mirror of
https://github.com/gradio-app/gradio.git
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795 lines
32 KiB
Python
795 lines
32 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 difflib import SequenceMatcher
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import numpy as np
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import pandas
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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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class InputComponent(unittest.TestCase):
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def test_as_component(self):
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input = gr.inputs.InputComponent(label="Test Input")
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self.assertEqual(input.preprocess("Hello World!"), "Hello World!")
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self.assertEqual(input.preprocess_example(["1", "2", "3"]), ["1", "2", "3"])
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self.assertEqual(input.serialize(1, True), 1)
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self.assertEqual(input.set_interpret_parameters(), input)
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self.assertIsNone(input.get_interpretation_neighbors("Hi!"))
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self.assertIsNone(input.get_interpretation_scores("Hi!", [], []))
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self.assertIsNone(input.generate_sample())
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class TestTextbox(unittest.TestCase):
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def test_as_component(self):
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text_input = gr.inputs.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.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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numeric_text_input = gr.inputs.Textbox(type="number")
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self.assertEqual(numeric_text_input.preprocess("2"), 2.0)
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with self.assertRaises(ValueError):
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wrong_type = gr.inputs.Textbox(type="unknown")
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wrong_type.preprocess(0)
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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.assertIsInstance(text_input.generate_sample(), str)
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def test_in_interface(self):
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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.inputs.Textbox(),
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gr.outputs.Textbox(),
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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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class TestNumber(unittest.TestCase):
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def test_as_component(self):
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numeric_input = gr.inputs.Number(optional=True)
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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.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": None, "optional": True, "name": "number", "label": None},
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)
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def test_in_interface(self):
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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", "textbox", 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_as_component(self):
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slider_input = gr.inputs.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.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.inputs.Slider(
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minimum=10, maximum=20, step=1, default=15, 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": 15,
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"name": "slider",
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"optional": False,
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"label": "Slide Your Input",
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},
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)
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def test_in_interface(self):
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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", "textbox", 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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class TestCheckbox(unittest.TestCase):
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def test_as_component(self):
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bool_input = gr.inputs.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.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.inputs.Checkbox(default=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": True,
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"name": "checkbox",
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"optional": False,
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"label": "Check Your Input",
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},
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)
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def test_in_interface(self):
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iface = gr.Interface(lambda x: 1 if x else 0, "checkbox", "textbox")
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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", "textbox", 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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class TestCheckboxGroup(unittest.TestCase):
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def test_as_component(self):
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checkboxes_input = gr.inputs.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.inputs.CheckboxGroup(
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choices=["a", "b", "c"], default=["a", "c"], 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": ["a", "c"],
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"optional": False,
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"name": "checkboxgroup",
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"label": "Check Your Inputs",
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},
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)
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with self.assertRaises(ValueError):
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wrong_type = gr.inputs.CheckboxGroup(["a"], type="unknown")
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wrong_type.preprocess(0)
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def test_in_interface(self):
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checkboxes_input = gr.inputs.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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checkboxes_input = gr.inputs.CheckboxGroup(["a", "b", "c"], type="index")
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iface = gr.Interface(
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lambda x: "|".join(map(str, x)),
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checkboxes_input,
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"textbox",
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interpretation="default",
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)
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self.assertEqual(iface.process([["a", "c"]])[0], ["0|2"])
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scores, alternative_outputs = iface.interpret([["a", "c"]])
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self.assertEqual(scores, [[[-1, None], [None, -1], [-1, None]]])
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self.assertEqual(alternative_outputs, [[["2"], ["0|2|1"], ["0"]]])
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class TestRadio(unittest.TestCase):
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def test_as_component(self):
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radio_input = gr.inputs.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.inputs.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(
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radio_input.get_template_context(),
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{
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"choices": ["a", "b", "c"],
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"default": "a",
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"name": "radio",
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"label": "Pick Your One Input",
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"optional": False,
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},
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)
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with self.assertRaises(ValueError):
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wrong_type = gr.inputs.Radio(["a", "b"], type="unknown")
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wrong_type.preprocess(0)
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def test_in_interface(self):
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radio_input = gr.inputs.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.inputs.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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|
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class TestDropdown(unittest.TestCase):
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def test_as_component(self):
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dropdown_input = gr.inputs.Dropdown(["a", "b", "c"])
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self.assertEqual(dropdown_input.preprocess("c"), "c")
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self.assertEqual(dropdown_input.preprocess_example("a"), "a")
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self.assertEqual(dropdown_input.serialize("a", True), "a")
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = dropdown_input.save_flagged(
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tmpdirname, "dropdown_input", "a", None
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)
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self.assertEqual(to_save, "a")
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restored = dropdown_input.restore_flagged(tmpdirname, to_save, None)
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self.assertEqual(restored, "a")
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self.assertIsInstance(dropdown_input.generate_sample(), str)
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dropdown_input = gr.inputs.Dropdown(
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choices=["a", "b", "c"], default="a", label="Drop Your Input"
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)
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self.assertEqual(
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dropdown_input.get_template_context(),
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{
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"choices": ["a", "b", "c"],
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"default": "a",
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"name": "dropdown",
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"label": "Drop Your Input",
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"optional": False,
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},
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)
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with self.assertRaises(ValueError):
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wrong_type = gr.inputs.Dropdown(["a"], type="unknown")
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wrong_type.preprocess(0)
|
|
|
|
def test_in_interface(self):
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dropdown_input = gr.inputs.Dropdown(["a", "b", "c"])
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iface = gr.Interface(lambda x: 2 * x, dropdown_input, "textbox")
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self.assertEqual(iface.process(["c"])[0], ["cc"])
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dropdown = gr.inputs.Dropdown(["a", "b", "c"], type="index")
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iface = gr.Interface(
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lambda x: 2 * x, dropdown, "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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|
|
|
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class TestImage(unittest.TestCase):
|
|
def test_as_component(self):
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|
img = gr.test_data.BASE64_IMAGE
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image_input = gr.inputs.Image()
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self.assertEqual(image_input.preprocess(img).shape, (68, 61, 3))
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image_input = gr.inputs.Image(image_mode="L", shape=(25, 25))
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self.assertEqual(image_input.preprocess(img).shape, (25, 25))
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image_input = gr.inputs.Image(shape=(30, 10), type="pil")
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self.assertEqual(image_input.preprocess(img).size, (30, 10))
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self.assertEqual(image_input.preprocess_example("test/test_files/bus.png"), img)
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self.assertEqual(image_input.serialize("test/test_files/bus.png", True), img)
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = image_input.save_flagged(tmpdirname, "image_input", img, None)
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self.assertEqual("image_input/0.png", to_save)
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to_save = image_input.save_flagged(tmpdirname, "image_input", img, None)
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self.assertEqual("image_input/1.png", to_save)
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restored = image_input.restore_flagged(tmpdirname, to_save, None)
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self.assertEqual(restored, "image_input/1.png")
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|
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self.assertIsInstance(image_input.generate_sample(), str)
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image_input = gr.inputs.Image(
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source="upload", tool="editor", type="pil", label="Upload Your Image"
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)
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|
self.assertEqual(
|
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image_input.get_template_context(),
|
|
{
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"image_mode": "RGB",
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"shape": None,
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"source": "upload",
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"tool": "editor",
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"optional": False,
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"name": "image",
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"label": "Upload Your Image",
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|
},
|
|
)
|
|
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(gr.test_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 = gr.test_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][0]
|
|
self.assertEqual(
|
|
gr.processing_utils.decode_base64_to_image(output).size, (10, 30)
|
|
)
|
|
iface = gr.Interface(
|
|
lambda x: np.sum(x), image_input, "textbox", interpretation="default"
|
|
)
|
|
scores, alternative_outputs = iface.interpret([img])
|
|
self.assertEqual(scores, gr.test_data.SUM_PIXELS_INTERPRETATION["scores"])
|
|
self.assertEqual(
|
|
alternative_outputs,
|
|
gr.test_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(gr.test_data.SUM_PIXELS_SHAP_INTERPRETATION["scores"][0]),
|
|
)
|
|
self.assertEqual(
|
|
len(alternative_outputs[0]),
|
|
len(gr.test_data.SUM_PIXELS_SHAP_INTERPRETATION["alternative_outputs"][0]),
|
|
)
|
|
image_input = gr.inputs.Image(shape=(30, 10))
|
|
iface = gr.Interface(
|
|
lambda x: np.sum(x), image_input, "textbox", interpretation="default"
|
|
)
|
|
self.assertIsNotNone(iface.interpret([img]))
|
|
|
|
|
|
class TestAudio(unittest.TestCase):
|
|
def test_as_component(self):
|
|
x_wav = gr.test_data.BASE64_AUDIO
|
|
audio_input = gr.inputs.Audio()
|
|
output = audio_input.preprocess(x_wav)
|
|
self.assertEqual(output[0], 8000)
|
|
self.assertEqual(output[1].shape, (8046,))
|
|
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",
|
|
"optional": False,
|
|
"name": "audio",
|
|
"label": "Upload Your Audio",
|
|
},
|
|
)
|
|
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 = gr.test_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 = gr.test_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",
|
|
"optional": False,
|
|
"name": "file",
|
|
"label": "Upload Your File",
|
|
},
|
|
)
|
|
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 = gr.test_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])
|
|
|
|
|
|
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": [[None, None, None], [None, None, None], [None, None, None]],
|
|
"name": "dataframe",
|
|
"label": "Dataframe Input",
|
|
"optional": False,
|
|
},
|
|
)
|
|
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])[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"])
|
|
|
|
|
|
class TestVideo(unittest.TestCase):
|
|
def test_as_component(self):
|
|
x_video = gr.test_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",
|
|
"optional": False,
|
|
"name": "video",
|
|
"label": "Upload Your Video",
|
|
},
|
|
)
|
|
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 = gr.test_data.BASE64_VIDEO
|
|
iface = gr.Interface(lambda x: x, "video", "playable_video")
|
|
self.assertEqual(iface.process([x_video])[0][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"],
|
|
"optional": False,
|
|
"name": "timeseries",
|
|
"label": "Upload Your Timeseries",
|
|
},
|
|
)
|
|
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])[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 ensures that `inputs.get_input_instance()` works correctly when instantiating from components
|
|
def test_no_duplicate_uncased_names(self):
|
|
subclasses = gr.inputs.InputComponent.__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()
|