diff --git a/.circleci/config.yml b/.circleci/config.yml
index 2d139d05b2..cc2a0c2e4e 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -53,7 +53,7 @@ jobs:
       - run:
           command: |
             . venv/bin/activate
-            python -m  flake8 --ignore=E731,E501,E722,W503,E126,F401 gradio test
+            python -m  flake8 --ignore=E731,E501,E722,W503,E126,F401,E203 gradio test
       - codecov/upload:
           file: 'coverage.xml'
       - store_artifacts:
diff --git a/gradio/encryptor.py b/gradio/encryptor.py
index 6dafec43fc..01951ea1d2 100644
--- a/gradio/encryptor.py
+++ b/gradio/encryptor.py
@@ -22,10 +22,10 @@ def encrypt(key: bytes, source: bytes) -> bytes:
 def decrypt(key: bytes, source: bytes) -> bytes:
     IV = source[: AES.block_size]  # extract the IV from the beginning
     decryptor = AES.new(key, AES.MODE_CBC, IV)
-    data = decryptor.decrypt(source[AES.block_size:])  # decrypt
+    data = decryptor.decrypt(source[AES.block_size :])  # decrypt
     padding = data[-1]  # pick the padding value from the end; Python 2.x: ord(data[-1])
     if (
-            data[-padding:] != bytes([padding]) * padding
+        data[-padding:] != bytes([padding]) * padding
     ):  # Python 2.x: chr(padding) * padding
         raise ValueError("Invalid padding...")
     return data[:-padding]  # remove the padding
diff --git a/gradio/external.py b/gradio/external.py
index 978a82ff9e..9684ba221e 100644
--- a/gradio/external.py
+++ b/gradio/external.py
@@ -1,6 +1,7 @@
 import base64
 import json
 import re
+
 import requests
 
 from gradio import inputs, outputs
@@ -213,7 +214,7 @@ def get_huggingface_interface(model_name, api_key, alias):
         # Convert to a list of input components
         data = pipeline["preprocess"](*params)
         if isinstance(
-                data, dict
+            data, dict
         ):  # HF doesn't allow additional parameters for binary files (e.g. images or audio files)
             data.update({"options": {"wait_for_model": True}})
             data = json.dumps(data)
@@ -248,7 +249,7 @@ def load_interface(name, src=None, api_key=None, alias=None):
             "/"
         )  # Separate the source (e.g. "huggingface") from the repo name (e.g. "google/vit-base-patch16-224")
         assert (
-                len(tokens) > 1
+            len(tokens) > 1
         ), "Either `src` parameter must be provided, or `name` must be formatted as {src}/{repo name}"
         src = tokens[0]
         name = "/".join(tokens[1:])
@@ -308,11 +309,11 @@ def get_spaces_interface(model_name, api_key, alias):
         result = json.loads(response.content.decode("utf-8"))
         output = result["data"]
         if (
-                len(interface_info["outputs"]) == 1
+            len(interface_info["outputs"]) == 1
         ):  # if the fn is supposed to return a single value, pop it
             output = output[0]
         if len(interface_info["outputs"]) == 1 and isinstance(
-                output, list
+            output, list
         ):  # Needed to support Output.Image() returning bounding boxes as well (TODO: handle different versions of gradio since they have slightly different APIs)
             output = output[0]
         return output
@@ -350,7 +351,7 @@ def load_from_pipeline(pipeline):
     # Handle the different pipelines. The has_attr() checks to make sure the pipeline exists in the
     # version of the transformers library that the user has installed.
     if hasattr(transformers, "AudioClassificationPipeline") and isinstance(
-            pipeline, transformers.AudioClassificationPipeline
+        pipeline, transformers.AudioClassificationPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Audio(label="Input", source="microphone", type="filepath"),
@@ -359,7 +360,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: {i["label"].split(", ")[0]: i["score"] for i in r},
         }
     elif hasattr(transformers, "AutomaticSpeechRecognitionPipeline") and isinstance(
-            pipeline, transformers.AutomaticSpeechRecognitionPipeline
+        pipeline, transformers.AutomaticSpeechRecognitionPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Audio(label="Input", source="microphone", type="filepath"),
@@ -368,7 +369,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: r["text"],
         }
     elif hasattr(transformers, "FeatureExtractionPipeline") and isinstance(
-            pipeline, transformers.FeatureExtractionPipeline
+        pipeline, transformers.FeatureExtractionPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Textbox(label="Input"),
@@ -377,7 +378,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: r[0],
         }
     elif hasattr(transformers, "FillMaskPipeline") and isinstance(
-            pipeline, transformers.FillMaskPipeline
+        pipeline, transformers.FillMaskPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Textbox(label="Input"),
@@ -386,7 +387,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: {i["token_str"]: i["score"] for i in r},
         }
     elif hasattr(transformers, "ImageClassificationPipeline") and isinstance(
-            pipeline, transformers.ImageClassificationPipeline
+        pipeline, transformers.ImageClassificationPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Image(label="Input Image", type="filepath"),
@@ -395,7 +396,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: {i["label"].split(", ")[0]: i["score"] for i in r},
         }
     elif hasattr(transformers, "QuestionAnsweringPipeline") and isinstance(
-            pipeline, transformers.QuestionAnsweringPipeline
+        pipeline, transformers.QuestionAnsweringPipeline
     ):
         pipeline_info = {
             "inputs": [
@@ -407,7 +408,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: (r["answer"], r["score"]),
         }
     elif hasattr(transformers, "SummarizationPipeline") and isinstance(
-            pipeline, transformers.SummarizationPipeline
+        pipeline, transformers.SummarizationPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Textbox(label="Input", lines=7),
@@ -416,7 +417,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: r[0]["summary_text"],
         }
     elif hasattr(transformers, "TextClassificationPipeline") and isinstance(
-            pipeline, transformers.TextClassificationPipeline
+        pipeline, transformers.TextClassificationPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Textbox(label="Input"),
@@ -425,7 +426,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: {i["label"].split(", ")[0]: i["score"] for i in r},
         }
     elif hasattr(transformers, "TextGenerationPipeline") and isinstance(
-            pipeline, transformers.TextGenerationPipeline
+        pipeline, transformers.TextGenerationPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Textbox(label="Input"),
@@ -434,7 +435,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: r[0]["generated_text"],
         }
     elif hasattr(transformers, "TranslationPipeline") and isinstance(
-            pipeline, transformers.TranslationPipeline
+        pipeline, transformers.TranslationPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Textbox(label="Input"),
@@ -443,7 +444,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: r[0]["translation_text"],
         }
     elif hasattr(transformers, "Text2TextGenerationPipeline") and isinstance(
-            pipeline, transformers.Text2TextGenerationPipeline
+        pipeline, transformers.Text2TextGenerationPipeline
     ):
         pipeline_info = {
             "inputs": inputs.Textbox(label="Input"),
@@ -452,7 +453,7 @@ def load_from_pipeline(pipeline):
             "postprocess": lambda r: r[0]["generated_text"],
         }
     elif hasattr(transformers, "ZeroShotClassificationPipeline") and isinstance(
-            pipeline, transformers.ZeroShotClassificationPipeline
+        pipeline, transformers.ZeroShotClassificationPipeline
     ):
         pipeline_info = {
             "inputs": [
@@ -478,12 +479,12 @@ def load_from_pipeline(pipeline):
         data = pipeline_info["preprocess"](*params)
         # special cases that needs to be handled differently
         if isinstance(
-                pipeline,
-                (
-                        transformers.TextClassificationPipeline,
-                        transformers.Text2TextGenerationPipeline,
-                        transformers.TranslationPipeline,
-                ),
+            pipeline,
+            (
+                transformers.TextClassificationPipeline,
+                transformers.Text2TextGenerationPipeline,
+                transformers.TranslationPipeline,
+            ),
         ):
             data = pipeline(*data)
         else:
diff --git a/gradio/inputs.py b/gradio/inputs.py
index 057ed47031..3cff47e6b1 100644
--- a/gradio/inputs.py
+++ b/gradio/inputs.py
@@ -77,7 +77,7 @@ class InputComponent(Component):
         pass
 
     def get_interpretation_scores(
-            self, x: Any, neighbors: List[Any], scores: List[float], **kwargs
+        self, x: Any, neighbors: List[Any], scores: List[float], **kwargs
     ) -> List[Any]:
         """
         Arrange the output values from the neighbors into interpretation scores for the interface to render.
@@ -106,13 +106,13 @@ class Textbox(InputComponent):
     """
 
     def __init__(
-            self,
-            lines: int = 1,
-            placeholder: Optional[str] = None,
-            default: str = "",
-            numeric: Optional[bool] = False,
-            type: Optional[str] = "str",
-            label: Optional[str] = None,
+        self,
+        lines: int = 1,
+        placeholder: Optional[str] = None,
+        default: str = "",
+        numeric: Optional[bool] = False,
+        type: Optional[str] = "str",
+        label: Optional[str] = None,
     ):
         """
         Parameters:
@@ -183,7 +183,7 @@ class Textbox(InputComponent):
         return x
 
     def set_interpret_parameters(
-            self, separator: str = " ", replacement: Optional[str] = None
+        self, separator: str = " ", replacement: Optional[str] = None
     ):
         """
         Calculates interpretation score of characters in input by splitting input into tokens, then using a "leave one out" method to calculate the score of each token by removing each token and measuring the delta of the output value.
@@ -213,7 +213,7 @@ class Textbox(InputComponent):
         return tokens, leave_one_out_strings, None
 
     def get_masked_inputs(
-            self, tokens: List[str], binary_mask_matrix: List[List[int]]
+        self, tokens: List[str], binary_mask_matrix: List[List[int]]
     ) -> List[str]:
         """
         Constructs partially-masked sentences for SHAP interpretation
@@ -225,7 +225,7 @@ class Textbox(InputComponent):
         return masked_inputs
 
     def get_interpretation_scores(
-            self, x, neighbors, scores: List[float], tokens: List[str], masks=None
+        self, x, neighbors, scores: List[float], tokens: List[str], masks=None
     ) -> List[Tuple[str, float]]:
         """
         Returns:
@@ -285,7 +285,7 @@ class Number(InputComponent):
         return x
 
     def set_interpret_parameters(
-            self, steps: int = 3, delta: float = 1, delta_type: str = "percent"
+        self, steps: int = 3, delta: float = 1, delta_type: str = "percent"
     ):
         """
         Calculates interpretation scores of numeric values close to the input number.
@@ -310,7 +310,7 @@ class Number(InputComponent):
         return negatives + positives, {}
 
     def get_interpretation_scores(
-            self, x: Number, neighbors: List[float], scores: List[float]
+        self, x: Number, neighbors: List[float], scores: List[float]
     ) -> List[Tuple[float, float]]:
         """
         Returns:
@@ -332,12 +332,12 @@ class Slider(InputComponent):
     """
 
     def __init__(
-            self,
-            minimum: float = 0,
-            maximum: float = 100,
-            step: Optional[float] = None,
-            default: Optional[float] = None,
-            label: Optional[str] = None,
+        self,
+        minimum: float = 0,
+        maximum: float = 100,
+        step: Optional[float] = None,
+        default: Optional[float] = None,
+        label: Optional[str] = None,
     ):
         """
         Parameters:
@@ -352,7 +352,7 @@ class Slider(InputComponent):
         if step is None:
             difference = maximum - minimum
             power = math.floor(math.log10(difference) - 2)
-            step = 10 ** power
+            step = 10**power
         self.step = step
         self.default = minimum if default is None else default
         self.test_input = self.default
@@ -406,7 +406,7 @@ class Slider(InputComponent):
         )
 
     def get_interpretation_scores(
-            self, x, neighbors, scores: List[float]
+        self, x, neighbors, scores: List[float]
     ) -> List[float]:
         """
         Returns:
@@ -492,11 +492,11 @@ class CheckboxGroup(InputComponent):
     """
 
     def __init__(
-            self,
-            choices: List[str],
-            default: List[str] = [],
-            type: str = "value",
-            label: Optional[str] = None,
+        self,
+        choices: List[str],
+        default: List[str] = [],
+        type: str = "value",
+        label: Optional[str] = None,
     ):
         """
         Parameters:
@@ -589,11 +589,11 @@ class Radio(InputComponent):
     """
 
     def __init__(
-            self,
-            choices: List(str),
-            type: str = "value",
-            default: Optional[str] = None,
-            label: Optional[str] = None,
+        self,
+        choices: List(str),
+        type: str = "value",
+        default: Optional[str] = None,
+        label: Optional[str] = None,
     ):
         """
         Parameters:
@@ -665,11 +665,11 @@ class Dropdown(InputComponent):
     """
 
     def __init__(
-            self,
-            choices: List[str],
-            type: str = "value",
-            default: Optional[str] = None,
-            label: Optional[str] = None,
+        self,
+        choices: List[str],
+        type: str = "value",
+        default: Optional[str] = None,
+        label: Optional[str] = None,
     ):
         """
         Parameters:
@@ -741,15 +741,15 @@ class Image(InputComponent):
     """
 
     def __init__(
-            self,
-            shape: Tuple[int, int] = None,
-            image_mode: str = "RGB",
-            invert_colors: bool = False,
-            source: str = "upload",
-            tool: str = "editor",
-            type: str = "numpy",
-            label: str = None,
-            optional: bool = False,
+        self,
+        shape: Tuple[int, int] = None,
+        image_mode: str = "RGB",
+        invert_colors: bool = False,
+        source: str = "upload",
+        tool: str = "editor",
+        type: str = "numpy",
+        label: str = None,
+        optional: bool = False,
     ):
         """
         Parameters:
@@ -978,11 +978,11 @@ class Video(InputComponent):
     """
 
     def __init__(
-            self,
-            type: Optional[str] = None,
-            source: str = "upload",
-            label: Optional[str] = None,
-            optional: bool = False,
+        self,
+        type: Optional[str] = None,
+        source: str = "upload",
+        label: Optional[str] = None,
+        optional: bool = False,
     ):
         """
         Parameters:
@@ -1035,7 +1035,7 @@ class Video(InputComponent):
         file_name = file.name
         uploaded_format = file_name.split(".")[-1].lower()
         if self.type is not None and uploaded_format != self.type:
-            output_file_name = file_name[0: file_name.rindex(".") + 1] + self.type
+            output_file_name = file_name[0 : file_name.rindex(".") + 1] + self.type
             ff = FFmpeg(inputs={file_name: None}, outputs={output_file_name: None})
             ff.run()
             return output_file_name
@@ -1065,11 +1065,11 @@ class Audio(InputComponent):
     """
 
     def __init__(
-            self,
-            source: str = "upload",
-            type: str = "numpy",
-            label: str = None,
-            optional: bool = False,
+        self,
+        source: str = "upload",
+        type: str = "numpy",
+        label: str = None,
+        optional: bool = False,
     ):
         """
         Parameters:
@@ -1277,12 +1277,12 @@ class File(InputComponent):
     """
 
     def __init__(
-            self,
-            file_count: str = "single",
-            type: str = "file",
-            label: Optional[str] = None,
-            keep_filename: bool = True,
-            optional: bool = False,
+        self,
+        file_count: str = "single",
+        type: str = "file",
+        label: Optional[str] = None,
+        keep_filename: bool = True,
+        optional: bool = False,
     ):
         """
         Parameters:
@@ -1378,15 +1378,15 @@ class Dataframe(InputComponent):
     """
 
     def __init__(
-            self,
-            headers: Optional[List[str]] = None,
-            row_count: int = 3,
-            col_count: Optional[int] = 3,
-            datatype: str | List[str] = "str",
-            col_width: int | List[int] = None,
-            default: Optional[List[List[Any]]] = None,
-            type: str = "pandas",
-            label: Optional[str] = None,
+        self,
+        headers: Optional[List[str]] = None,
+        row_count: int = 3,
+        col_count: Optional[int] = 3,
+        datatype: str | List[str] = "str",
+        col_width: int | List[int] = None,
+        default: Optional[List[List[Any]]] = None,
+        type: str = "pandas",
+        label: Optional[str] = None,
     ):
         """
         Parameters:
@@ -1491,11 +1491,11 @@ class Timeseries(InputComponent):
     """
 
     def __init__(
-            self,
-            x: Optional[str] = None,
-            y: str | List[str] = None,
-            label: Optional[str] = None,
-            optional: bool = False,
+        self,
+        x: Optional[str] = None,
+        y: str | List[str] = None,
+        label: Optional[str] = None,
+        optional: bool = False,
     ):
         """
         Parameters:
@@ -1591,7 +1591,7 @@ def get_input_instance(iface: Interface):
         shortcut = InputComponent.get_all_shortcut_implementations()[iface]
         return shortcut[0](**shortcut[1])
     elif isinstance(
-            iface, dict
+        iface, dict
     ):  # a dict with `name` as the input component type and other keys as parameters
         name = iface.pop("name")
         for component in InputComponent.__subclasses__():
diff --git a/gradio/outputs.py b/gradio/outputs.py
index a9760dd0e2..06d4210532 100644
--- a/gradio/outputs.py
+++ b/gradio/outputs.py
@@ -99,10 +99,10 @@ class Label(OutputComponent):
     CONFIDENCES_KEY = "confidences"
 
     def __init__(
-            self,
-            num_top_classes: Optional[int] = None,
-            type: str = "auto",
-            label: Optional[str] = None,
+        self,
+        num_top_classes: Optional[int] = None,
+        type: str = "auto",
+        label: Optional[str] = None,
     ):
         """
         Parameters:
@@ -122,11 +122,11 @@ class Label(OutputComponent):
         (Dict[label: str, confidences: List[Dict[label: str, confidence: number]]]): Object with key 'label' representing primary label, and key 'confidences' representing a list of label-confidence pairs
         """
         if self.type == "label" or (
-                self.type == "auto" and (isinstance(y, str) or isinstance(y, Number))
+            self.type == "auto" and (isinstance(y, str) or isinstance(y, Number))
         ):
             return {"label": str(y)}
         elif self.type == "confidences" or (
-                self.type == "auto" and isinstance(y, dict)
+            self.type == "auto" and isinstance(y, dict)
         ):
             sorted_pred = sorted(y.items(), key=operator.itemgetter(1), reverse=True)
             if self.num_top_classes is not None:
@@ -146,13 +146,13 @@ class Label(OutputComponent):
     def deserialize(self, y):
         # 5 cases: (1): {'label': 'lion'}, {'label': 'lion', 'confidences':...}, {'lion': 0.46, ...}, 'lion', '0.46'
         if self.type == "label" or (
-                self.type == "auto"
-                and (
-                        isinstance(y, str)
-                        or isinstance(y, int)
-                        or isinstance(y, float)
-                        or ("label" in y and not ("confidences" in y.keys()))
-                )
+            self.type == "auto"
+            and (
+                isinstance(y, str)
+                or isinstance(y, int)
+                or isinstance(y, float)
+                or ("label" in y and not ("confidences" in y.keys()))
+            )
         ):
             if isinstance(y, str) or isinstance(y, int) or isinstance(y, float):
                 return y
@@ -201,7 +201,7 @@ class Image(OutputComponent):
     """
 
     def __init__(
-            self, type: str = "auto", plot: bool = False, label: Optional[str] = None
+        self, type: str = "auto", plot: bool = False, label: Optional[str] = None
     ):
         """
         Parameters:
@@ -301,7 +301,7 @@ class Video(OutputComponent):
         """
         returned_format = y.split(".")[-1].lower()
         if self.type is not None and returned_format != self.type:
-            output_file_name = y[0: y.rindex(".") + 1] + self.type
+            output_file_name = y[0 : y.rindex(".") + 1] + self.type
             ff = FFmpeg(inputs={y: None}, outputs={output_file_name: None})
             ff.run()
             y = output_file_name
@@ -373,10 +373,10 @@ class HighlightedText(OutputComponent):
     """
 
     def __init__(
-            self,
-            color_map: Dict[str, str] = None,
-            label: Optional[str] = None,
-            show_legend: bool = False,
+        self,
+        color_map: Dict[str, str] = None,
+        label: Optional[str] = None,
+        show_legend: bool = False,
     ):
         """
         Parameters:
@@ -591,13 +591,13 @@ class Dataframe(OutputComponent):
     """
 
     def __init__(
-            self,
-            headers: Optional[List[str]] = None,
-            max_rows: Optional[int] = 20,
-            max_cols: Optional[int] = None,
-            overflow_row_behaviour: str = "paginate",
-            type: str = "auto",
-            label: Optional[str] = None,
+        self,
+        headers: Optional[List[str]] = None,
+        max_rows: Optional[int] = 20,
+        max_cols: Optional[int] = None,
+        overflow_row_behaviour: str = "paginate",
+        type: str = "auto",
+        label: Optional[str] = None,
     ):
         """
         Parameters:
@@ -679,9 +679,9 @@ class Carousel(OutputComponent):
     """
 
     def __init__(
-            self,
-            components: OutputComponent | List[OutputComponent],
-            label: Optional[str] = None,
+        self,
+        components: OutputComponent | List[OutputComponent],
+        label: Optional[str] = None,
     ):
         """
         Parameters:
@@ -752,7 +752,7 @@ class Timeseries(OutputComponent):
     """
 
     def __init__(
-            self, x: str = None, y: str | List[str] = None, label: Optional[str] = None
+        self, x: str = None, y: str | List[str] = None, label: Optional[str] = None
     ):
         """
         Parameters:
diff --git a/gradio/processing_utils.py b/gradio/processing_utils.py
index 4b354de892..d95b9d40ab 100644
--- a/gradio/processing_utils.py
+++ b/gradio/processing_utils.py
@@ -60,10 +60,10 @@ def encode_file_to_base64(f, encryption_key=None):
         base64_str = str(encoded_string, "utf-8")
         mimetype = get_mimetype(f)
         return (
-                "data:"
-                + (mimetype if mimetype is not None else "")
-                + ";base64,"
-                + base64_str
+            "data:"
+            + (mimetype if mimetype is not None else "")
+            + ";base64,"
+            + base64_str
         )
 
 
@@ -72,7 +72,7 @@ def encode_url_to_base64(url):
     base64_str = str(encoded_string, "utf-8")
     mimetype = get_mimetype(url)
     return (
-            "data:" + (mimetype if mimetype is not None else "") + ";base64," + base64_str
+        "data:" + (mimetype if mimetype is not None else "") + ";base64," + base64_str
     )
 
 
@@ -190,8 +190,8 @@ def decode_base64_to_file(encoding, encryption_key=None, file_path=None):
         filename = os.path.basename(file_path)
         prefix = filename
         if "." in filename:
-            prefix = filename[0: filename.index(".")]
-            extension = filename[filename.index(".") + 1:]
+            prefix = filename[0 : filename.index(".")]
+            extension = filename[filename.index(".") + 1 :]
     if extension is None:
         file_obj = tempfile.NamedTemporaryFile(delete=False, prefix=prefix)
     else:
@@ -209,8 +209,8 @@ def create_tmp_copy_of_file(file_path):
     file_name = os.path.basename(file_path)
     prefix, extension = file_name, None
     if "." in file_name:
-        prefix = file_name[0: file_name.index(".")]
-        extension = file_name[file_name.index(".") + 1:]
+        prefix = file_name[0 : file_name.index(".")]
+        extension = file_name[file_name.index(".") + 1 :]
     if extension is None:
         file_obj = tempfile.NamedTemporaryFile(delete=False, prefix=prefix)
     else:
@@ -336,7 +336,7 @@ def _convert(image, dtype, force_copy=False, uniform=False):
             Output image array. Has the same kind as `a`.
         """
         kind = a.dtype.kind
-        if n > m and a.max() < 2 ** m:
+        if n > m and a.max() < 2**m:
             return a.astype(_dtype_bits(kind, m))
         elif n == m:
             return a.copy() if copy else a
@@ -353,11 +353,11 @@ def _convert(image, dtype, force_copy=False, uniform=False):
             # exact upscale to a multiple of `n` bits
             if copy:
                 b = np.empty(a.shape, _dtype_bits(kind, m))
-                np.multiply(a, (2 ** m - 1) // (2 ** n - 1), out=b, dtype=b.dtype)
+                np.multiply(a, (2**m - 1) // (2**n - 1), out=b, dtype=b.dtype)
                 return b
             else:
                 a = a.astype(_dtype_bits(kind, m, a.dtype.itemsize), copy=False)
-                a *= (2 ** m - 1) // (2 ** n - 1)
+                a *= (2**m - 1) // (2**n - 1)
                 return a
         else:
             # upscale to a multiple of `n` bits,
@@ -365,12 +365,12 @@ def _convert(image, dtype, force_copy=False, uniform=False):
             o = (m // n + 1) * n
             if copy:
                 b = np.empty(a.shape, _dtype_bits(kind, o))
-                np.multiply(a, (2 ** o - 1) // (2 ** n - 1), out=b, dtype=b.dtype)
+                np.multiply(a, (2**o - 1) // (2**n - 1), out=b, dtype=b.dtype)
                 b //= 2 ** (o - m)
                 return b
             else:
                 a = a.astype(_dtype_bits(kind, o, a.dtype.itemsize), copy=False)
-                a *= (2 ** o - 1) // (2 ** n - 1)
+                a *= (2**o - 1) // (2**n - 1)
                 a //= 2 ** (o - m)
                 return a
 
diff --git a/gradio/routes.py b/gradio/routes.py
index 6f5347a5a0..2a7e7f9db9 100644
--- a/gradio/routes.py
+++ b/gradio/routes.py
@@ -82,9 +82,9 @@ def get_token(request: Request) -> Optional[str]:
 def login(form_data: OAuth2PasswordRequestForm = Depends()):
     username, password = form_data.username, form_data.password
     if (
-            not callable(app.auth)
-            and username in app.auth
-            and app.auth[username] == password
+        not callable(app.auth)
+        and username in app.auth
+        and app.auth[username] == password
     ) or (callable(app.auth) and app.auth.__call__(username, password)):
         token = secrets.token_urlsafe(16)
         app.tokens[token] = username
@@ -144,9 +144,9 @@ def build_resource(path: str):
 @app.get("/file/{path:path}", dependencies=[Depends(login_check)])
 def file(path):
     if (
-            app.interface.encrypt
-            and isinstance(app.interface.examples, str)
-            and path.startswith(app.interface.examples)
+        app.interface.encrypt
+        and isinstance(app.interface.examples, str)
+        and path.startswith(app.interface.examples)
     ):
         with open(safe_join(app.cwd, path), "rb") as encrypted_file:
             encrypted_data = encrypted_file.read()
@@ -305,10 +305,10 @@ def safe_join(directory: str, path: str) -> Optional[str]:
         filename = posixpath.normpath(path)
 
     if (
-            any(sep in filename for sep in _os_alt_seps)
-            or os.path.isabs(filename)
-            or filename == ".."
-            or filename.startswith("../")
+        any(sep in filename for sep in _os_alt_seps)
+        or os.path.isabs(filename)
+        or filename == ".."
+        or filename.startswith("../")
     ):
         return None
 
diff --git a/gradio/utils.py b/gradio/utils.py
index 92c6d384f0..da878d17b3 100644
--- a/gradio/utils.py
+++ b/gradio/utils.py
@@ -105,7 +105,7 @@ async def log_feature_analytics(ip_address: str, feature: str) -> None:
     async with aiohttp.ClientSession() as session:
         try:
             async with session.post(
-                    analytics_url + "gradio-feature-analytics/", data=data
+                analytics_url + "gradio-feature-analytics/", data=data
             ):
                 pass
         except (aiohttp.ClientError):
@@ -232,9 +232,9 @@ def get_config_file(interface: Interface) -> Dict[str, Any]:
                 iface["label"] = ret_name
             if len(interface.predict) > 1:
                 iface["label"] = (
-                        interface.function_names[function_index].replace("_", " ")
-                        + ": "
-                        + iface["label"]
+                    interface.function_names[function_index].replace("_", " ")
+                    + ": "
+                    + iface["label"]
                 )
 
     except ValueError:
@@ -263,10 +263,10 @@ def get_config_file(interface: Interface) -> Dict[str, Any]:
                     examples = examples[1:]  # remove header
             for i, example in enumerate(examples):
                 for j, (component, cell) in enumerate(
-                        zip(
-                            interface.input_components + interface.output_components,
-                            example,
-                        )
+                    zip(
+                        interface.input_components + interface.output_components,
+                        example,
+                    )
                 ):
                     examples[i][j] = component.restore_flagged(
                         interface.flagging_dir,
diff --git a/scripts/format.sh b/scripts/format.sh
new file mode 100644
index 0000000000..f08bb097f6
--- /dev/null
+++ b/scripts/format.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+if [ -z "$(ls | grep CONTRIBUTING.md)" ]; then
+  echo "Please run the script from repo directory"
+  exit -1
+else
+  echo "Installing formatting with black and isort, also checking for standards with flake8"
+  python -m black gradio test
+  python -m isort --profile=black gradio test
+  python -m  flake8 --ignore=E731,E501,E722,W503,E126,F401,E203 gradio test
+fi
diff --git a/test/test_interfaces.py b/test/test_interfaces.py
index 4faf3bc4ab..cdb93c1d30 100644
--- a/test/test_interfaces.py
+++ b/test/test_interfaces.py
@@ -7,7 +7,8 @@ from contextlib import contextmanager
 import mlflow
 import requests
 import wandb
-from gradio.interface import Interface, os, close_all
+
+from gradio.interface import Interface, close_all, os
 
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
 
diff --git a/test/test_queuing.py b/test/test_queuing.py
index cca40f91dc..7bbfe5d159 100644
--- a/test/test_queuing.py
+++ b/test/test_queuing.py
@@ -2,6 +2,7 @@
 
 import os
 import unittest
+
 from gradio import queueing
 
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
diff --git a/test/test_utils.py b/test/test_utils.py
index b9c4502982..4f17a68a14 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -7,7 +7,16 @@ import warnings
 import pkg_resources
 import requests
 
-from gradio.utils import version_check, json, error_analytics, colab_check, ipython_check, readme_to_html, get_local_ip_address, launch_analytics
+from gradio.utils import (
+    colab_check,
+    error_analytics,
+    get_local_ip_address,
+    ipython_check,
+    json,
+    launch_analytics,
+    readme_to_html,
+    version_check,
+)
 
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"