import copy import json import os import tempfile import unittest from difflib import SequenceMatcher import numpy as np import pandas import PIL import gradio as gr from gradio import media_data os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" # TODO: Delete this file after confirming backwards compatibility works well. class TestTextbox(unittest.TestCase): def test_as_component(self): text_input = gr.inputs.Textbox() self.assertEqual(text_input.preprocess("Hello World!"), "Hello World!") self.assertEqual(text_input.preprocess_example("Hello World!"), "Hello World!") self.assertEqual(text_input.serialize("Hello World!", True), "Hello World!") with tempfile.TemporaryDirectory() as tmpdirname: to_save = text_input.save_flagged( tmpdirname, "text_input", "Hello World!", None ) self.assertEqual(to_save, "Hello World!") restored = text_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, "Hello World!") with self.assertWarns(DeprecationWarning): _ = gr.inputs.Textbox(type="number") self.assertEqual( text_input.tokenize("Hello World! Gradio speaking."), ( ["Hello", "World!", "Gradio", "speaking."], [ "World! Gradio speaking.", "Hello Gradio speaking.", "Hello World! speaking.", "Hello World! Gradio", ], None, ), ) text_input.interpretation_replacement = "unknown" self.assertEqual( text_input.tokenize("Hello World! Gradio speaking."), ( ["Hello", "World!", "Gradio", "speaking."], [ "unknown World! Gradio speaking.", "Hello unknown Gradio speaking.", "Hello World! unknown speaking.", "Hello World! Gradio unknown", ], None, ), ) self.assertIsInstance(text_input.generate_sample(), str) def test_in_interface(self): iface = gr.Interface(lambda x: x[::-1], "textbox", "textbox") self.assertEqual(iface.process(["Hello"]), ["olleH"]) iface = gr.Interface( lambda sentence: max([len(word) for word in sentence.split()]), gr.inputs.Textbox(), "number", interpretation="default", ) scores = iface.interpret( ["Return the length of the longest word in this sentence"] )[0]["interpretation"] self.assertEqual( scores, [ ("Return", 0.0), (" ", 0), ("the", 0.0), (" ", 0), ("length", 0.0), (" ", 0), ("of", 0.0), (" ", 0), ("the", 0.0), (" ", 0), ("longest", 0.0), (" ", 0), ("word", 0.0), (" ", 0), ("in", 0.0), (" ", 0), ("this", 0.0), (" ", 0), ("sentence", 1.0), (" ", 0), ], ) class TestNumber(unittest.TestCase): def test_as_component(self): numeric_input = gr.inputs.Number(optional=True) self.assertEqual(numeric_input.preprocess(3), 3.0) self.assertEqual(numeric_input.preprocess(None), None) self.assertEqual(numeric_input.preprocess_example(3), 3) self.assertEqual(numeric_input.serialize(3, True), 3) with tempfile.TemporaryDirectory() as tmpdirname: to_save = numeric_input.save_flagged(tmpdirname, "numeric_input", 3, None) self.assertEqual(to_save, 3) restored = numeric_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, 3) self.assertIsInstance(numeric_input.generate_sample(), float) numeric_input.set_interpret_parameters(steps=3, delta=1, delta_type="absolute") self.assertEqual( numeric_input.get_interpretation_neighbors(1), ([-2.0, -1.0, 0.0, 2.0, 3.0, 4.0], {}), ) numeric_input.set_interpret_parameters(steps=3, delta=1, delta_type="percent") self.assertEqual( numeric_input.get_interpretation_neighbors(1), ([0.97, 0.98, 0.99, 1.01, 1.02, 1.03], {}), ) self.assertEqual( numeric_input.get_template_context(), { "default_value": None, "name": "number", "label": None, "css": {}, "interactive": None, }, ) def test_in_interface(self): iface = gr.Interface(lambda x: x**2, "number", "textbox") self.assertEqual(iface.process([2]), ["4.0"]) iface = gr.Interface( lambda x: x**2, "number", "number", interpretation="default" ) scores = iface.interpret([2])[0]["interpretation"] self.assertEqual( scores, [ (1.94, -0.23640000000000017), (1.96, -0.15840000000000032), (1.98, -0.07960000000000012), [2, None], (2.02, 0.08040000000000003), (2.04, 0.16159999999999997), (2.06, 0.24359999999999982), ], ) class TestSlider(unittest.TestCase): def test_as_component(self): slider_input = gr.inputs.Slider() self.assertEqual(slider_input.preprocess(3.0), 3.0) self.assertEqual(slider_input.preprocess_example(3), 3) self.assertEqual(slider_input.serialize(3, True), 3) with tempfile.TemporaryDirectory() as tmpdirname: to_save = slider_input.save_flagged(tmpdirname, "slider_input", 3, None) self.assertEqual(to_save, 3) restored = slider_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, 3) self.assertIsInstance(slider_input.generate_sample(), int) slider_input = gr.inputs.Slider( default=15, minimum=10, maximum=20, step=1, label="Slide Your Input" ) self.assertEqual( slider_input.get_template_context(), { "minimum": 10, "maximum": 20, "step": 1, "default_value": 15, "name": "slider", "label": "Slide Your Input", "css": {}, "interactive": None, }, ) def test_in_interface(self): iface = gr.Interface(lambda x: x**2, "slider", "textbox") self.assertEqual(iface.process([2]), ["4"]) iface = gr.Interface( lambda x: x**2, "slider", "number", interpretation="default" ) scores = iface.interpret([2])[0]["interpretation"] self.assertEqual( scores, [ -4.0, 200.08163265306123, 812.3265306122449, 1832.7346938775513, 3261.3061224489797, 5098.040816326531, 7342.938775510205, 9996.0, ], ) class TestCheckbox(unittest.TestCase): def test_as_component(self): bool_input = gr.inputs.Checkbox() self.assertEqual(bool_input.preprocess(True), True) self.assertEqual(bool_input.preprocess_example(True), True) self.assertEqual(bool_input.serialize(True, True), True) with tempfile.TemporaryDirectory() as tmpdirname: to_save = bool_input.save_flagged(tmpdirname, "bool_input", True, None) self.assertEqual(to_save, True) restored = bool_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, True) self.assertIsInstance(bool_input.generate_sample(), bool) bool_input = gr.inputs.Checkbox(default=True, label="Check Your Input") self.assertEqual( bool_input.get_template_context(), { "default_value": True, "name": "checkbox", "label": "Check Your Input", "css": {}, "interactive": None, }, ) def test_in_interface(self): iface = gr.Interface(lambda x: 1 if x else 0, "checkbox", "number") self.assertEqual(iface.process([True]), [1]) iface = gr.Interface( lambda x: 1 if x else 0, "checkbox", "number", interpretation="default" ) scores = iface.interpret([False])[0]["interpretation"] self.assertEqual(scores, (None, 1.0)) scores = iface.interpret([True])[0]["interpretation"] self.assertEqual(scores, (-1.0, None)) class TestCheckboxGroup(unittest.TestCase): def test_as_component(self): checkboxes_input = gr.inputs.CheckboxGroup(["a", "b", "c"]) self.assertEqual(checkboxes_input.preprocess(["a", "c"]), ["a", "c"]) self.assertEqual(checkboxes_input.preprocess_example(["a", "c"]), ["a", "c"]) self.assertEqual(checkboxes_input.serialize(["a", "c"], True), ["a", "c"]) with tempfile.TemporaryDirectory() as tmpdirname: to_save = checkboxes_input.save_flagged( tmpdirname, "checkboxes_input", ["a", "c"], None ) self.assertEqual(to_save, '["a", "c"]') restored = checkboxes_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, ["a", "c"]) self.assertIsInstance(checkboxes_input.generate_sample(), list) checkboxes_input = gr.inputs.CheckboxGroup( default=["a", "c"], choices=["a", "b", "c"], label="Check Your Inputs" ) self.assertEqual( checkboxes_input.get_template_context(), { "choices": ["a", "b", "c"], "default_value": ["a", "c"], "name": "checkboxgroup", "label": "Check Your Inputs", "css": {}, "interactive": None, }, ) with self.assertRaises(ValueError): wrong_type = gr.inputs.CheckboxGroup(["a"], type="unknown") wrong_type.preprocess(0) def test_in_interface(self): checkboxes_input = gr.inputs.CheckboxGroup(["a", "b", "c"]) iface = gr.Interface(lambda x: "|".join(x), checkboxes_input, "textbox") self.assertEqual(iface.process([["a", "c"]]), ["a|c"]) self.assertEqual(iface.process([[]]), [""]) checkboxes_input = gr.inputs.CheckboxGroup(["a", "b", "c"], type="index") class TestRadio(unittest.TestCase): def test_as_component(self): radio_input = gr.inputs.Radio(["a", "b", "c"]) self.assertEqual(radio_input.preprocess("c"), "c") self.assertEqual(radio_input.preprocess_example("a"), "a") self.assertEqual(radio_input.serialize("a", True), "a") with tempfile.TemporaryDirectory() as tmpdirname: to_save = radio_input.save_flagged(tmpdirname, "radio_input", "a", None) self.assertEqual(to_save, "a") restored = radio_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, "a") self.assertIsInstance(radio_input.generate_sample(), str) radio_input = gr.inputs.Radio( choices=["a", "b", "c"], default="a", label="Pick Your One Input" ) self.assertEqual( radio_input.get_template_context(), { "choices": ["a", "b", "c"], "default_value": "a", "name": "radio", "label": "Pick Your One Input", "css": {}, "interactive": None, }, ) with self.assertRaises(ValueError): wrong_type = gr.inputs.Radio(["a", "b"], type="unknown") wrong_type.preprocess(0) def test_in_interface(self): radio_input = gr.inputs.Radio(["a", "b", "c"]) iface = gr.Interface(lambda x: 2 * x, radio_input, "textbox") self.assertEqual(iface.process(["c"]), ["cc"]) radio_input = gr.inputs.Radio(["a", "b", "c"], type="index") iface = gr.Interface( lambda x: 2 * x, radio_input, "number", interpretation="default" ) self.assertEqual(iface.process(["c"]), [4]) scores = iface.interpret(["b"])[0]["interpretation"] self.assertEqual(scores, [-2.0, None, 2.0]) class TestDropdown(unittest.TestCase): def test_as_component(self): dropdown_input = gr.inputs.Dropdown(["a", "b", "c"]) self.assertEqual(dropdown_input.preprocess("c"), "c") self.assertEqual(dropdown_input.preprocess_example("a"), "a") self.assertEqual(dropdown_input.serialize("a", True), "a") with tempfile.TemporaryDirectory() as tmpdirname: to_save = dropdown_input.save_flagged( tmpdirname, "dropdown_input", "a", None ) self.assertEqual(to_save, "a") restored = dropdown_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, "a") self.assertIsInstance(dropdown_input.generate_sample(), str) dropdown_input = gr.inputs.Dropdown( choices=["a", "b", "c"], default="a", label="Drop Your Input" ) self.assertEqual( dropdown_input.get_template_context(), { "choices": ["a", "b", "c"], "default_value": "a", "name": "dropdown", "label": "Drop Your Input", "css": {}, "interactive": None, }, ) with self.assertRaises(ValueError): wrong_type = gr.inputs.Dropdown(["a"], type="unknown") wrong_type.preprocess(0) def test_in_interface(self): dropdown_input = gr.inputs.Dropdown(["a", "b", "c"]) iface = gr.Interface(lambda x: 2 * x, dropdown_input, "textbox") self.assertEqual(iface.process(["c"]), ["cc"]) dropdown = gr.inputs.Dropdown(["a", "b", "c"], type="index") iface = gr.Interface( lambda x: 2 * x, dropdown, "number", interpretation="default" ) self.assertEqual(iface.process(["c"]), [4]) scores = iface.interpret(["b"])[0]["interpretation"] self.assertEqual(scores, [-2.0, None, 2.0]) class TestImage(unittest.TestCase): def test_as_component(self): img = media_data.BASE64_IMAGE image_input = gr.inputs.Image() self.assertEqual(image_input.preprocess(img).shape, (68, 61, 3)) image_input = gr.inputs.Image(shape=(25, 25), image_mode="L") self.assertEqual(image_input.preprocess(img).shape, (25, 25)) image_input = gr.inputs.Image(shape=(30, 10), type="pil") self.assertEqual(image_input.preprocess(img).size, (30, 10)) self.assertEqual(image_input.preprocess_example("test/test_files/bus.png"), img) self.assertEqual(image_input.serialize("test/test_files/bus.png", True), img) with tempfile.TemporaryDirectory() as tmpdirname: to_save = image_input.save_flagged(tmpdirname, "image_input", img, None) self.assertEqual("image_input/0.png", to_save) to_save = image_input.save_flagged(tmpdirname, "image_input", img, None) self.assertEqual("image_input/1.png", to_save) restored = image_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, os.path.join(tmpdirname, "image_input/1.png")) self.assertIsInstance(image_input.generate_sample(), str) image_input = gr.inputs.Image( source="upload", tool="editor", type="pil", label="Upload Your Image" ) self.assertEqual( image_input.get_template_context(), { "image_mode": "RGB", "shape": None, "source": "upload", "tool": "editor", "name": "image", "label": "Upload Your Image", "css": {}, "default_value": None, "interactive": None, }, ) self.assertIsNone(image_input.preprocess(None)) image_input = gr.inputs.Image(invert_colors=True) self.assertIsNotNone(image_input.preprocess(img)) image_input.preprocess(img) with self.assertWarns(DeprecationWarning): file_image = gr.inputs.Image(type="file") file_image.preprocess(media_data.BASE64_IMAGE) file_image = gr.inputs.Image(type="filepath") self.assertIsInstance(file_image.preprocess(img), str) with self.assertRaises(ValueError): wrong_type = gr.inputs.Image(type="unknown") wrong_type.preprocess(img) with self.assertRaises(ValueError): wrong_type = gr.inputs.Image(type="unknown") wrong_type.serialize("test/test_files/bus.png", False) img_pil = PIL.Image.open("test/test_files/bus.png") image_input = gr.inputs.Image(type="numpy") self.assertIsInstance(image_input.serialize(img_pil, False), str) image_input = gr.inputs.Image(type="pil") self.assertIsInstance(image_input.serialize(img_pil, False), str) image_input = gr.inputs.Image(type="file") with open("test/test_files/bus.png") as f: self.assertEqual(image_input.serialize(f, False), img) image_input.shape = (30, 10) self.assertIsNotNone(image_input._segment_by_slic(img)) def test_in_interface(self): img = media_data.BASE64_IMAGE image_input = gr.inputs.Image() iface = gr.Interface( lambda x: PIL.Image.open(x).rotate(90, expand=True), gr.inputs.Image(shape=(30, 10), type="file"), "image", ) output = iface.process([img])[0] self.assertEqual( gr.processing_utils.decode_base64_to_image(output).size, (10, 30) ) iface = gr.Interface( lambda x: np.sum(x), image_input, "number", interpretation="default" ) scores = iface.interpret([img])[0]["interpretation"] self.assertEqual(scores, media_data.SUM_PIXELS_INTERPRETATION["scores"][0]) iface = gr.Interface( lambda x: np.sum(x), image_input, "label", interpretation="shap" ) scores = iface.interpret([img])[0]["interpretation"] self.assertEqual( len(scores[0]), len(media_data.SUM_PIXELS_SHAP_INTERPRETATION["scores"][0][0]), ) image_input = gr.inputs.Image(shape=(30, 10)) iface = gr.Interface( lambda x: np.sum(x), image_input, "number", interpretation="default" ) self.assertIsNotNone(iface.interpret([img])) class TestAudio(unittest.TestCase): def test_as_component(self): x_wav = copy.deepcopy(media_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,)) self.assertEqual( audio_input.serialize("test/test_files/audio_sample.wav", True)["data"], x_wav["data"], ) with tempfile.TemporaryDirectory() as tmpdirname: to_save = audio_input.save_flagged(tmpdirname, "audio_input", x_wav, None) self.assertEqual("audio_input/0.wav", to_save) to_save = audio_input.save_flagged(tmpdirname, "audio_input", x_wav, None) self.assertEqual("audio_input/1.wav", to_save) restored = audio_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, "audio_input/1.wav") self.assertIsInstance(audio_input.generate_sample(), dict) audio_input = gr.inputs.Audio(label="Upload Your Audio") self.assertEqual( audio_input.get_template_context(), { "source": "upload", "name": "audio", "label": "Upload Your Audio", "css": {}, "default_value": None, "interactive": None, }, ) self.assertIsNone(audio_input.preprocess(None)) x_wav["is_example"] = True x_wav["crop_min"], x_wav["crop_max"] = 1, 4 self.assertIsNotNone(audio_input.preprocess(x_wav)) with self.assertWarns(DeprecationWarning): audio_input = gr.inputs.Audio(type="file") audio_input.preprocess(x_wav) with open("test/test_files/audio_sample.wav") as f: audio_input.serialize(f, False) audio_input = gr.inputs.Audio(type="filepath") self.assertIsInstance(audio_input.preprocess(x_wav), str) with self.assertRaises(ValueError): audio_input = gr.inputs.Audio(type="unknown") audio_input.preprocess(x_wav) audio_input.serialize(x_wav, False) audio_input = gr.inputs.Audio(type="numpy") x_wav = gr.processing_utils.audio_from_file("test/test_files/audio_sample.wav") self.assertIsInstance(audio_input.serialize(x_wav, False), dict) def test_tokenize(self): x_wav = media_data.BASE64_AUDIO audio_input = gr.inputs.Audio() tokens, _, _ = audio_input.tokenize(x_wav) self.assertEquals(len(tokens), audio_input.interpretation_segments) x_new = audio_input.get_masked_inputs(tokens, [[1] * len(tokens)])[0] similarity = SequenceMatcher(a=x_wav["data"], b=x_new).ratio() self.assertGreater(similarity, 0.9) class TestFile(unittest.TestCase): def test_as_component(self): x_file = media_data.BASE64_FILE file_input = gr.inputs.File() output = file_input.preprocess(x_file) self.assertIsInstance(output, tempfile._TemporaryFileWrapper) self.assertEqual( file_input.serialize("test/test_files/sample_file.pdf", True), "test/test_files/sample_file.pdf", ) with tempfile.TemporaryDirectory() as tmpdirname: to_save = file_input.save_flagged(tmpdirname, "file_input", [x_file], None) self.assertEqual("file_input/0", to_save) to_save = file_input.save_flagged(tmpdirname, "file_input", [x_file], None) self.assertEqual("file_input/1", to_save) restored = file_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, "file_input/1") self.assertIsInstance(file_input.generate_sample(), dict) file_input = gr.inputs.File(label="Upload Your File") self.assertEqual( file_input.get_template_context(), { "file_count": "single", "name": "file", "label": "Upload Your File", "css": {}, "default_value": None, "interactive": None, }, ) self.assertIsNone(file_input.preprocess(None)) x_file["is_example"] = True self.assertIsNotNone(file_input.preprocess(x_file)) def test_in_interface(self): x_file = media_data.BASE64_FILE def get_size_of_file(file_obj): return os.path.getsize(file_obj.name) iface = gr.Interface(get_size_of_file, "file", "number") self.assertEqual(iface.process([[x_file]]), [10558]) class TestDataframe(unittest.TestCase): def test_as_component(self): x_data = [["Tim", 12, False], ["Jan", 24, True]] dataframe_input = gr.inputs.Dataframe(headers=["Name", "Age", "Member"]) output = dataframe_input.preprocess(x_data) self.assertEqual(output["Age"][1], 24) self.assertEqual(output["Member"][0], False) self.assertEqual(dataframe_input.preprocess_example(x_data), x_data) self.assertEqual(dataframe_input.serialize(x_data, True), x_data) with tempfile.TemporaryDirectory() as tmpdirname: to_save = dataframe_input.save_flagged( tmpdirname, "dataframe_input", x_data, None ) self.assertEqual(json.dumps(x_data), to_save) restored = dataframe_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(x_data, restored) self.assertIsInstance(dataframe_input.generate_sample(), list) dataframe_input = gr.inputs.Dataframe( headers=["Name", "Age", "Member"], label="Dataframe Input" ) self.assertEqual( dataframe_input.get_template_context(), { "headers": ["Name", "Age", "Member"], "datatype": "str", "row_count": 3, "col_count": 3, "col_width": None, "default_value": [ ["", "", ""], ["", "", ""], ["", "", ""], ], "name": "dataframe", "label": "Dataframe Input", "max_rows": 20, "max_cols": None, "overflow_row_behaviour": "paginate", "css": {}, "interactive": None, }, ) dataframe_input = gr.inputs.Dataframe() output = dataframe_input.preprocess(x_data) self.assertEqual(output[1][1], 24) with self.assertRaises(ValueError): wrong_type = gr.inputs.Dataframe(type="unknown") wrong_type.preprocess(x_data) def test_in_interface(self): x_data = [[1, 2, 3], [4, 5, 6]] iface = gr.Interface(np.max, "numpy", "number") self.assertEqual(iface.process([x_data]), [6]) x_data = [["Tim"], ["Jon"], ["Sal"]] def get_last(my_list): return my_list[-1] iface = gr.Interface(get_last, "list", "text") self.assertEqual(iface.process([x_data]), ["Sal"]) class TestVideo(unittest.TestCase): def test_as_component(self): x_video = media_data.BASE64_VIDEO video_input = gr.inputs.Video() output = video_input.preprocess(x_video) self.assertIsInstance(output, str) with tempfile.TemporaryDirectory() as tmpdirname: to_save = video_input.save_flagged(tmpdirname, "video_input", x_video, None) self.assertEqual("video_input/0.mp4", to_save) to_save = video_input.save_flagged(tmpdirname, "video_input", x_video, None) self.assertEqual("video_input/1.mp4", to_save) restored = video_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored, "video_input/1.mp4") self.assertIsInstance(video_input.generate_sample(), dict) video_input = gr.inputs.Video(label="Upload Your Video") self.assertEqual( video_input.get_template_context(), { "source": "upload", "name": "video", "label": "Upload Your Video", "css": {}, "default_value": None, "interactive": None, }, ) self.assertIsNone(video_input.preprocess(None)) x_video["is_example"] = True self.assertIsNotNone(video_input.preprocess(x_video)) video_input = gr.inputs.Video(type="avi") # self.assertEqual(video_input.preprocess(x_video)[-3:], "avi") with self.assertRaises(NotImplementedError): video_input.serialize(x_video, True) def test_in_interface(self): x_video = media_data.BASE64_VIDEO iface = gr.Interface(lambda x: x, "video", "playable_video") self.assertEqual(iface.process([x_video])[0]["data"], x_video["data"]) class TestTimeseries(unittest.TestCase): def test_as_component(self): timeseries_input = gr.inputs.Timeseries(x="time", y=["retail", "food", "other"]) x_timeseries = { "data": [[1] + [2] * len(timeseries_input.y)] * 4, "headers": [timeseries_input.x] + timeseries_input.y, } output = timeseries_input.preprocess(x_timeseries) self.assertIsInstance(output, pandas.core.frame.DataFrame) with tempfile.TemporaryDirectory() as tmpdirname: to_save = timeseries_input.save_flagged( tmpdirname, "video_input", x_timeseries, None ) self.assertEqual(json.dumps(x_timeseries), to_save) restored = timeseries_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(x_timeseries, restored) self.assertIsInstance(timeseries_input.generate_sample(), dict) timeseries_input = gr.inputs.Timeseries( x="time", y="retail", label="Upload Your Timeseries" ) self.assertEqual( timeseries_input.get_template_context(), { "x": "time", "y": ["retail"], "name": "timeseries", "label": "Upload Your Timeseries", "css": {}, "default_value": None, "interactive": None, }, ) self.assertIsNone(timeseries_input.preprocess(None)) x_timeseries["range"] = (0, 1) self.assertIsNotNone(timeseries_input.preprocess(x_timeseries)) def test_in_interface(self): timeseries_input = gr.inputs.Timeseries(x="time", y=["retail", "food", "other"]) x_timeseries = { "data": [[1] + [2] * len(timeseries_input.y)] * 4, "headers": [timeseries_input.x] + timeseries_input.y, } iface = gr.Interface(lambda x: x, timeseries_input, "dataframe") self.assertEqual( iface.process([x_timeseries]), [ { "headers": ["time", "retail", "food", "other"], "data": [[1, 2, 2, 2], [1, 2, 2, 2], [1, 2, 2, 2], [1, 2, 2, 2]], } ], ) class TestImage3D(unittest.TestCase): def test_as_component(self): Image3D = media_data.BASE64_MODEL3D Image3D_input = gr.inputs.Image3D() output = Image3D_input.preprocess(Image3D) self.assertIsInstance(output, str) with tempfile.TemporaryDirectory() as tmpdirname: to_save = Image3D_input.save_flagged( tmpdirname, "Image3D_input", Image3D, None ) self.assertEqual("Image3D_input/0", to_save) to_save = Image3D_input.save_flagged( tmpdirname, "Image3D_input", Image3D, None ) self.assertEqual("Image3D_input/1", to_save) restored = Image3D_input.restore_flagged(tmpdirname, to_save, None) self.assertEqual(restored["name"], "Image3D_input/1") self.assertIsInstance(Image3D_input.generate_sample(), dict) Image3D_input = gr.inputs.Image3D(label="Upload Your 3D Image Model") self.assertEqual( Image3D_input.get_template_context(), { "clearColor": None, "name": "image3d", "css": {}, "interactive": None, "label": "Upload Your 3D Image Model", }, ) self.assertIsNone(Image3D_input.preprocess(None)) Image3D["is_example"] = True self.assertIsNotNone(Image3D_input.preprocess(Image3D)) Image3D_input = gr.inputs.Image3D() with self.assertRaises(NotImplementedError): Image3D_input.serialize(Image3D, True) def test_in_interface(self): Image3D = media_data.BASE64_MODEL3D iface = gr.Interface(lambda x: x, "model3d", "model3d") self.assertEqual( iface.process([Image3D])[0]["data"], Image3D["data"].replace("@file/gltf", ""), ) class TestNames(unittest.TestCase): # this ensures that `components.get_component_instance()` works correctly when instantiating from components def test_no_duplicate_uncased_names(self): subclasses = gr.components.Component.__subclasses__() unique_subclasses_uncased = set([s.__name__.lower() for s in subclasses]) self.assertEqual(len(subclasses), len(unique_subclasses_uncased)) if __name__ == "__main__": unittest.main()