mirror of
https://github.com/gradio-app/gradio.git
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ceee0fce9e
- use deepcopy in the remaining tests as well - update a failing test - add processing utils to init for IDE to identify the function
1497 lines
55 KiB
Python
1497 lines
55 KiB
Python
import json
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import os
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import tempfile
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import unittest
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from copy import deepcopy
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from difflib import SequenceMatcher
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from test.test_data import media_data
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import PIL
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import gradio as gr
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os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
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"""
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Tests are divided into two
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1. test_component_functionalities are unit tests that check essential functions of a component, the functions that are checked are documented in the docstring.
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2. test_in_interface_... are functional tests that check a component's functionalities inside an Interface. Please do not use Interface.launch() in this file, as it slow downs the tests.
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"""
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class TestTextbox(unittest.TestCase):
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def test_component_functionalities(self):
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"""
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Preprocess, postprocess, serialize, save_flagged, restore_flagged, tokenize, generate_sample, get_template_context
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"""
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text_input = gr.Textbox()
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self.assertEqual(text_input.preprocess("Hello World!"), "Hello World!")
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self.assertEqual(text_input.preprocess_example("Hello World!"), "Hello World!")
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self.assertEqual(text_input.postprocess(None), None)
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self.assertEqual(text_input.postprocess("Ali"), "Ali")
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self.assertEqual(text_input.postprocess(2), "2")
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self.assertEqual(text_input.postprocess(2.14), "2.14")
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self.assertEqual(text_input.serialize("Hello World!", True), "Hello World!")
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = text_input.save_flagged(
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tmpdirname, "text_input", "Hello World!", None
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)
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self.assertEqual(to_save, "Hello World!")
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restored = text_input.restore_flagged(tmpdirname, to_save, None)
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self.assertEqual(restored, "Hello World!")
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with self.assertWarns(DeprecationWarning):
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_ = gr.Textbox(type="number")
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self.assertEqual(
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text_input.tokenize("Hello World! Gradio speaking."),
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(
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["Hello", "World!", "Gradio", "speaking."],
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[
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"World! Gradio speaking.",
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"Hello Gradio speaking.",
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"Hello World! speaking.",
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"Hello World! Gradio",
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],
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None,
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),
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)
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text_input.interpretation_replacement = "unknown"
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self.assertEqual(
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text_input.tokenize("Hello World! Gradio speaking."),
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(
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["Hello", "World!", "Gradio", "speaking."],
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[
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"unknown World! Gradio speaking.",
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"Hello unknown Gradio speaking.",
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"Hello World! unknown speaking.",
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"Hello World! Gradio unknown",
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],
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None,
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),
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)
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self.assertEqual(
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text_input.get_template_context(),
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{
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"lines": 1,
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"placeholder": None,
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"default_value": "",
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"name": "textbox",
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"label": None,
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"css": {},
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},
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)
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self.assertIsInstance(text_input.generate_sample(), str)
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def test_in_interface_as_input(self):
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"""
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Interface, process, interpret,
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"""
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iface = gr.Interface(lambda x: x[::-1], "textbox", "textbox")
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self.assertEqual(iface.process(["Hello"])[0], ["olleH"])
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iface = gr.Interface(
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lambda sentence: max([len(word) for word in sentence.split()]),
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gr.Textbox(),
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"number",
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interpretation="default",
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)
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scores, alternative_outputs = iface.interpret(
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["Return the length of the longest word in this sentence"]
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)
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self.assertEqual(
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scores,
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[
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[
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("Return", 0.0),
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(" ", 0),
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("the", 0.0),
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(" ", 0),
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("length", 0.0),
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(" ", 0),
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("of", 0.0),
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(" ", 0),
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("the", 0.0),
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(" ", 0),
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("longest", 0.0),
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(" ", 0),
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("word", 0.0),
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(" ", 0),
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("in", 0.0),
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(" ", 0),
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("this", 0.0),
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(" ", 0),
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("sentence", 1.0),
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(" ", 0),
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]
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],
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)
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self.assertEqual(
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alternative_outputs,
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[[[8], [8], [8], [8], [8], [8], [8], [8], [8], [7]]],
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)
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def test_in_interface_as_output(self):
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"""
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Interface, process
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"""
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iface = gr.Interface(lambda x: x[-1], "textbox", gr.Textbox())
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self.assertEqual(iface.process(["Hello"])[0], ["o"])
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iface = gr.Interface(lambda x: x / 2, "number", gr.Textbox())
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self.assertEqual(iface.process([10])[0], ["5.0"])
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class TestNumber(unittest.TestCase):
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def test_component_functionalities(self):
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"""
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Preprocess, postprocess, serialize, save_flagged, restore_flagged, generate_sample, set_interpret_parameters, get_interpretation_neighbors, get_template_context
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"""
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numeric_input = gr.Number()
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self.assertEqual(numeric_input.preprocess(3), 3.0)
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self.assertEqual(numeric_input.preprocess(None), None)
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self.assertEqual(numeric_input.preprocess_example(3), 3)
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self.assertEqual(numeric_input.postprocess(3), 3.0)
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self.assertEqual(numeric_input.postprocess(2.14), 2.14)
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self.assertEqual(numeric_input.postprocess(None), None)
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self.assertEqual(numeric_input.serialize(3, True), 3)
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = numeric_input.save_flagged(tmpdirname, "numeric_input", 3, None)
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self.assertEqual(to_save, 3)
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restored = numeric_input.restore_flagged(tmpdirname, to_save, None)
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self.assertEqual(restored, 3)
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self.assertIsInstance(numeric_input.generate_sample(), float)
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numeric_input.set_interpret_parameters(steps=3, delta=1, delta_type="absolute")
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self.assertEqual(
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numeric_input.get_interpretation_neighbors(1),
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([-2.0, -1.0, 0.0, 2.0, 3.0, 4.0], {}),
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)
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numeric_input.set_interpret_parameters(steps=3, delta=1, delta_type="percent")
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self.assertEqual(
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numeric_input.get_interpretation_neighbors(1),
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([0.97, 0.98, 0.99, 1.01, 1.02, 1.03], {}),
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)
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self.assertEqual(
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numeric_input.get_template_context(),
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{"default_value": None, "name": "number", "label": None, "css": {}},
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)
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def test_in_interface_as_input(self):
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"""
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Interface, process, interpret
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"""
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iface = gr.Interface(lambda x: x**2, "number", "textbox")
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self.assertEqual(iface.process([2])[0], ["4.0"])
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iface = gr.Interface(
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lambda x: x**2, "number", "number", interpretation="default"
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)
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scores, alternative_outputs = iface.interpret([2])
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self.assertEqual(
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scores,
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[
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[
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(1.94, -0.23640000000000017),
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(1.96, -0.15840000000000032),
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(1.98, -0.07960000000000012),
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[2, None],
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(2.02, 0.08040000000000003),
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(2.04, 0.16159999999999997),
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(2.06, 0.24359999999999982),
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]
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],
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)
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self.assertEqual(
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alternative_outputs,
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[
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[
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[3.7636],
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[3.8415999999999997],
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[3.9204],
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[4.0804],
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[4.1616],
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[4.2436],
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]
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],
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)
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def test_in_interface_as_output(self):
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"""
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Interface, process, interpret
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"""
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iface = gr.Interface(lambda x: int(x) ** 2, "textbox", "number")
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self.assertEqual(iface.process([2])[0], [4.0])
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iface = gr.Interface(
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lambda x: x**2, "number", "number", interpretation="default"
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)
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scores, alternative_outputs = iface.interpret([2])
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self.assertEqual(
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scores,
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[
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[
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(1.94, -0.23640000000000017),
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(1.96, -0.15840000000000032),
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(1.98, -0.07960000000000012),
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[2, None],
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(2.02, 0.08040000000000003),
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(2.04, 0.16159999999999997),
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(2.06, 0.24359999999999982),
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]
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],
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)
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self.assertEqual(
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alternative_outputs,
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[
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[
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[3.7636],
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[3.8415999999999997],
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[3.9204],
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[4.0804],
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[4.1616],
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[4.2436],
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]
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],
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)
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class TestSlider(unittest.TestCase):
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def test_component_functionalities(self):
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"""
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Preprocess, postprocess, serialize, save_flagged, restore_flagged, generate_sample, get_template_context
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"""
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slider_input = gr.Slider()
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self.assertEqual(slider_input.preprocess(3.0), 3.0)
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self.assertEqual(slider_input.preprocess_example(3), 3)
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self.assertEqual(slider_input.postprocess(3), 3)
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self.assertEqual(slider_input.postprocess(None), None)
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self.assertEqual(slider_input.serialize(3, True), 3)
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = slider_input.save_flagged(tmpdirname, "slider_input", 3, None)
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self.assertEqual(to_save, 3)
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restored = slider_input.restore_flagged(tmpdirname, to_save, None)
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self.assertEqual(restored, 3)
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self.assertIsInstance(slider_input.generate_sample(), int)
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slider_input = gr.Slider(
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default_value=15, minimum=10, maximum=20, step=1, label="Slide Your Input"
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)
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self.assertEqual(
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slider_input.get_template_context(),
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{
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"minimum": 10,
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"maximum": 20,
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"step": 1,
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"default_value": 15,
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"name": "slider",
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"label": "Slide Your Input",
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"css": {},
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},
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)
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def test_in_interface(self):
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""" "
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Interface, process, interpret
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"""
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iface = gr.Interface(lambda x: x**2, "slider", "textbox")
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self.assertEqual(iface.process([2])[0], ["4"])
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iface = gr.Interface(
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lambda x: x**2, "slider", "number", interpretation="default"
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)
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scores, alternative_outputs = iface.interpret([2])
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self.assertEqual(
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scores,
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[
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[
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-4.0,
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200.08163265306123,
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812.3265306122449,
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1832.7346938775513,
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3261.3061224489797,
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5098.040816326531,
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7342.938775510205,
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9996.0,
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]
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],
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)
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self.assertEqual(
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alternative_outputs,
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[
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[
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[0.0],
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[204.08163265306123],
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[816.3265306122449],
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[1836.7346938775513],
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[3265.3061224489797],
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[5102.040816326531],
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[7346.938775510205],
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[10000.0],
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]
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],
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)
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|
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class TestCheckbox(unittest.TestCase):
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def test_component_functionalities(self):
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"""
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Preprocess, postprocess, serialize, generate_sample, get_template_context
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"""
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bool_input = gr.Checkbox()
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self.assertEqual(bool_input.preprocess(True), True)
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self.assertEqual(bool_input.preprocess_example(True), True)
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self.assertEqual(bool_input.postprocess(True), True)
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self.assertEqual(bool_input.serialize(True, True), True)
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = bool_input.save_flagged(tmpdirname, "bool_input", True, None)
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self.assertEqual(to_save, True)
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restored = bool_input.restore_flagged(tmpdirname, to_save, None)
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self.assertEqual(restored, True)
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self.assertIsInstance(bool_input.generate_sample(), bool)
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bool_input = gr.Checkbox(default_value=True, label="Check Your Input")
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self.assertEqual(
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bool_input.get_template_context(),
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{
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"default_value": True,
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"name": "checkbox",
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"label": "Check Your Input",
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"css": {},
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},
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)
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def test_in_interface(self):
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"""
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Interface, process, interpret
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"""
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iface = gr.Interface(lambda x: 1 if x else 0, "checkbox", "number")
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self.assertEqual(iface.process([True])[0], [1])
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iface = gr.Interface(
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lambda x: 1 if x else 0, "checkbox", "number", interpretation="default"
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)
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scores, alternative_outputs = iface.interpret([False])
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self.assertEqual(scores, [(None, 1.0)])
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self.assertEqual(alternative_outputs, [[[1]]])
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scores, alternative_outputs = iface.interpret([True])
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self.assertEqual(scores, [(-1.0, None)])
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self.assertEqual(alternative_outputs, [[[0]]])
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|
|
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class TestCheckboxGroup(unittest.TestCase):
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def test_component_functionalities(self):
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"""
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Preprocess, preprocess_example, serialize, save_flagged, restore_flagged, generate_sample, get_template_context
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"""
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checkboxes_input = gr.CheckboxGroup(["a", "b", "c"])
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self.assertEqual(checkboxes_input.preprocess(["a", "c"]), ["a", "c"])
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self.assertEqual(checkboxes_input.preprocess_example(["a", "c"]), ["a", "c"])
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self.assertEqual(checkboxes_input.serialize(["a", "c"], True), ["a", "c"])
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = checkboxes_input.save_flagged(
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tmpdirname, "checkboxes_input", ["a", "c"], None
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)
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self.assertEqual(to_save, '["a", "c"]')
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restored = checkboxes_input.restore_flagged(tmpdirname, to_save, None)
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self.assertEqual(restored, ["a", "c"])
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self.assertIsInstance(checkboxes_input.generate_sample(), list)
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checkboxes_input = gr.CheckboxGroup(
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default_selected=["a", "c"],
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choices=["a", "b", "c"],
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label="Check Your Inputs",
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)
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self.assertEqual(
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checkboxes_input.get_template_context(),
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{
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"choices": ["a", "b", "c"],
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"default_value": ["a", "c"],
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"name": "checkboxgroup",
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"label": "Check Your Inputs",
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"css": {},
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},
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)
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with self.assertRaises(ValueError):
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wrong_type = gr.CheckboxGroup(["a"], type="unknown")
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wrong_type.preprocess(0)
|
|
|
|
def test_in_interface(self):
|
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"""
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Interface, process
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"""
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checkboxes_input = gr.CheckboxGroup(["a", "b", "c"])
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iface = gr.Interface(lambda x: "|".join(x), checkboxes_input, "textbox")
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self.assertEqual(iface.process([["a", "c"]])[0], ["a|c"])
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self.assertEqual(iface.process([[]])[0], [""])
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_ = gr.CheckboxGroup(["a", "b", "c"], type="index")
|
|
|
|
|
|
class TestRadio(unittest.TestCase):
|
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def test_component_functionalities(self):
|
|
"""
|
|
Preprocess, preprocess_example, serialize, save_flagged, generate_sample, get_template_context
|
|
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|
"""
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radio_input = gr.Radio(["a", "b", "c"])
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self.assertEqual(radio_input.preprocess("c"), "c")
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self.assertEqual(radio_input.preprocess_example("a"), "a")
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self.assertEqual(radio_input.serialize("a", True), "a")
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with tempfile.TemporaryDirectory() as tmpdirname:
|
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to_save = radio_input.save_flagged(tmpdirname, "radio_input", "a", None)
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self.assertEqual(to_save, "a")
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restored = radio_input.restore_flagged(tmpdirname, to_save, None)
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self.assertEqual(restored, "a")
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self.assertIsInstance(radio_input.generate_sample(), str)
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radio_input = gr.Radio(
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choices=["a", "b", "c"], default="a", label="Pick Your One Input"
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)
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|
self.assertEqual(
|
|
radio_input.get_template_context(),
|
|
{
|
|
"choices": ["a", "b", "c"],
|
|
"default_value": "a",
|
|
"name": "radio",
|
|
"label": "Pick Your One Input",
|
|
"css": {},
|
|
},
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
wrong_type = gr.Radio(["a", "b"], type="unknown")
|
|
wrong_type.preprocess(0)
|
|
|
|
def test_in_interface(self):
|
|
"""
|
|
Interface, process, interpret
|
|
"""
|
|
radio_input = gr.Radio(["a", "b", "c"])
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|
iface = gr.Interface(lambda x: 2 * x, radio_input, "textbox")
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|
self.assertEqual(iface.process(["c"])[0], ["cc"])
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radio_input = gr.Radio(["a", "b", "c"], type="index")
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iface = gr.Interface(
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lambda x: 2 * x, radio_input, "number", interpretation="default"
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|
)
|
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self.assertEqual(iface.process(["c"])[0], [4])
|
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scores, alternative_outputs = iface.interpret(["b"])
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|
self.assertEqual(scores, [[-2.0, None, 2.0]])
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|
self.assertEqual(alternative_outputs, [[[0], [4]]])
|
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|
|
|
|
class TestImage(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Preprocess, postprocess, serialize, save_flagged, restore_flagged, generate_sample, get_template_context, _segment_by_slic
|
|
type: pil, file, filepath, numpy
|
|
"""
|
|
img = deepcopy(media_data.BASE64_IMAGE)
|
|
image_input = gr.Image()
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|
self.assertEqual(image_input.preprocess(img).shape, (68, 61, 3))
|
|
image_input = gr.Image(shape=(25, 25), image_mode="L")
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|
self.assertEqual(image_input.preprocess(img).shape, (25, 25))
|
|
image_input = gr.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, "image_input/1.png")
|
|
|
|
self.assertIsInstance(image_input.generate_sample(), str)
|
|
image_input = gr.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,
|
|
},
|
|
)
|
|
self.assertIsNone(image_input.preprocess(None))
|
|
image_input = gr.Image(invert_colors=True)
|
|
self.assertIsNotNone(image_input.preprocess(img))
|
|
image_input.preprocess(img)
|
|
with self.assertWarns(DeprecationWarning):
|
|
file_image = gr.Image(type="file")
|
|
file_image.preprocess(deepcopy(media_data.BASE64_IMAGE))
|
|
file_image = gr.Image(type="filepath")
|
|
self.assertIsInstance(file_image.preprocess(img), str)
|
|
with self.assertRaises(ValueError):
|
|
wrong_type = gr.Image(type="unknown")
|
|
wrong_type.preprocess(img)
|
|
with self.assertRaises(ValueError):
|
|
wrong_type = gr.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.Image(type="numpy")
|
|
self.assertIsInstance(image_input.serialize(img_pil, False), str)
|
|
image_input = gr.Image(type="pil")
|
|
self.assertIsInstance(image_input.serialize(img_pil, False), str)
|
|
image_input = gr.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))
|
|
|
|
# Output functionalities
|
|
y_img = gr.processing_utils.decode_base64_to_image(
|
|
deepcopy(media_data.BASE64_IMAGE)
|
|
)
|
|
image_output = gr.Image()
|
|
self.assertTrue(
|
|
image_output.postprocess(y_img).startswith(
|
|
"data:image/png;base64,iVBORw0KGgoAAA"
|
|
)
|
|
)
|
|
self.assertTrue(
|
|
image_output.postprocess(np.array(y_img)).startswith(
|
|
"data:image/png;base64,iVBORw0KGgoAAA"
|
|
)
|
|
)
|
|
with self.assertWarns(DeprecationWarning):
|
|
plot_output = gr.Image(plot=True)
|
|
|
|
xpoints = np.array([0, 6])
|
|
ypoints = np.array([0, 250])
|
|
fig = plt.figure()
|
|
plt.plot(xpoints, ypoints)
|
|
self.assertTrue(
|
|
plot_output.postprocess(fig).startswith("data:image/png;base64,")
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
image_output.postprocess([1, 2, 3])
|
|
image_output = gr.Image(type="numpy")
|
|
self.assertTrue(
|
|
image_output.postprocess(y_img).startswith("data:image/png;base64,")
|
|
)
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
to_save = image_output.save_flagged(
|
|
tmpdirname, "image_output", deepcopy(media_data.BASE64_IMAGE), None
|
|
)
|
|
self.assertEqual("image_output/0.png", to_save)
|
|
to_save = image_output.save_flagged(
|
|
tmpdirname, "image_output", deepcopy(media_data.BASE64_IMAGE), None
|
|
)
|
|
self.assertEqual("image_output/1.png", to_save)
|
|
|
|
def test_in_interface_as_input(self):
|
|
"""
|
|
Interface, process, interpret
|
|
type: file
|
|
interpretation: default, shap,
|
|
"""
|
|
img = deepcopy(media_data.BASE64_IMAGE)
|
|
image_input = gr.Image()
|
|
iface = gr.Interface(
|
|
lambda x: PIL.Image.open(x).rotate(90, expand=True),
|
|
gr.Image(shape=(30, 10), type="file"),
|
|
"image",
|
|
)
|
|
output = iface.process([img])[0][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, alternative_outputs = iface.interpret([img])
|
|
self.assertEqual(
|
|
scores, deepcopy(media_data.SUM_PIXELS_INTERPRETATION)["scores"]
|
|
)
|
|
self.assertEqual(
|
|
alternative_outputs,
|
|
deepcopy(media_data.SUM_PIXELS_INTERPRETATION)["alternative_outputs"],
|
|
)
|
|
iface = gr.Interface(
|
|
lambda x: np.sum(x), image_input, "label", interpretation="shap"
|
|
)
|
|
scores, alternative_outputs = iface.interpret([img])
|
|
self.assertEqual(
|
|
len(scores[0]),
|
|
len(deepcopy(media_data.SUM_PIXELS_SHAP_INTERPRETATION)["scores"][0]),
|
|
)
|
|
self.assertEqual(
|
|
len(alternative_outputs[0]),
|
|
len(
|
|
deepcopy(media_data.SUM_PIXELS_SHAP_INTERPRETATION)[
|
|
"alternative_outputs"
|
|
][0]
|
|
),
|
|
)
|
|
image_input = gr.Image(shape=(30, 10))
|
|
iface = gr.Interface(
|
|
lambda x: np.sum(x), image_input, "number", interpretation="default"
|
|
)
|
|
self.assertIsNotNone(iface.interpret([img]))
|
|
|
|
def test_in_interface_as_output(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
|
|
def generate_noise(width, height):
|
|
return np.random.randint(0, 256, (width, height, 3))
|
|
|
|
iface = gr.Interface(generate_noise, ["slider", "slider"], "image")
|
|
self.assertTrue(
|
|
iface.process([10, 20])[0][0].startswith("data:image/png;base64")
|
|
)
|
|
|
|
|
|
class TestAudio(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Preprocess, postprocess serialize, save_flagged, restore_flagged, generate_sample, get_template_context, deserialize
|
|
type: filepath, numpy, file
|
|
"""
|
|
x_wav = deepcopy(media_data.BASE64_AUDIO)
|
|
audio_input = gr.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.Audio(label="Upload Your Audio")
|
|
self.assertEqual(
|
|
audio_input.get_template_context(),
|
|
{
|
|
"source": "upload",
|
|
"name": "audio",
|
|
"label": "Upload Your Audio",
|
|
"css": {},
|
|
"default_value": 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.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.Audio(type="filepath")
|
|
self.assertIsInstance(audio_input.preprocess(x_wav), str)
|
|
with self.assertRaises(ValueError):
|
|
audio_input = gr.Audio(type="unknown")
|
|
audio_input.preprocess(x_wav)
|
|
audio_input.serialize(x_wav, False)
|
|
audio_input = gr.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)
|
|
|
|
# Output functionalities
|
|
y_audio = gr.processing_utils.decode_base64_to_file(
|
|
deepcopy(media_data.BASE64_AUDIO)["data"]
|
|
)
|
|
audio_output = gr.Audio(type="file")
|
|
self.assertTrue(
|
|
audio_output.postprocess(y_audio.name).startswith(
|
|
"data:audio/wav;base64,UklGRuI/AABXQVZFZm10IBAAA"
|
|
)
|
|
)
|
|
self.assertEqual(
|
|
audio_output.get_template_context(),
|
|
{
|
|
"name": "audio",
|
|
"label": None,
|
|
"source": "upload",
|
|
"css": {},
|
|
"default_value": None,
|
|
},
|
|
)
|
|
self.assertTrue(
|
|
audio_output.deserialize(
|
|
deepcopy(media_data.BASE64_AUDIO)["data"]
|
|
).endswith(".wav")
|
|
)
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
to_save = audio_output.save_flagged(
|
|
tmpdirname, "audio_output", deepcopy(media_data.BASE64_AUDIO), None
|
|
)
|
|
self.assertEqual("audio_output/0.wav", to_save)
|
|
to_save = audio_output.save_flagged(
|
|
tmpdirname, "audio_output", deepcopy(media_data.BASE64_AUDIO), None
|
|
)
|
|
self.assertEqual("audio_output/1.wav", to_save)
|
|
|
|
def test_tokenize(self):
|
|
"""
|
|
Tokenize, get_masked_inputs
|
|
"""
|
|
x_wav = deepcopy(media_data.BASE64_AUDIO)
|
|
audio_input = gr.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)
|
|
|
|
# TODO: add test_in_interface_as_input
|
|
def test_in_interface_as_output(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
|
|
def generate_noise(duration):
|
|
return 48000, np.random.randint(-256, 256, (duration, 3)).astype(np.int16)
|
|
|
|
iface = gr.Interface(generate_noise, "slider", "audio")
|
|
self.assertTrue(iface.process([100])[0][0].startswith("data:audio/wav;base64"))
|
|
|
|
|
|
class TestFile(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Preprocess, serialize, save_flagged, restore_flagged, generate_sample, get_template_context, default_value
|
|
"""
|
|
x_file = deepcopy(media_data.BASE64_FILE)
|
|
file_input = gr.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.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,
|
|
},
|
|
)
|
|
self.assertIsNone(file_input.preprocess(None))
|
|
x_file["is_example"] = True
|
|
self.assertIsNotNone(file_input.preprocess(x_file))
|
|
|
|
file_input = gr.File("test/test_files/sample_file.pdf")
|
|
self.assertEqual(
|
|
file_input.get_template_context(),
|
|
deepcopy(media_data.FILE_TEMPLATE_CONTEXT),
|
|
)
|
|
|
|
def test_in_interface_as_input(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
x_file = deepcopy(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]])[0], [10558])
|
|
|
|
def test_as_component_as_output(self):
|
|
"""
|
|
Interface, process, save_flagged,
|
|
"""
|
|
|
|
def write_file(content):
|
|
with open("test.txt", "w") as f:
|
|
f.write(content)
|
|
return "test.txt"
|
|
|
|
iface = gr.Interface(write_file, "text", "file")
|
|
self.assertDictEqual(
|
|
iface.process(["hello world"])[0][0],
|
|
{
|
|
"name": "test.txt",
|
|
"size": 11,
|
|
"data": "data:text/plain;base64,aGVsbG8gd29ybGQ=",
|
|
},
|
|
)
|
|
file_output = gr.File()
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
to_save = file_output.save_flagged(
|
|
tmpdirname, "file_output", [deepcopy(media_data.BASE64_FILE)], None
|
|
)
|
|
self.assertEqual("file_output/0", to_save)
|
|
to_save = file_output.save_flagged(
|
|
tmpdirname, "file_output", [deepcopy(media_data.BASE64_FILE)], None
|
|
)
|
|
self.assertEqual("file_output/1", to_save)
|
|
|
|
|
|
class TestDataframe(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Preprocess, serialize, save_flagged, restore_flagged, generate_sample, get_template_context
|
|
"""
|
|
x_data = [["Tim", 12, False], ["Jan", 24, True]]
|
|
dataframe_input = gr.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.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": [
|
|
[None, None, None],
|
|
[None, None, None],
|
|
[None, None, None],
|
|
],
|
|
"name": "dataframe",
|
|
"label": "Dataframe Input",
|
|
"max_rows": 20,
|
|
"max_cols": None,
|
|
"overflow_row_behaviour": "paginate",
|
|
"css": {},
|
|
},
|
|
)
|
|
dataframe_input = gr.Dataframe()
|
|
output = dataframe_input.preprocess(x_data)
|
|
self.assertEqual(output[1][1], 24)
|
|
with self.assertRaises(ValueError):
|
|
wrong_type = gr.Dataframe(type="unknown")
|
|
wrong_type.preprocess(x_data)
|
|
|
|
# Output functionalities
|
|
dataframe_output = gr.Dataframe()
|
|
output = dataframe_output.postprocess(np.zeros((2, 2)))
|
|
self.assertDictEqual(output, {"data": [[0, 0], [0, 0]]})
|
|
output = dataframe_output.postprocess([[1, 3, 5]])
|
|
self.assertDictEqual(output, {"data": [[1, 3, 5]]})
|
|
output = dataframe_output.postprocess(
|
|
pd.DataFrame([[2, True], [3, True], [4, False]], columns=["num", "prime"])
|
|
)
|
|
self.assertDictEqual(
|
|
output,
|
|
{"headers": ["num", "prime"], "data": [[2, True], [3, True], [4, False]]},
|
|
)
|
|
self.assertEqual(
|
|
dataframe_output.get_template_context(),
|
|
{
|
|
"headers": None,
|
|
"max_rows": 20,
|
|
"max_cols": None,
|
|
"overflow_row_behaviour": "paginate",
|
|
"name": "dataframe",
|
|
"label": None,
|
|
"css": {},
|
|
"datatype": "str",
|
|
"row_count": 3,
|
|
"col_count": 3,
|
|
"col_width": None,
|
|
"default_value": [
|
|
[None, None, None],
|
|
[None, None, None],
|
|
[None, None, None],
|
|
],
|
|
},
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
wrong_type = gr.Dataframe(type="unknown")
|
|
wrong_type.postprocess(0)
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
to_save = dataframe_output.save_flagged(
|
|
tmpdirname, "dataframe_output", output, None
|
|
)
|
|
self.assertEqual(
|
|
to_save,
|
|
json.dumps(
|
|
{
|
|
"headers": ["num", "prime"],
|
|
"data": [[2, True], [3, True], [4, False]],
|
|
}
|
|
),
|
|
)
|
|
self.assertEqual(
|
|
dataframe_output.restore_flagged(tmpdirname, to_save, None),
|
|
{
|
|
"headers": ["num", "prime"],
|
|
"data": [[2, True], [3, True], [4, False]],
|
|
},
|
|
)
|
|
|
|
def test_in_interface_as_input(self):
|
|
"""
|
|
Interface, process,
|
|
"""
|
|
x_data = [[1, 2, 3], [4, 5, 6]]
|
|
iface = gr.Interface(np.max, "numpy", "number")
|
|
self.assertEqual(iface.process([x_data])[0], [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])[0], ["Sal"])
|
|
|
|
def test_in_interface_as_output(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
|
|
def check_odd(array):
|
|
return array % 2 == 0
|
|
|
|
iface = gr.Interface(check_odd, "numpy", "numpy")
|
|
self.assertEqual(
|
|
iface.process([[2, 3, 4]])[0][0], {"data": [[True, False, True]]}
|
|
)
|
|
|
|
|
|
class TestVideo(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Preprocess, serialize, deserialize, save_flagged, restore_flagged, generate_sample, get_template_context
|
|
"""
|
|
x_video = deepcopy(media_data.BASE64_VIDEO)
|
|
video_input = gr.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.Video(label="Upload Your Video")
|
|
self.assertEqual(
|
|
video_input.get_template_context(),
|
|
{
|
|
"source": "upload",
|
|
"name": "video",
|
|
"label": "Upload Your Video",
|
|
"css": {},
|
|
"default_value": None,
|
|
},
|
|
)
|
|
self.assertIsNone(video_input.preprocess(None))
|
|
x_video["is_example"] = True
|
|
self.assertIsNotNone(video_input.preprocess(x_video))
|
|
video_input = gr.Video(type="avi")
|
|
self.assertEqual(video_input.preprocess(x_video)[-3:], "avi")
|
|
with self.assertRaises(NotImplementedError):
|
|
video_input.serialize(x_video, True)
|
|
|
|
# Output functionalities
|
|
y_vid_path = "test/test_files/video_sample.mp4"
|
|
video_output = gr.Video()
|
|
self.assertTrue(
|
|
video_output.postprocess(y_vid_path)["data"].startswith(
|
|
"data:video/mp4;base64,"
|
|
)
|
|
)
|
|
self.assertTrue(
|
|
video_output.deserialize(
|
|
deepcopy(media_data.BASE64_VIDEO)["data"]
|
|
).endswith(".mp4")
|
|
)
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
to_save = video_output.save_flagged(
|
|
tmpdirname, "video_output", deepcopy(media_data.BASE64_VIDEO), None
|
|
)
|
|
self.assertEqual("video_output/0.mp4", to_save)
|
|
to_save = video_output.save_flagged(
|
|
tmpdirname, "video_output", deepcopy(media_data.BASE64_VIDEO), None
|
|
)
|
|
self.assertEqual("video_output/1.mp4", to_save)
|
|
|
|
def test_in_interface_as_input(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
x_video = deepcopy(media_data.BASE64_VIDEO)
|
|
iface = gr.Interface(lambda x: x, "video", "playable_video")
|
|
self.assertEqual(iface.process([x_video])[0][0]["data"], x_video["data"])
|
|
|
|
# TODO: test_in_interface_as_output
|
|
|
|
|
|
class TestTimeseries(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Preprocess, postprocess, save_flagged, restore_flagged, generate_sample, get_template_context,
|
|
"""
|
|
timeseries_input = gr.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, pd.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.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,
|
|
},
|
|
)
|
|
self.assertIsNone(timeseries_input.preprocess(None))
|
|
x_timeseries["range"] = (0, 1)
|
|
self.assertIsNotNone(timeseries_input.preprocess(x_timeseries))
|
|
|
|
# Output functionalities
|
|
|
|
timeseries_output = gr.Timeseries(label="Disease")
|
|
|
|
self.assertEqual(
|
|
timeseries_output.get_template_context(),
|
|
{
|
|
"x": None,
|
|
"y": None,
|
|
"name": "timeseries",
|
|
"label": "Disease",
|
|
"css": {},
|
|
"default_value": None,
|
|
},
|
|
)
|
|
data = {"Name": ["Tom", "nick", "krish", "jack"], "Age": [20, 21, 19, 18]}
|
|
df = pd.DataFrame(data)
|
|
self.assertEqual(
|
|
timeseries_output.postprocess(df),
|
|
{
|
|
"headers": ["Name", "Age"],
|
|
"data": [["Tom", 20], ["nick", 21], ["krish", 19], ["jack", 18]],
|
|
},
|
|
)
|
|
|
|
timeseries_output = gr.Timeseries(y="Age", label="Disease")
|
|
output = timeseries_output.postprocess(df)
|
|
self.assertEqual(
|
|
output,
|
|
{
|
|
"headers": ["Name", "Age"],
|
|
"data": [["Tom", 20], ["nick", 21], ["krish", 19], ["jack", 18]],
|
|
},
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
to_save = timeseries_output.save_flagged(
|
|
tmpdirname, "timeseries_output", output, None
|
|
)
|
|
self.assertEqual(
|
|
to_save,
|
|
'{"headers": ["Name", "Age"], "data": [["Tom", 20], ["nick", 21], ["krish", 19], '
|
|
'["jack", 18]]}',
|
|
)
|
|
self.assertEqual(
|
|
timeseries_output.restore_flagged(tmpdirname, to_save, None),
|
|
{
|
|
"headers": ["Name", "Age"],
|
|
"data": [["Tom", 20], ["nick", 21], ["krish", 19], ["jack", 18]],
|
|
},
|
|
)
|
|
|
|
# TODO: test_in_interface_as_input
|
|
|
|
def test_in_interface_as_output(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
timeseries_input = gr.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])[0],
|
|
[
|
|
{
|
|
"headers": ["time", "retail", "food", "other"],
|
|
"data": [[1, 2, 2, 2], [1, 2, 2, 2], [1, 2, 2, 2], [1, 2, 2, 2]],
|
|
}
|
|
],
|
|
)
|
|
|
|
|
|
class TestNames(unittest.TestCase):
|
|
# This test 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))
|
|
|
|
|
|
class TestLabel(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Process, postprocess, deserialize, save_flagged, restore_flagged
|
|
"""
|
|
y = "happy"
|
|
label_output = gr.Label()
|
|
label = label_output.postprocess(y)
|
|
self.assertDictEqual(label, {"label": "happy"})
|
|
self.assertEqual(label_output.deserialize(y), y)
|
|
self.assertEqual(label_output.deserialize(label), y)
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
to_save = label_output.save_flagged(tmpdir, "label_output", label, None)
|
|
self.assertEqual(to_save, y)
|
|
y = {3: 0.7, 1: 0.2, 0: 0.1}
|
|
label_output = gr.Label()
|
|
label = label_output.postprocess(y)
|
|
self.assertDictEqual(
|
|
label,
|
|
{
|
|
"label": 3,
|
|
"confidences": [
|
|
{"label": 3, "confidence": 0.7},
|
|
{"label": 1, "confidence": 0.2},
|
|
{"label": 0, "confidence": 0.1},
|
|
],
|
|
},
|
|
)
|
|
label_output = gr.Label(num_top_classes=2)
|
|
label = label_output.postprocess(y)
|
|
self.assertDictEqual(
|
|
label,
|
|
{
|
|
"label": 3,
|
|
"confidences": [
|
|
{"label": 3, "confidence": 0.7},
|
|
{"label": 1, "confidence": 0.2},
|
|
],
|
|
},
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
label_output.postprocess([1, 2, 3])
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
to_save = label_output.save_flagged(tmpdir, "label_output", label, None)
|
|
self.assertEqual(to_save, '{"3": 0.7, "1": 0.2}')
|
|
self.assertEqual(
|
|
label_output.restore_flagged(tmpdir, to_save, None),
|
|
{
|
|
"label": "3",
|
|
"confidences": [
|
|
{"label": "3", "confidence": 0.7},
|
|
{"label": "1", "confidence": 0.2},
|
|
],
|
|
},
|
|
)
|
|
|
|
self.assertEqual(
|
|
label_output.get_template_context(),
|
|
{"name": "label", "label": None, "css": {}},
|
|
)
|
|
|
|
def test_in_interface(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
x_img = deepcopy(media_data.BASE64_IMAGE)
|
|
|
|
def rgb_distribution(img):
|
|
rgb_dist = np.mean(img, axis=(0, 1))
|
|
rgb_dist /= np.sum(rgb_dist)
|
|
rgb_dist = np.round(rgb_dist, decimals=2)
|
|
return {
|
|
"red": rgb_dist[0],
|
|
"green": rgb_dist[1],
|
|
"blue": rgb_dist[2],
|
|
}
|
|
|
|
iface = gr.Interface(rgb_distribution, "image", "label")
|
|
output = iface.process([x_img])[0][0]
|
|
self.assertDictEqual(
|
|
output,
|
|
{
|
|
"label": "red",
|
|
"confidences": [
|
|
{"label": "red", "confidence": 0.44},
|
|
{"label": "green", "confidence": 0.28},
|
|
{"label": "blue", "confidence": 0.28},
|
|
],
|
|
},
|
|
)
|
|
|
|
|
|
class TestHighlightedText(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
get_template_context, save_flagged, restore_flagged
|
|
"""
|
|
ht_output = gr.HighlightedText(color_map={"pos": "green", "neg": "red"})
|
|
self.assertEqual(
|
|
ht_output.get_template_context(),
|
|
{
|
|
"color_map": {"pos": "green", "neg": "red"},
|
|
"name": "highlightedtext",
|
|
"label": None,
|
|
"show_legend": False,
|
|
"css": {},
|
|
"default_value": "",
|
|
},
|
|
)
|
|
ht = {"pos": "Hello ", "neg": "World"}
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
to_save = ht_output.save_flagged(tmpdirname, "ht_output", ht, None)
|
|
self.assertEqual(to_save, '{"pos": "Hello ", "neg": "World"}')
|
|
self.assertEqual(
|
|
ht_output.restore_flagged(tmpdirname, to_save, None),
|
|
{"pos": "Hello ", "neg": "World"},
|
|
)
|
|
|
|
def test_in_interface(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
|
|
def highlight_vowels(sentence):
|
|
phrases, cur_phrase = [], ""
|
|
vowels, mode = "aeiou", None
|
|
for letter in sentence:
|
|
letter_mode = "vowel" if letter in vowels else "non"
|
|
if mode is None:
|
|
mode = letter_mode
|
|
elif mode != letter_mode:
|
|
phrases.append((cur_phrase, mode))
|
|
cur_phrase = ""
|
|
mode = letter_mode
|
|
cur_phrase += letter
|
|
phrases.append((cur_phrase, mode))
|
|
return phrases
|
|
|
|
iface = gr.Interface(highlight_vowels, "text", "highlight")
|
|
self.assertListEqual(
|
|
iface.process(["Helloooo"])[0][0],
|
|
[("H", "non"), ("e", "vowel"), ("ll", "non"), ("oooo", "vowel")],
|
|
)
|
|
|
|
|
|
class TestJSON(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Postprocess, save_flagged, restore_flagged
|
|
"""
|
|
js_output = gr.JSON()
|
|
self.assertTrue(
|
|
js_output.postprocess('{"a":1, "b": 2}'), '"{\\"a\\":1, \\"b\\": 2}"'
|
|
)
|
|
js = {"pos": "Hello ", "neg": "World"}
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
to_save = js_output.save_flagged(tmpdirname, "js_output", js, None)
|
|
self.assertEqual(to_save, '{"pos": "Hello ", "neg": "World"}')
|
|
self.assertEqual(
|
|
js_output.restore_flagged(tmpdirname, to_save, None),
|
|
{"pos": "Hello ", "neg": "World"},
|
|
)
|
|
self.assertEqual(
|
|
js_output.get_template_context(),
|
|
{"css": {}, "default_value": '""', "label": None, "name": "json"},
|
|
)
|
|
|
|
def test_in_interface(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
|
|
def get_avg_age_per_gender(data):
|
|
return {
|
|
"M": int(data[data["gender"] == "M"].mean()),
|
|
"F": int(data[data["gender"] == "F"].mean()),
|
|
"O": int(data[data["gender"] == "O"].mean()),
|
|
}
|
|
|
|
iface = gr.Interface(
|
|
get_avg_age_per_gender,
|
|
gr.inputs.Dataframe(headers=["gender", "age"]),
|
|
"json",
|
|
)
|
|
y_data = [
|
|
["M", 30],
|
|
["F", 20],
|
|
["M", 40],
|
|
["O", 20],
|
|
["F", 30],
|
|
]
|
|
self.assertDictEqual(iface.process([y_data])[0][0], {"M": 35, "F": 25, "O": 20})
|
|
|
|
|
|
class TestHTML(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Get_template_context
|
|
"""
|
|
html_component = gr.components.HTML("#Welcome onboard", label="HTML Input")
|
|
self.assertEqual(
|
|
{
|
|
"css": {},
|
|
"default_value": "#Welcome onboard",
|
|
"label": "HTML Input",
|
|
"name": "html",
|
|
},
|
|
html_component.get_template_context(),
|
|
)
|
|
|
|
def test_in_interface(self):
|
|
"""
|
|
Interface, process
|
|
"""
|
|
|
|
def bold_text(text):
|
|
return "<strong>" + text + "</strong>"
|
|
|
|
iface = gr.Interface(bold_text, "text", "html")
|
|
self.assertEqual(iface.process(["test"])[0][0], "<strong>test</strong>")
|
|
|
|
|
|
class TestCarousel(unittest.TestCase):
|
|
def test_component_functionalities(self):
|
|
"""
|
|
Postprocess, get_template_context, 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_template_context(),
|
|
{
|
|
"components": [
|
|
{
|
|
"name": "textbox",
|
|
"label": None,
|
|
"default_value": "",
|
|
"lines": 1,
|
|
"css": {},
|
|
"placeholder": None,
|
|
}
|
|
],
|
|
"name": "carousel",
|
|
"label": "Disease",
|
|
"css": {},
|
|
},
|
|
)
|
|
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, output, None)
|
|
self.assertEqual(None, 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.inputs.Image(type="numpy"), carousel_output)
|
|
result = iface.process([deepcopy(media_data.BASE64_IMAGE)])
|
|
self.assertTrue(result[0][0][0][0] == "Red")
|
|
self.assertTrue(
|
|
result[0][0][0][1].startswith("data:image/png;base64,iVBORw0KGgoAAA")
|
|
)
|
|
self.assertTrue(result[0][0][1][0] == "Green")
|
|
self.assertTrue(
|
|
result[0][0][1][1].startswith("data:image/png;base64,iVBORw0KGgoAAA")
|
|
)
|
|
self.assertTrue(result[0][0][2][0] == "Blue")
|
|
self.assertTrue(
|
|
result[0][0][2][1].startswith("data:image/png;base64,iVBORw0KGgoAAA")
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|