio/init.py
@ -11,7 +11,6 @@ from gradio.components import (
    JSON,
    Audio,
    Button,
-    Carousel,
    Chatbot,
    Checkbox,
    Checkboxgroup,
--- a/gradio/components.py
+++ b/gradio/components.py
@ -3033,111 +3033,6 @@ class Gallery(IOComponent):
        )


-# max_grid=[3], grid_behavior="scale", height="auto"
-class Carousel(IOComponent):
-    """
-    Used to display a list of arbitrary components that can be scrolled through.
-    Preprocessing: this component does *not* accept input.
-    Postprocessing: Expects a nested {List[List]} where the inner elements depend on the components in the Carousel.
-
-    Demos: disease_report
-    """
-
-    def __init__(
-        self,
-        *,
-        components: Component | List[Component],
-        label: Optional[str] = None,
-        show_label: bool = True,
-        visible: bool = True,
-        elem_id: Optional[str] = None,
-        **kwargs,
-    ):
-        """
-        Parameters:
-        components (Union[List[OutputComponent], OutputComponent]): Classes of component(s) that will be scrolled through.
-        label (Optional[str]): component name in interface.
-        show_label (bool): if True, will display label.
-        visible (bool): If False, component will be hidden.
-        """
-        if not isinstance(components, list):
-            components = [components]
-        self.components = [
-            get_component_instance(component) for component in components
-        ]
-        IOComponent.__init__(
-            self,
-            label=label,
-            show_label=show_label,
-            visible=visible,
-            elem_id=elem_id,
-            **kwargs,
-        )
-
-    def get_config(self):
-        return {
-            "components": [component.get_config() for component in self.components],
-            **IOComponent.get_config(self),
-        }
-
-    @staticmethod
-    def update(
-        value: Optional[Any] = None,
-        label: Optional[str] = None,
-        show_label: Optional[bool] = None,
-        visible: Optional[bool] = None,
-    ):
-        return {
-            "label": label,
-            "show_label": show_label,
-            "visible": visible,
-            "value": value,
-            "__type__": "update",
-        }
-
-    def postprocess(self, y):
-        """
-        Parameters:
-        y (List[List[Any]]): carousel output
-        Returns:
-        (List[List[Any]]): 2D array, where each sublist represents one set of outputs or 'slide' in the carousel
-        """
-        if isinstance(y, list):
-            if len(y) != 0 and not isinstance(y[0], list):
-                y = [[z] for z in y]
-            output = []
-            for row in y:
-                output_row = []
-                for i, cell in enumerate(row):
-                    output_row.append(self.components[i].postprocess(cell))
-                output.append(output_row)
-            return output
-        else:
-            raise ValueError("Unknown type. Please provide a list for the Carousel.")
-
-    def save_flagged(self, dir, label, data, encryption_key):
-        return json.dumps(
-            [
-                [
-                    component.save_flagged(
-                        dir, f"{label}_{j}", data[i][j], encryption_key
-                    )
-                    for j, component in enumerate(self.components)
-                ]
-                for i, _ in enumerate(data)
-            ]
-        )
-
-    def restore_flagged(self, dir, data, encryption_key):
-        return [
-            [
-                component.restore_flagged(dir, sample, encryption_key)
-                for component, sample in zip(self.components, sample_set)
-            ]
-            for sample_set in json.loads(data)
-        ]
-
-
 class Chatbot(Changeable, IOComponent):
    """
    Displays a chatbot output showing both user submitted messages and responses
@ -3341,7 +3236,7 @@ class Plot(Changeable, Clearable, IOComponent):
    Preprocessing: this component does *not* accept input.
    Postprocessing: expects either a {matplotlib.pyplot.Figure}, a {plotly.graph_objects._figure.Figure}, or a {dict} corresponding to a bokeh plot (json_item format)

-    Demos: outbreak_forecast, blocks_kinematics
+    Demos: outbreak_forecast, blocks_kinematics, stock_forecast
    """

    def __init__(
@ -3394,7 +3289,6 @@ class Plot(Changeable, Clearable, IOComponent):
        (str): plot type
        (str): plot base64 or json
        """
-        dtype = self.type
        if isinstance(y, (ModuleType, matplotlib.pyplot.Figure)):
            dtype = "matplotlib"
            out_y = processing_utils.encode_plot_to_base64(y)
--- a/gradio/outputs.py
+++ b/gradio/outputs.py
@ -12,7 +12,6 @@ from typing import Dict, List, Optional
 from gradio.components import HTML as C_HTML
 from gradio.components import JSON as C_JSON
 from gradio.components import Audio as C_Audio
-from gradio.components import Carousel as C_Carousel
 from gradio.components import Chatbot as C_Chatbot
 from gradio.components import Component as Component
 from gradio.components import Dataframe as C_Dataframe
@ -311,7 +310,7 @@ class HTML(C_HTML):
        super().__init__(label=label)


-class Carousel(C_Carousel):
+class Carousel:
    """
    Component displays a set of output components that can be scrolled through.
    Output type: List[List[Any]]
@ -328,11 +327,10 @@ class Carousel(C_Carousel):
        components (Union[List[OutputComponent], OutputComponent]): Classes of component(s) that will be scrolled through.
        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,
+        raise NotImplementedError(
+            "The Carousel component has not been implemented in Gradio 3.0. Please "
+            "consider using the Gallery component instead."
        )
-        super().__init__(components=components, label=label)


 class Chatbot(C_Chatbot):
--- a/gradio/templates.py
+++ b/gradio/templates.py
@ -47,16 +47,6 @@ class Sketchpad(components.Image):
        )


-class Plot(components.Image):
-    def __init__(self, **kwargs):
-        """
-        Custom component
-        @param kwargs:
-        """
-        self.is_template = True
-        super().__init__(type="plot", **kwargs)
-
-
 class Pil(components.Image):
    def __init__(self, **kwargs):
        """
--- a/test/test_components.py
+++ b/test/test_components.py
@ -1578,102 +1578,5 @@ class TestHTML(unittest.TestCase):
        self.assertEqual(iface.process(["test"])[0], "<strong>test</strong>")


-class TestCarousel(unittest.TestCase):
-    def test_component_functions(self):
-        """
-        Postprocess, get_config, save_flagged, restore_flagged
-        """
-        carousel_output = gr.Carousel(
-            components=[gr.Textbox(), gr.Image()], label="Disease"
-        )
-
-        output = carousel_output.postprocess(
-            [
-                ["Hello World", "test/test_files/bus.png"],
-                ["Bye World", "test/test_files/bus.png"],
-            ]
-        )
-        self.assertEqual(
-            output,
-            [
-                ["Hello World", deepcopy(media_data.BASE64_IMAGE)],
-                ["Bye World", deepcopy(media_data.BASE64_IMAGE)],
-            ],
-        )
-
-        carousel_output = gr.Carousel(components=gr.Textbox(), label="Disease")
-        output = carousel_output.postprocess([["Hello World"], ["Bye World"]])
-        self.assertEqual(output, [["Hello World"], ["Bye World"]])
-        self.assertEqual(
-            carousel_output.get_config(),
-            {
-                "components": [
-                    {
-                        "name": "textbox",
-                        "show_label": True,
-                        "label": None,
-                        "value": "",
-                        "lines": 1,
-                        "max_lines": 20,
-                        "style": {},
-                        "elem_id": None,
-                        "visible": True,
-                        "placeholder": None,
-                        "interactive": None,
-                    }
-                ],
-                "name": "carousel",
-                "show_label": True,
-                "label": "Disease",
-                "style": {},
-                "elem_id": None,
-                "visible": True,
-                "interactive": None,
-            },
-        )
-        output = carousel_output.postprocess(["Hello World", "Bye World"])
-        self.assertEqual(output, [["Hello World"], ["Bye World"]])
-        with self.assertRaises(ValueError):
-            carousel_output.postprocess("Hello World!")
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            to_save = carousel_output.save_flagged(
-                tmpdirname, "carousel_output", output, None
-            )
-            self.assertEqual(to_save, '[["Hello World"], ["Bye World"]]')
-            restored = carousel_output.restore_flagged(tmpdirname, to_save, None)
-            self.assertEqual(output, restored)
-
-    def test_in_interface(self):
-        """
-        Interface, process
-        """
-        carousel_output = gr.Carousel(
-            components=[gr.Textbox(), gr.Image()], label="Disease"
-        )
-
-        def report(img):
-            results = []
-            for i, mode in enumerate(["Red", "Green", "Blue"]):
-                color_filter = np.array([0, 0, 0])
-                color_filter[i] = 1
-                results.append([mode, img * color_filter])
-            return results
-
-        iface = gr.Interface(report, gr.Image(type="numpy"), carousel_output)
-        result = iface.process([deepcopy(media_data.BASE64_IMAGE)])
-        self.assertTrue(result[0][0][0] == "Red")
-        self.assertTrue(
-            result[0][0][1].startswith("data:image/png;base64,iVBORw0KGgoAAA")
-        )
-        self.assertTrue(result[0][1][0] == "Green")
-        self.assertTrue(
-            result[0][1][1].startswith("data:image/png;base64,iVBORw0KGgoAAA")
-        )
-        self.assertTrue(result[0][2][0] == "Blue")
-        self.assertTrue(
-            result[0][2][1].startswith("data:image/png;base64,iVBORw0KGgoAAA")
-        )
-
-
 if __name__ == "__main__":
    unittest.main()