Adding API docs to all interfaces (#309)

* api view with basic html

* added base docs for raw inputs/outputs

* reading correct url from frontend

* styling

* fill in the blank request snippet

* post a request from docs

* post button and random generator

* styling

* added view the api to interface

* style changes

* corrected input output docs

* prefill with example instead of random

* added curl and javascript syntax

* removed scrollbars

* API doc fixes

* added correct docs to all pre/post processing methods

* updated to new doc style

* live demo with sample inputs

* fixing golden screenshots

* correct timeseries preprocess doc

* correct timeseries preprocess doc

* correct timeseries preprocess doc

* fixed overwrite issue

* remove static from git

* fix merge

* fix tests

* fix tests

* fix tests

Co-authored-by: Ali Abid <you@example.comgit>

.circleci/config.yml

@@ -18,6 +18,7 @@ jobs:
             . venv/bin/activate
             pip install --upgrade pip
             pip install -r gradio.egg-info/requires.txt
+            pip install shap IPython
             pip install selenium==4.0.0a6.post2 coverage
       - run:
           command: |

.gitignore

@@ -32,7 +32,7 @@ demo/flagged
 test.txt
 build/
 gradio/launches.json
-gradio/frontend/static
+gradio/templates/frontend/static
 workspace.code-workspace
 *.db
 demo/files/*.avi

MANIFEST.in

@@ -1,2 +1,2 @@
-recursive-include gradio/frontend *
+recursive-include gradio/templates *
 include gradio/version.txt

frontend/craco.config.js

@@ -29,7 +29,7 @@ module.exports = {
       webpackConfig.entry = "./src/index";
       webpackConfig.output = {
         publicPath: "",
-        path: path.resolve(__dirname, "../gradio/frontend"),
+        path: path.resolve(__dirname, "../gradio/templates/frontend"),
         filename: "static/bundle.js",
         chunkFilename: "static/js/[name].chunk.js"
       };

frontend/src/gradio.jsx

@@ -39,15 +39,19 @@ export class GradioPage extends React.Component {
               false
             )}
           </div>
+          <a href="/api/" target="_blank" class="footer" rel="noreferrer">
+            <span>view the api </span><img class="logo" src="https://i.ibb.co/6DVLqmf/noun-tools-2220412.png" alt="logo"/>
+          <span> |</span>
           <a
             href="https://gradio.app"
             target="_blank"
             className="footer"
             rel="noreferrer"
           >
-            <span>built with</span>
+            <span> built with</span>
             <img className="logo" src={logo} alt="logo" />
           </a>
+          </a>
         </div>
       </div>
     );
@@ -335,6 +339,7 @@ export class GradioInterface extends React.Component {
                 <button className="panel_button submit" onClick={this.submit}>
                   Submit
                 </button>
+
               )}
             </div>
           </div>

frontend/src/themes/defaults.scss

@@ -19,6 +19,9 @@
     .footer {
         @apply flex-shrink-0 flex gap-1 items-center text-gray-400 dark:text-gray-50 justify-center py-2;
     }
+    .api {
+        @apply flex-shrink-0 flex gap-1 items-center text-gray-400 dark:text-gray-50 justify-end py-2;
+    }
     .logo {
         @apply h-6;
     }

gradio.egg-info/PKG-INFO

@@ -1,4 +1,4 @@
-Metadata-Version: 2.1
+Metadata-Version: 1.0
 Name: gradio
 Version: 2.3.9
 Summary: Python library for easily interacting with trained machine learning models
@@ -6,9 +6,6 @@ Home-page: https://github.com/gradio-app/gradio-UI
 Author: Abubakar Abid
 Author-email: a12d@stanford.edu
 License: Apache License 2.0
+Description: UNKNOWN
 Keywords: machine learning,visualization,reproducibility
 Platform: UNKNOWN
-License-File: LICENSE
-
-UNKNOWN
-

gradio.egg-info/requires.txt

@@ -1,17 +1,15 @@
-numpy
-pydub
-matplotlib
-pandas
-pillow
-ffmpy
-markdown2
-pycryptodome
-requests
-paramiko
-analytics-python
-Flask>=1.1.1
 Flask-Cors>=3.0.8
-flask-cachebuster
 Flask-Login
-IPython
-shap
+Flask>=1.1.1
+analytics-python
+ffmpy
+flask-cachebuster
+markdown2
+matplotlib
+numpy
+pandas
+paramiko
+pillow
+pycryptodome
+pydub
+requests

gradio/inputs.py

@@ -78,6 +78,13 @@ class InputComponent(Component):
         '''
         pass
 
+    def generate_sample(self):
+        """
+        Returns a sample value of the input that would be accepted by the api. Used for api documentation.
+        """
+        pass
+
+
 class Textbox(InputComponent):
     """
     Component creates a textbox for user to enter input. Provides a string as an argument to the wrapped function.
@@ -130,6 +137,10 @@ class Textbox(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        Parameters:
+        x (str): text input
+        """
         if self.type == "str":
             return x
         elif self.type == "number":
@@ -195,10 +206,13 @@ class Textbox(InputComponent):
             result.append((self.interpretation_separator, 0))
         return result
 
+    def generate_sample(self):
+        return "Hello World"
+
 
 class Number(InputComponent):
     """
-    Component creates a field for user to enter numeric input. Provides a nuber as an argument to the wrapped function.
+    Component creates a field for user to enter numeric input. Provides a number as an argument to the wrapped function.
     Input type: float
     Demos: tax_calculator.py, titanic_survival.py
     """
@@ -228,8 +242,10 @@ class Number(InputComponent):
 
     def preprocess(self, x):
         """
+        Parameters:
+        x (number): numeric input
         Returns:
-        (float): Number representing function input
+        (float): number representing function input
         """
         return float(x)
 
@@ -275,6 +291,9 @@ class Number(InputComponent):
         interpretation.insert(int(len(interpretation) / 2), [x, None])
         return interpretation
 
+    def generate_sample(self):
+        return 1
+
 
 class Slider(InputComponent):
     """
@@ -319,6 +338,15 @@ class Slider(InputComponent):
             "slider": {},
         }
 
+    def preprocess(self, x):
+        """
+        Parameters:
+        x (number): numeric input
+        Returns:
+        (number): numeric input
+        """
+        return x
+
     def preprocess_example(self, x):
         """
         Returns:
@@ -345,6 +373,9 @@ class Slider(InputComponent):
         """
         return scores
 
+    def generate_sample(self):
+        return self.maximum
+
 
 class Checkbox(InputComponent):
     """
@@ -376,6 +407,15 @@ class Checkbox(InputComponent):
             "checkbox": {},
         }
 
+    def preprocess(self, x):
+        """
+        Parameters:
+        x (bool): boolean input
+        Returns:
+        (bool): boolean input
+        """
+        return x
+
     def preprocess_example(self, x):
         """
         Returns:
@@ -402,6 +442,8 @@ class Checkbox(InputComponent):
         else:
             return None, scores[0]
 
+    def generate_sample(self):
+        return True
 
 class CheckboxGroup(InputComponent):
     """
@@ -433,6 +475,12 @@ class CheckboxGroup(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        Parameters:
+        x (List[str]): list of selected choices
+        Returns:
+        (Union[List[str], List[int]]): list of selected choices as strings or indices within choice list
+        """
         if self.type == "value":
             return x
         elif self.type == "index":
@@ -481,6 +529,8 @@ class CheckboxGroup(InputComponent):
     def restore_flagged(self, data):
         return json.loads(data)
 
+    def generate_sample(self):
+        return self.choices
 
 class Radio(InputComponent):
     """
@@ -512,6 +562,12 @@ class Radio(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        Parameters:
+        x (str): selected choice
+        Returns:
+        (Union[str, int]): selected choice as string or index within choice list
+        """
         if self.type == "value":
             return x
         elif self.type == "index":
@@ -539,6 +595,9 @@ class Radio(InputComponent):
         scores.insert(self.choices.index(x), None)
         return scores
 
+    def generate_sample(self):
+        return self.choices[0]
+
 
 class Dropdown(InputComponent):
     """
@@ -570,6 +629,12 @@ class Dropdown(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        Parameters:
+        x (str): selected choice
+        Returns:
+        (Union[str, int]): selected choice as string or index within choice list
+        """
         if self.type == "value":
             return x
         elif self.type == "index":
@@ -597,6 +662,10 @@ class Dropdown(InputComponent):
         scores.insert(self.choices.index(x), None)
         return scores
 
+    def generate_sample(self):
+        return self.choices[0]
+
+
 class Image(InputComponent):
     """
     Component creates an image upload box with editing capabilities. 
@@ -647,6 +716,12 @@ class Image(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        Parameters:
+        x (str): base64 url data
+        Returns:
+        (Union[numpy.array, PIL.Image, file-object]): image in requested format
+        """
         if x is None:
             return x
         im = processing_utils.decode_base64_to_image(x)
@@ -791,6 +866,9 @@ class Image(InputComponent):
         """
         return self.save_flagged_file(dir, label, data, encryption_key)
 
+    def generate_sample(self):
+        return test_data.BASE64_IMAGE
+
 
 class Video(InputComponent):
     """
@@ -823,9 +901,15 @@ class Video(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        Parameters:
+        x (Dict[name: str, data: str]): JSON object with filename as 'name' property and base64 data as 'data' property  
+        Returns:
+        (str): file path to video
+        """
         if x is None:
             return x
-        file_name, file_data, is_example = x["name"], x["data"], x["is_example"]
+        file_name, file_data, is_example = x["name"], x["data"], x.get("is_example", False)
         if is_example:
             file = processing_utils.create_tmp_copy_of_file(file_name)
         else:
@@ -857,6 +941,9 @@ class Video(InputComponent):
         """
         return self.save_flagged_file(dir, label, data, encryption_key)
 
+    def generate_sample(self):
+        return test_data.BASE64_VIDEO
+
 
 class Audio(InputComponent):
     """
@@ -877,8 +964,7 @@ class Audio(InputComponent):
         requires_permissions = source == "microphone"
         self.type = type
         self.optional = optional
-        self.test_input = {"name": "sample.wav",
-                           "data": test_data.BASE64_AUDIO, "is_example": False}
+        self.test_input = test_data.BASE64_AUDIO
         self.interpret_by_tokens = True
         super().__init__(label, requires_permissions)
 
@@ -898,6 +984,12 @@ class Audio(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        Parameters:
+        x (Dict[name: str, data: str]): JSON object with filename as 'name' property and base64 data as 'data' property  
+        Returns:
+        (Union[Tuple[int, numpy.array], file-object, numpy.array]): audio in requested format
+        """
         if x is None:
             return x
         file_name, file_data, is_example = x["name"], x["data"], x.get("is_example", False)
@@ -1023,12 +1115,15 @@ class Audio(InputComponent):
         """
         return self.save_flagged_file(dir, label, data, encryption_key)
 
+    def generate_sample(self):
+        return test_data.BASE64_AUDIO
+
 
 class File(InputComponent):
     """
     Component accepts generic file uploads.
     Input type: Union[file-object, bytes, List[Union[file-object, bytes]]]
-    Demos: zip_to_json.py, zip_two_files.py
+    Demos: zisp_to_json.py, zip_two_files.py
     """
 
     def __init__(self, file_count="single", type="file", label=None, keep_filename=True, optional=False):
@@ -1061,11 +1156,17 @@ class File(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        Parameters:
+        x (List[Dict[name: str, data: str]]): List of JSON objects with filename as 'name' property and base64 data as 'data' property
+        Returns:
+        (Union[file-object, bytes, List[Union[file-object, bytes]]]): File objects in requested format
+        """
         if x is None:
             return None
 
         def process_single_file(f):
-            file_name, data, is_example = f["name"], f["data"], f["is_example"]
+            file_name, data, is_example = f["name"], f["data"], f.get("is_example", False)
             if self.type == "file":
                 if is_example:
                     return processing_utils.create_tmp_copy_of_file(file_name)
@@ -1093,6 +1194,9 @@ class File(InputComponent):
         """
         return self.save_flagged_file(dir, label, data["data"], encryption_key)
 
+    def generate_sample(self):
+        return test_data.BASE64_FILE
+
 
 class Dataframe(InputComponent):
     """
@@ -1151,6 +1255,12 @@ class Dataframe(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        Parameters:
+        x (List[List[Union[str, number, bool]]]): 2D array of str, numeric, or bool data
+        Returns:
+        (Union[pandas.DataFrame, numpy.array, List[Union[str, float]], List[List[Union[str, float]]]]): Dataframe in requested format
+        """
         if self.type == "pandas":
             if self.headers:
                 return pd.DataFrame(x, columns=self.headers)
@@ -1175,6 +1285,9 @@ class Dataframe(InputComponent):
     def restore_flagged(self, data):
         return json.loads(data)
 
+    def generate_sample(self):
+        return [[1, 2, 3], [4, 5, 6]]
+
 
 class Timeseries(InputComponent):
     """
@@ -1213,10 +1326,16 @@ class Timeseries(InputComponent):
         }
 
     def preprocess(self, x):
+        """
+        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
         dataframe = pd.DataFrame(data=x["data"], columns=x["headers"])
-        if x["range"] is not None:
+        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
@@ -1230,6 +1349,11 @@ class Timeseries(InputComponent):
     def restore_flagged(self, data):
         return json.loads(data)
 
+    def generate_sample(self):
+        return {"data": [[1] + [2] * len(self.y)] * 4, "headers": [self.x] + self.y} 
+
+
+
 
 def get_input_instance(iface):
     if isinstance(iface, str):

gradio/networking.py

@@ -25,9 +25,9 @@ from gradio import encryptor
 from gradio import queue
 from functools import wraps
 import io
+import inspect
 import traceback
 
-
 INITIAL_PORT_VALUE = int(os.getenv(
     'GRADIO_SERVER_PORT', "7860"))  # The http server will try to open on port 7860. If not available, 7861, 7862, etc.
 TRY_NUM_PORTS = int(os.getenv(
@@ -37,8 +37,8 @@ LOCALHOST_NAME = os.getenv(
 GRADIO_API_SERVER = "https://api.gradio.app/v1/tunnel-request"
 GRADIO_FEATURE_ANALYTICS_URL = "https://api.gradio.app/gradio-feature-analytics/"
 
-STATIC_TEMPLATE_LIB = pkg_resources.resource_filename("gradio", "frontend/")
-STATIC_PATH_LIB = pkg_resources.resource_filename("gradio", "frontend/static")
+STATIC_TEMPLATE_LIB = pkg_resources.resource_filename("gradio", "templates/")
+STATIC_PATH_LIB = pkg_resources.resource_filename("gradio", "templates/frontend/static")
 VERSION_FILE = pkg_resources.resource_filename("gradio", "version.txt")
 with open(VERSION_FILE) as version_file:
     GRADIO_STATIC_ROOT = "https://gradio.s3-us-west-2.amazonaws.com/" + \
@@ -129,7 +129,7 @@ def get_first_available_port(initial, final):
 @login_check
 def main():
     session["state"] = None
-    return render_template("index.html", config=app.interface.config)
+    return render_template("frontend/index.html", config=app.interface.config)
 
 
 @app.route("/static/<path:path>", methods=["GET"])
@@ -145,7 +145,7 @@ def static_resource(path):
 def login():
     if request.method == "GET":
         config = get_config()
-        return render_template("index.html", config=config)
+        return render_template("frontend/index.html", config=config)
     elif request.method == "POST":
         username = request.form.get("username")
         password = request.form.get("password")
@@ -207,6 +207,49 @@ def predict():
     return jsonify(output)
 
 
+def get_types(cls_set, component):
+    docset = []
+    types = []
+    if component == "input":
+        for cls in cls_set:
+            doc = inspect.getdoc(cls.preprocess)
+            doc_lines = doc.split("\n")
+            docset.append(doc_lines[1].split(":")[-1])
+            types.append(doc_lines[1].split(")")[0].split("(")[-1])
+    else:
+        for cls in cls_set:
+            doc = inspect.getdoc(cls.postprocess)
+            doc_lines = doc.split("\n")
+            docset.append(doc_lines[-1].split(":")[-1])
+            types.append(doc_lines[-1].split(")")[0].split("(")[-1])
+    return docset, types
+
+
+@app.route("/api/", methods=["GET"])
+def api_docs():
+    inputs = [type(inp) for inp in app.interface.input_components]
+    outputs = [type(out) for out in app.interface.output_components]
+    input_types_doc, input_types = get_types(inputs, "input")
+    output_types_doc, output_types = get_types(outputs, "output")
+    input_names = [type(inp).__name__ for inp in app.interface.input_components]
+    output_names = [type(out).__name__ for out in app.interface.output_components]
+    sample_inputs = [inp.generate_sample() for inp in app.interface.input_components]
+    docs = {
+        "inputs": input_names,
+        "outputs": output_names,
+        "len_inputs": len(inputs),
+        "len_outputs": len(outputs),
+        "inputs_lower": [name.lower() for name in input_names],
+        "outputs_lower": [name.lower() for name in output_names],
+        "input_types": input_types,
+        "output_types": output_types,
+        "input_types_doc": input_types_doc,
+        "output_types_doc": output_types_doc,
+        "sample_inputs": sample_inputs
+    }
+    return render_template("api_docs.html", **docs)
+
+
 def log_feature_analytics(feature):
     if app.interface.analytics_enabled:
         try:

gradio/outputs.py

@@ -68,6 +68,12 @@ class Textbox(OutputComponent):
         }
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (str): text output
+        Returns:
+        (Union[str, number]): output value
+        """
         if self.type == "str" or self.type == "auto":
             return str(y)
         elif self.type == "number":
@@ -98,6 +104,12 @@ class Label(OutputComponent):
         super().__init__(label)
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (Dict[str, float]): dictionary mapping label to confidence value 
+        Returns:
+        (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))):
             return {"label": str(y)}
         elif self.type == "confidences" or (self.type == "auto" and isinstance(y, dict)):
@@ -190,6 +202,12 @@ class Image(OutputComponent):
         }
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (Union[numpy.array, PIL.Image, str, matplotlib.pyplot, Tuple[Union[numpy.array, PIL.Image, str], List[Tuple[str, float, float, float, float]]]]): image in specified format 
+        Returns:
+        (str): base64 url data
+        """
         if self.type == "auto":
             if isinstance(y, np.ndarray):
                 dtype = "numpy"
@@ -252,6 +270,12 @@ class Video(OutputComponent):
         }
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (str): path to video 
+        Returns:
+        (str): base64 url data
+        """
         returned_format = y.split(".")[-1].lower()
         if self.type is not None and returned_format != self.type:
             output_file_name = y[0: y.rindex(
@@ -292,6 +316,12 @@ class KeyValues(OutputComponent):
         super().__init__(label)
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (Union[Dict, List[Tuple[str, Union[str, int, float]]]]): dictionary or tuple list representing key value pairs 
+        Returns:
+        (List[Tuple[str, Union[str, number]]]): list of key value pairs
+        """
         if isinstance(y, dict):
             return list(y.items())
         elif isinstance(y, list):
@@ -343,6 +373,13 @@ class HighlightedText(OutputComponent):
         }
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (Union[Dict, List[Tuple[str, Union[str, int, float]]]]): dictionary or tuple list representing key value pairs 
+        Returns:
+        (List[Tuple[str, Union[str, number]]]): list of key value pairs
+
+        """
         return y
 
     def save_flagged(self, dir, label, data, encryption_key):
@@ -380,6 +417,12 @@ class Audio(OutputComponent):
         }
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (Union[Tuple[int, numpy.array], str]): audio data in requested format
+        Returns:
+        (str): base64 url data
+        """
         if self.type in ["numpy", "file", "auto"]:
             if self.type == "numpy" or (self.type == "auto" and isinstance(y, tuple)):
                 sample_rate, data = y
@@ -416,6 +459,12 @@ class JSON(OutputComponent):
         super().__init__(label)
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (Union[Dict, List, str]): JSON output
+        Returns:
+        (Union[Dict, List]): JSON output
+        """
         if isinstance(y, str):
             return json.dumps(y)
         else:
@@ -448,6 +497,15 @@ class HTML(OutputComponent):
         '''
         super().__init__(label)
 
+    def postprocess(self, x):
+        """
+        Parameters:
+        y (str): HTML output
+        Returns:
+        (str): HTML output
+        """
+        return x
+
     @classmethod
     def get_shortcut_implementations(cls):
         return {
@@ -476,6 +534,12 @@ class File(OutputComponent):
         }
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (str): file path
+        Returns:
+        (Dict[name: str, size: number, data: str]): JSON object with key 'name' for filename, 'data' for base64 url, and 'size' for filesize in bytes 
+        """
         return {
             "name": os.path.basename(y),
             "size": os.path.getsize(y),
@@ -532,6 +596,12 @@ class Dataframe(OutputComponent):
         }
 
     def postprocess(self, y):
+        """
+        Parameters:
+        y (Union[pandas.DataFrame, numpy.array, List[Union[str, float]], List[List[Union[str, float]]]]): dataframe in given format
+        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
+        """
         if self.type == "auto":
             if isinstance(y, pd.core.frame.DataFrame):
                 dtype = "pandas"
@@ -589,6 +659,12 @@ class Carousel(OutputComponent):
         }
 
     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]
@@ -667,6 +743,12 @@ class Timeseries(OutputComponent):
         }
 
     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/templates/frontend/asset-manifest.json

@@ -1,6 +1,6 @@
 {
   "files": {
-    "main.css": "/static/css/main.ac4c682c.css",
+    "main.css": "/static/css/main.6169d032.css",
     "main.js": "/static/bundle.js",
     "index.html": "/index.html",
     "static/media/arrow-left.e497f657.svg": "/static/media/arrow-left.e497f657.svg",
@@ -10,7 +10,7 @@
   },
   "entrypoints": [
     "static/bundle.css",
-    "static/css/main.ac4c682c.css",
+    "static/css/main.6169d032.css",
     "static/bundle.js"
   ]
 }

gradio/templates/frontend/index.html

@@ -8,4 +8,4 @@
       window.config = {{ config|tojson }};
     } catch (e) {
       window.config = {};
-    }</script><script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script><title>Gradio</title><link href="static/bundle.css" rel="stylesheet"><link href="static/css/main.ac4c682c.css" rel="stylesheet"></head><body style="height:100%"><div id="root" style="height:100%"></div><script src="static/bundle.js"></script></body></html>
+    }</script><script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script><title>Gradio</title><link href="static/bundle.css" rel="stylesheet"><link href="static/css/main.6169d032.css" rel="stylesheet"></head><body style="height:100%"><div id="root" style="height:100%"></div><script src="static/bundle.js"></script></body></html>

test/test_inputs.py

@@ -89,14 +89,14 @@ class TestImage(unittest.TestCase):
 
 class TestAudio(unittest.TestCase):
     def test_as_component(self):
-        x_wav = {"name": "sample.wav", "data": gr.test_data.BASE64_AUDIO, "is_example": False}
+        x_wav = gr.test_data.BASE64_AUDIO
         audio_input = gr.inputs.Audio()
         output = audio_input.preprocess(x_wav)
         self.assertEqual(output[0], 8000)
         self.assertEqual(output[1].shape, (8046,))
 
     def test_in_interface(self):
-        x_wav = {"name": "sample.wav", "data": gr.test_data.BASE64_AUDIO, "is_example": False}
+        x_wav = gr.test_data.BASE64_AUDIO
         def max_amplitude_from_wav_file(wav_file):
             audio_segment = AudioSegment.from_file(wav_file.name)
             data = np.array(audio_segment.get_array_of_samples())
@@ -110,11 +110,7 @@ class TestAudio(unittest.TestCase):
 
 class TestFile(unittest.TestCase):
     def test_in_interface(self):
-        x_file = {
-            "name": "audio.wav",
-            "data": gr.test_data.BASE64_AUDIO,
-            "is_example": False
-        }
+        x_file =  gr.test_data.BASE64_AUDIO
         def get_size_of_file(file_obj):
             return os.path.getsize(file_obj.name)
 

test/test_outputs.py

@@ -108,7 +108,7 @@ class TestHighlightedText(unittest.TestCase):
 
 class TestAudio(unittest.TestCase):
     def test_as_component(self):
-        y_audio = gr.processing_utils.decode_base64_to_file(gr.test_data.BASE64_AUDIO)
+        y_audio = gr.processing_utils.decode_base64_to_file(gr.test_data.BASE64_AUDIO["data"])
         audio_output = gr.outputs.Audio(type="file")
         self.assertTrue(audio_output.postprocess(y_audio.name).startswith("data:audio/wav;base64,UklGRuI/AABXQVZFZm10IBAAA"))
 

upload_to_pypi.sh

@@ -18,7 +18,7 @@ fi
 read -p "frontend updates? " -r
 if [[ $REPLY =~ ^[Yy]$ ]]
 then
-    aws s3 cp gradio/frontend s3://gradio/$new_version/ --recursive 
+    aws s3 cp gradio/templates/frontend s3://gradio/$new_version/ --recursive 
     echo $new_version > gradio/version.txt
 fi