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555 lines
26 KiB
Python
555 lines
26 KiB
Python
import os
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import tempfile
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import unittest
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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 gradio as gr
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os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
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class OutputComponent(unittest.TestCase):
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def test_as_component(self):
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output = gr.outputs.OutputComponent(label="Test Input")
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self.assertEqual(output.postprocess("Hello World!"), "Hello World!")
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self.assertEqual(output.deserialize(1), 1)
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class TestTextbox(unittest.TestCase):
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def test_as_component(self):
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with self.assertRaises(ValueError):
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wrong_type = gr.outputs.Textbox(type="unknown")
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wrong_type.postprocess(0)
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def test_in_interface(self):
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iface = gr.Interface(lambda x: x[-1], "textbox", gr.outputs.Textbox())
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self.assertEqual(iface.process(["Hello"])[0], ["o"])
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iface = gr.Interface(
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lambda x: x / 2, "number", gr.outputs.Textbox(type="number")
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)
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self.assertEqual(iface.process([10])[0], [5])
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class TestLabel(unittest.TestCase):
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def test_as_component(self):
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y = "happy"
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label_output = gr.outputs.Label()
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label = label_output.postprocess(y)
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self.assertDictEqual(label, {"label": "happy"})
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self.assertEqual(label_output.deserialize(y), y)
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self.assertEqual(label_output.deserialize(label), y)
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with tempfile.TemporaryDirectory() as tmpdir:
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to_save = label_output.save_flagged(tmpdir, "label_output", label, None)
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self.assertEqual(to_save, y)
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y = {3: 0.7, 1: 0.2, 0: 0.1}
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label_output = gr.outputs.Label()
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label = label_output.postprocess(y)
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self.assertDictEqual(
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label,
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{
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"label": 3,
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"confidences": [
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{"label": 3, "confidence": 0.7},
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{"label": 1, "confidence": 0.2},
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{"label": 0, "confidence": 0.1},
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],
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},
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)
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label_output = gr.outputs.Label(num_top_classes=2)
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label = label_output.postprocess(y)
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self.assertDictEqual(
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label,
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{
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"label": 3,
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"confidences": [
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{"label": 3, "confidence": 0.7},
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{"label": 1, "confidence": 0.2},
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],
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},
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)
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with self.assertRaises(ValueError):
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label_output.postprocess([1, 2, 3])
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with tempfile.TemporaryDirectory() as tmpdir:
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to_save = label_output.save_flagged(tmpdir, "label_output", label, None)
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self.assertEqual(to_save, '{"3": 0.7, "1": 0.2}')
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self.assertEqual(
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label_output.restore_flagged(tmpdir, to_save, None),
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{
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"label": "3",
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"confidences": [
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{"label": "3", "confidence": 0.7},
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{"label": "1", "confidence": 0.2},
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],
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},
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)
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with self.assertRaises(ValueError):
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label_output = gr.outputs.Label(type="unknown")
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label_output.deserialize([1, 2, 3])
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def test_in_interface(self):
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x_img = gr.test_data.BASE64_IMAGE
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def rgb_distribution(img):
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rgb_dist = np.mean(img, axis=(0, 1))
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rgb_dist /= np.sum(rgb_dist)
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rgb_dist = np.round(rgb_dist, decimals=2)
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return {
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"red": rgb_dist[0],
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"green": rgb_dist[1],
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"blue": rgb_dist[2],
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}
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iface = gr.Interface(rgb_distribution, "image", "label")
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output = iface.process([x_img])[0][0]
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self.assertDictEqual(
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output,
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{
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"label": "red",
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"confidences": [
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{"label": "red", "confidence": 0.44},
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{"label": "green", "confidence": 0.28},
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{"label": "blue", "confidence": 0.28},
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],
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},
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)
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class TestImage(unittest.TestCase):
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def test_as_component(self):
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y_img = gr.processing_utils.decode_base64_to_image(gr.test_data.BASE64_IMAGE)
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image_output = gr.outputs.Image()
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self.assertTrue(
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image_output.postprocess(y_img).startswith(
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"data:image/png;base64,iVBORw0KGgoAAA"
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)
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)
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self.assertTrue(
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image_output.postprocess(np.array(y_img)).startswith(
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"data:image/png;base64,iVBORw0KGgoAAA"
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)
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)
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with self.assertWarns(DeprecationWarning):
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plot_output = gr.outputs.Image(plot=True)
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xpoints = np.array([0, 6])
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ypoints = np.array([0, 250])
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fig = plt.figure()
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plt.plot(xpoints, ypoints)
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self.assertTrue(
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plot_output.postprocess(fig).startswith("data:image/png;base64,")
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)
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with self.assertRaises(ValueError):
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image_output.postprocess([1, 2, 3])
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image_output = gr.outputs.Image(type="numpy")
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self.assertTrue(
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image_output.postprocess(y_img).startswith("data:image/png;base64,")
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)
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = image_output.save_flagged(
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tmpdirname, "image_output", gr.test_data.BASE64_IMAGE, None
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)
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self.assertEqual("image_output/0.png", to_save)
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to_save = image_output.save_flagged(
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tmpdirname, "image_output", gr.test_data.BASE64_IMAGE, None
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)
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self.assertEqual("image_output/1.png", to_save)
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def test_in_interface(self):
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def generate_noise(width, height):
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return np.random.randint(0, 256, (width, height, 3))
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iface = gr.Interface(generate_noise, ["slider", "slider"], "image")
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self.assertTrue(
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iface.process([10, 20])[0][0].startswith("data:image/png;base64")
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)
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class TestVideo(unittest.TestCase):
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def test_as_component(self):
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y_vid = "test/test_files/video_sample.mp4"
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video_output = gr.outputs.Video()
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self.assertTrue(
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video_output.postprocess(y_vid)["data"].startswith("data:video/mp4;base64,")
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)
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self.assertTrue(
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video_output.deserialize(gr.test_data.BASE64_VIDEO["data"]).endswith(".mp4")
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)
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = video_output.save_flagged(
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tmpdirname, "video_output", gr.test_data.BASE64_VIDEO, None
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)
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self.assertEqual("video_output/0.mp4", to_save)
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to_save = video_output.save_flagged(
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tmpdirname, "video_output", gr.test_data.BASE64_VIDEO, None
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)
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self.assertEqual("video_output/1.mp4", to_save)
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class TestKeyValues(unittest.TestCase):
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def test_as_component(self):
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kv_output = gr.outputs.KeyValues()
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kv_dict = {"a": 1, "b": 2}
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kv_list = [("a", 1), ("b", 2)]
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self.assertEqual(kv_output.postprocess(kv_dict), kv_list)
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self.assertEqual(kv_output.postprocess(kv_list), kv_list)
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with self.assertRaises(ValueError):
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kv_output.postprocess(0)
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = kv_output.save_flagged(tmpdirname, "kv_output", kv_list, None)
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self.assertEqual(to_save, '[["a", 1], ["b", 2]]')
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self.assertEqual(
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kv_output.restore_flagged(tmpdirname, to_save, None),
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[["a", 1], ["b", 2]],
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)
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def test_in_interface(self):
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def letter_distribution(word):
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dist = {}
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for letter in word:
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dist[letter] = dist.get(letter, 0) + 1
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return dist
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iface = gr.Interface(letter_distribution, "text", "key_values")
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self.assertListEqual(
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iface.process(["alpaca"])[0][0], [("a", 3), ("l", 1), ("p", 1), ("c", 1)]
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)
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class TestHighlightedText(unittest.TestCase):
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def test_as_component(self):
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ht_output = gr.outputs.HighlightedText(color_map={"pos": "green", "neg": "red"})
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self.assertEqual(
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ht_output.get_template_context(),
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{
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"color_map": {"pos": "green", "neg": "red"},
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"name": "highlightedtext",
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"label": None,
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"show_legend": False,
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},
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)
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ht = {"pos": "Hello ", "neg": "World"}
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = ht_output.save_flagged(tmpdirname, "ht_output", ht, None)
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self.assertEqual(to_save, '{"pos": "Hello ", "neg": "World"}')
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self.assertEqual(
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ht_output.restore_flagged(tmpdirname, to_save, None),
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{"pos": "Hello ", "neg": "World"},
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)
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def test_in_interface(self):
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def highlight_vowels(sentence):
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phrases, cur_phrase = [], ""
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vowels, mode = "aeiou", None
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for letter in sentence:
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letter_mode = "vowel" if letter in vowels else "non"
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if mode is None:
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mode = letter_mode
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elif mode != letter_mode:
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phrases.append((cur_phrase, mode))
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cur_phrase = ""
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mode = letter_mode
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cur_phrase += letter
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phrases.append((cur_phrase, mode))
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return phrases
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iface = gr.Interface(highlight_vowels, "text", "highlight")
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self.assertListEqual(
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iface.process(["Helloooo"])[0][0],
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[("H", "non"), ("e", "vowel"), ("ll", "non"), ("oooo", "vowel")],
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)
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class TestAudio(unittest.TestCase):
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def test_as_component(self):
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y_audio = gr.processing_utils.decode_base64_to_file(
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gr.test_data.BASE64_AUDIO["data"]
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)
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audio_output = gr.outputs.Audio(type="file")
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self.assertTrue(
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audio_output.postprocess(y_audio.name).startswith(
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"data:audio/wav;base64,UklGRuI/AABXQVZFZm10IBAAA"
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)
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)
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self.assertEqual(
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audio_output.get_template_context(), {"name": "audio", "label": None}
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)
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with self.assertRaises(ValueError):
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wrong_type = gr.outputs.Audio(type="unknown")
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wrong_type.postprocess(y_audio.name)
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self.assertTrue(
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audio_output.deserialize(gr.test_data.BASE64_AUDIO["data"]).endswith(".wav")
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)
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = audio_output.save_flagged(
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tmpdirname, "audio_output", gr.test_data.BASE64_AUDIO["data"], None
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)
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self.assertEqual("audio_output/0.wav", to_save)
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to_save = audio_output.save_flagged(
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tmpdirname, "audio_output", gr.test_data.BASE64_AUDIO["data"], None
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)
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self.assertEqual("audio_output/1.wav", to_save)
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def test_in_interface(self):
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def generate_noise(duration):
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return 48000, np.random.randint(-256, 256, (duration, 3)).astype(np.int16)
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iface = gr.Interface(generate_noise, "slider", "audio")
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self.assertTrue(iface.process([100])[0][0].startswith("data:audio/wav;base64"))
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class TestJSON(unittest.TestCase):
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def test_as_component(self):
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js_output = gr.outputs.JSON()
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self.assertTrue(
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js_output.postprocess('{"a":1, "b": 2}'), '"{\\"a\\":1, \\"b\\": 2}"'
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)
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js = {"pos": "Hello ", "neg": "World"}
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = js_output.save_flagged(tmpdirname, "js_output", js, None)
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self.assertEqual(to_save, '{"pos": "Hello ", "neg": "World"}')
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self.assertEqual(
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js_output.restore_flagged(tmpdirname, to_save, None),
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{"pos": "Hello ", "neg": "World"},
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)
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def test_in_interface(self):
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def get_avg_age_per_gender(data):
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return {
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"M": int(data[data["gender"] == "M"].mean()),
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"F": int(data[data["gender"] == "F"].mean()),
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"O": int(data[data["gender"] == "O"].mean()),
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}
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iface = gr.Interface(
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get_avg_age_per_gender,
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gr.inputs.Dataframe(headers=["gender", "age"]),
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"json",
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)
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y_data = [
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["M", 30],
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["F", 20],
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["M", 40],
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["O", 20],
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["F", 30],
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]
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self.assertDictEqual(iface.process([y_data])[0][0], {"M": 35, "F": 25, "O": 20})
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class TestHTML(unittest.TestCase):
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def test_in_interface(self):
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def bold_text(text):
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return "<strong>" + text + "</strong>"
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iface = gr.Interface(bold_text, "text", "html")
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self.assertEqual(iface.process(["test"])[0][0], "<strong>test</strong>")
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class TestFile(unittest.TestCase):
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def test_as_component(self):
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def write_file(content):
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with open("test.txt", "w") as f:
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f.write(content)
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return "test.txt"
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iface = gr.Interface(write_file, "text", "file")
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self.assertDictEqual(
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iface.process(["hello world"])[0][0],
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{
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"name": "test.txt",
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"size": 11,
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"data": "data:text/plain;base64,aGVsbG8gd29ybGQ=",
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},
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)
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file_output = gr.outputs.File()
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = file_output.save_flagged(
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tmpdirname, "file_output", gr.test_data.BASE64_FILE, None
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)
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self.assertEqual("file_output/0", to_save)
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to_save = file_output.save_flagged(
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tmpdirname, "file_output", gr.test_data.BASE64_FILE, None
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)
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self.assertEqual("file_output/1", to_save)
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class TestDataframe(unittest.TestCase):
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def test_as_component(self):
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dataframe_output = gr.outputs.Dataframe()
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output = dataframe_output.postprocess(np.zeros((2, 2)))
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self.assertDictEqual(output, {"data": [[0, 0], [0, 0]]})
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output = dataframe_output.postprocess([[1, 3, 5]])
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self.assertDictEqual(output, {"data": [[1, 3, 5]]})
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output = dataframe_output.postprocess(
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pd.DataFrame([[2, True], [3, True], [4, False]], columns=["num", "prime"])
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)
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self.assertDictEqual(
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output,
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{"headers": ["num", "prime"], "data": [[2, True], [3, True], [4, False]]},
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)
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self.assertEqual(
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dataframe_output.get_template_context(),
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{
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"headers": None,
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"max_rows": 20,
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"max_cols": None,
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"overflow_row_behaviour": "paginate",
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"name": "dataframe",
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"label": None,
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},
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)
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with self.assertRaises(ValueError):
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wrong_type = gr.outputs.Dataframe(type="unknown")
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wrong_type.postprocess(0)
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = dataframe_output.save_flagged(
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tmpdirname, "dataframe_output", output, None
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)
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self.assertEqual(to_save, "[[2, true], [3, true], [4, false]]")
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self.assertEqual(
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dataframe_output.restore_flagged(tmpdirname, to_save, None),
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{"data": [[2, True], [3, True], [4, False]]},
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)
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def test_in_interface(self):
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def check_odd(array):
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return array % 2 == 0
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iface = gr.Interface(check_odd, "numpy", "numpy")
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self.assertEqual(
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iface.process([[2, 3, 4]])[0][0], {"data": [[True, False, True]]}
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)
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class TestCarousel(unittest.TestCase):
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def test_as_component(self):
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carousel_output = gr.outputs.Carousel(["text", "image"], label="Disease")
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output = carousel_output.postprocess(
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[
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["Hello World", "test/test_files/bus.png"],
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["Bye World", "test/test_files/bus.png"],
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]
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)
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self.assertEqual(
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output,
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[
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["Hello World", gr.test_data.BASE64_IMAGE],
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["Bye World", gr.test_data.BASE64_IMAGE],
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],
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)
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carousel_output = gr.outputs.Carousel("text", label="Disease")
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output = carousel_output.postprocess([["Hello World"], ["Bye World"]])
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self.assertEqual(output, [["Hello World"], ["Bye World"]])
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self.assertEqual(
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carousel_output.get_template_context(),
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{
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"components": [{"name": "textbox", "label": None}],
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"name": "carousel",
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"label": "Disease",
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},
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)
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output = carousel_output.postprocess(["Hello World", "Bye World"])
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self.assertEqual(output, [["Hello World"], ["Bye World"]])
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with self.assertRaises(ValueError):
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carousel_output.postprocess("Hello World!")
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with tempfile.TemporaryDirectory() as tmpdirname:
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to_save = carousel_output.save_flagged(
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tmpdirname, "carousel_output", output, None
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)
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self.assertEqual(to_save, '[["Hello World"], ["Bye World"]]')
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def test_in_interface(self):
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carousel_output = gr.outputs.Carousel(["text", "image"], label="Disease")
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def report(img):
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results = []
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for i, mode in enumerate(["Red", "Green", "Blue"]):
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color_filter = np.array([0, 0, 0])
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color_filter[i] = 1
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results.append([mode, img * color_filter])
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return results
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|
|
iface = gr.Interface(report, gr.inputs.Image(type="numpy"), carousel_output)
|
|
self.assertEqual(
|
|
iface.process([gr.test_data.BASE64_IMAGE])[0],
|
|
[
|
|
[
|
|
[
|
|
"Red",
|
|
"data:image/png;base64,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",
|
|
],
|
|
[
|
|
"Green",
|
|
"data:image/png;base64,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",
|
|
],
|
|
[
|
|
"Blue",
|
|
"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAD0AAABECAIAAAC9Laq3AAAFGklEQVR4nNXaMWwbVRzH8U8RUjMU1RIDaZeeGKADKJbaIWJozRYJWrKB6NBUKoIxS8VWUlaQCFurIpEOgMoCIgUhpIqwkA61SBUGmslZAkhUSlVEG5YyHOee7bvnu/MlgZ+s5Pm987uvn/+//7t79/bw0P9Qj23/KZa5wp16O92zneO9xkKK+AVe5slaut4m7jUWWctqqoe+du47XOPHYYeNSl8j932us1jmI9Xp6+K+zjX+qvTZKvSjc9/iah3pohz9KNwB81VWUfpq3AXNV1nD6ctyVzBfZYXoS3Ff43pV81VWNn1B7mUWa5+ry6iffij3dpivsh7RB7jv8DkrO4hVRFuc5+nHs9rus8j1nUYartu0OZPJvSvmG6oNltMGS3Pvuvky9QfL/NpXG3Ovs8DGjjOFdY92XkqIuTvM8QxHeGLnwHK1xc+s8nfeEek4WWPtP0B/m+UAcaxBX+4i/QZL/JnVtI9TnOJY/D4zD9px+mzzJXqTU30YedyxdoA+ZD7wDG8N1vZxH6fDem/lNtHH5mvntB7hJO9xNLM5zT3BElhgbpvpV2nnmO8A53gRfJV3rvS6TyMpzNDhYw4NHL/GZyxxrypxh0/zM8Y+ribQIQXWq2bqpt9gke9yMsbr7OPZgj9m2JeYYZp55rnb2xRHzhGeY2+wk7D5TvAWB7ldhDjWUG40mGM2h77NKs/n0IfNh8t5zgur+Lpmgzk6vMP+3qa/afMZbbZS9atJ5aAOJH9LQT8Ky7LrsY1i9LfzzbePC3zDCQ6WOfUG5ytzx2oMo/8hx3wn+IaTKAx9k3u8x0tJmma09e9GPn2ezpXM/Ru0OcanfQ1p7hU2y3QaqzGM/giXaaEk9Cf5Vyw93HeZrYQuRf9KqvIAl/mIozxbprebnOX9wBF9cXKFiLkR6OdTb98tn+PavMobwdRJVnzf5cII9FGqXAp6I2EttFaT58sR6UvpHhd5tdRnwvkkTV+74sk/Jr6UkzdzVSQPdukXquDl6ntwKZA0Aiqev9c5UxP9BmcL4zb5kpm+2rLzzoj033Oel4ami0RNWszTSGaAR3qYnj/L6BAf83Dg1dVPqdeJSqeYTpVnk8ISD0eZ54uP/VeVHlE0ewe0kxQa8b/K451Weuy7+prLySVrBR0Gp/kAzNFipXuWWrhjHWKipq4wzv7UWMylo7HI/U5xrQ+sAlTWGGimajbTzTuwj6OaxvktKa+ADvPd5x8x93EWei8tdl0R6LCUzNYRm3zJt3qf79zq/V12SxFTPMWl1JBHnKbBdPyV+tardlfjTKWWa6IU9xTT6WFNc2/SSnJLk4ilmi4GGzRSCTjzgNbAwLX4BczxZuLUf9WNkyWW2GKFsVQXt0ambxLxAHFo9mqMSSZzVo6aTPUR93E/4AY3khP0qTJ9g/Fk2CZZ6e0/xsokjphOLVn2q++5a+30hxNojDGeREuMlXkfHUd5FO4383lxXfRd0OOscDcJ2amstVJJlDcL9Bx6zj06fUSH0ywwy4fM5oxxN8ozQjlTgXl+jBaTOfQTHA5+sa5mkERqJnQzz3wBDb0+CdDv5Xj+F9OLsplFFoXNF1CpfTOByNnKaoro8AUtIg6kbtqjpLKiKuyvKkI/Tiu5nNhPg3WmmShlvoAq72cLuzbW71xMyg3eZnLwNrGaRtw/OJT+Ch3GknWScuYLqJb9muHIafBaTsKurhr3xw7SRyOaL6Da9yM/4Fs6tZgvoG3dt76N+gfaDbBaHMV3YgAAAABJRU5ErkJggg==",
|
|
],
|
|
]
|
|
],
|
|
)
|
|
|
|
|
|
class TestTimeseries(unittest.TestCase):
|
|
def test_as_component(self):
|
|
timeseries_output = gr.outputs.Timeseries(label="Disease")
|
|
self.assertEqual(
|
|
timeseries_output.get_template_context(),
|
|
{"x": None, "y": None, "name": "timeseries", "label": "Disease"},
|
|
)
|
|
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.outputs.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]],
|
|
},
|
|
)
|
|
|
|
|
|
class TestNames(unittest.TestCase):
|
|
def test_no_duplicate_uncased_names(
|
|
self,
|
|
): # this ensures that get_input_instance() works correctly when instantiating from components
|
|
subclasses = gr.outputs.OutputComponent.__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()
|