mirror of
https://github.com/gradio-app/gradio.git
synced 2024-12-09 02:00:44 +08:00
249 lines
10 KiB
Python
249 lines
10 KiB
Python
import os
|
|
import pathlib
|
|
import unittest
|
|
|
|
import transformers
|
|
|
|
import gradio as gr
|
|
|
|
"""
|
|
WARNING: These tests have an external dependency: namely that Hugging Face's
|
|
Hub and Space APIs do not change, and they keep their most famous models up.
|
|
So if, e.g. Spaces is down, then these test will not pass.
|
|
"""
|
|
|
|
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
|
|
|
|
|
|
class TestHuggingFaceModelAPI(unittest.TestCase):
|
|
def test_question_answering(self):
|
|
model_type = "question-answering"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"deepset/roberta-base-squad2", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"][0], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["inputs"][1], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"][0], gr.outputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"][1], gr.outputs.Label)
|
|
|
|
def test_text_generation(self):
|
|
model_type = "text_generation"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"gpt2", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Textbox)
|
|
|
|
def test_summarization(self):
|
|
model_type = "summarization"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"facebook/bart-large-cnn", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Textbox)
|
|
|
|
def test_translation(self):
|
|
model_type = "translation"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"facebook/bart-large-cnn", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Textbox)
|
|
|
|
def test_text2text_generation(self):
|
|
model_type = "text2text-generation"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"sshleifer/tiny-mbart", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Textbox)
|
|
|
|
def test_text_classification(self):
|
|
model_type = "text-classification"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"distilbert-base-uncased-finetuned-sst-2-english",
|
|
api_key=None,
|
|
alias=model_type,
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Label)
|
|
|
|
def test_fill_mask(self):
|
|
model_type = "fill-mask"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"bert-base-uncased", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Label)
|
|
|
|
def test_zero_shot_classification(self):
|
|
model_type = "zero-shot-classification"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"facebook/bart-large-mnli", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"][0], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["inputs"][1], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["inputs"][2], gr.inputs.Checkbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Label)
|
|
|
|
def test_automatic_speech_recognition(self):
|
|
model_type = "automatic-speech-recognition"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"facebook/wav2vec2-base-960h", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Audio)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Textbox)
|
|
|
|
def test_image_classification(self):
|
|
model_type = "image-classification"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"google/vit-base-patch16-224", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Image)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Label)
|
|
|
|
def test_feature_extraction(self):
|
|
model_type = "feature-extraction"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"sentence-transformers/distilbert-base-nli-mean-tokens",
|
|
api_key=None,
|
|
alias=model_type,
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Dataframe)
|
|
|
|
def test_sentence_similarity(self):
|
|
model_type = "text-to-speech"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train",
|
|
api_key=None,
|
|
alias=model_type,
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Audio)
|
|
|
|
def test_text_to_speech(self):
|
|
model_type = "text-to-speech"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train",
|
|
api_key=None,
|
|
alias=model_type,
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Audio)
|
|
|
|
def test_text_to_image(self):
|
|
model_type = "text-to-image"
|
|
interface_info = gr.external.get_huggingface_interface(
|
|
"osanseviero/BigGAN-deep-128", api_key=None, alias=model_type
|
|
)
|
|
self.assertEqual(interface_info["fn"].__name__, model_type)
|
|
self.assertIsInstance(interface_info["inputs"], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"], gr.outputs.Image)
|
|
|
|
def test_english_to_spanish(self):
|
|
interface_info = gr.external.get_spaces_interface(
|
|
"abidlabs/english_to_spanish", api_key=None, alias=None
|
|
)
|
|
self.assertIsInstance(interface_info["inputs"][0], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"][0], gr.outputs.Textbox)
|
|
|
|
|
|
class TestLoadInterface(unittest.TestCase):
|
|
def test_english_to_spanish(self):
|
|
interface_info = gr.external.load_interface(
|
|
"spaces/abidlabs/english_to_spanish"
|
|
)
|
|
self.assertIsInstance(interface_info["inputs"][0], gr.inputs.Textbox)
|
|
self.assertIsInstance(interface_info["outputs"][0], gr.outputs.Textbox)
|
|
|
|
def test_sentiment_model(self):
|
|
interface_info = gr.external.load_interface(
|
|
"models/distilbert-base-uncased-finetuned-sst-2-english",
|
|
alias="sentiment_classifier",
|
|
)
|
|
io = gr.Interface(**interface_info)
|
|
io.api_mode = True
|
|
output = io("I am happy, I love you.")
|
|
self.assertGreater(output["POSITIVE"], 0.5)
|
|
|
|
def test_image_classification_model(self):
|
|
interface_info = gr.external.load_interface(
|
|
"models/google/vit-base-patch16-224"
|
|
)
|
|
io = gr.Interface(**interface_info)
|
|
io.api_mode = True
|
|
output = io("test/test_data/lion.jpg")
|
|
self.assertGreater(output["lion"], 0.5)
|
|
|
|
def test_translation_model(self):
|
|
interface_info = gr.external.load_interface("models/t5-base")
|
|
io = gr.Interface(**interface_info)
|
|
io.api_mode = True
|
|
output = io("My name is Sarah and I live in London")
|
|
self.assertEquals(output, "Mein Name ist Sarah und ich lebe in London")
|
|
|
|
def test_numerical_to_label_space(self):
|
|
interface_info = gr.external.load_interface("spaces/abidlabs/titanic-survival")
|
|
io = gr.Interface(**interface_info)
|
|
io.api_mode = True
|
|
output = io("male", 77, 10)
|
|
self.assertLess(output["Survives"], 0.5)
|
|
|
|
def test_speech_recognition_model(self):
|
|
interface_info = gr.external.load_interface(
|
|
"models/jonatasgrosman/wav2vec2-large-xlsr-53-english"
|
|
)
|
|
io = gr.Interface(**interface_info)
|
|
io.api_mode = True
|
|
output = io("test/test_data/test_audio.wav")
|
|
self.assertIsNotNone(output)
|
|
|
|
def test_text_to_image_model(self):
|
|
interface_info = gr.external.load_interface(
|
|
"models/osanseviero/BigGAN-deep-128"
|
|
)
|
|
io = gr.Interface(**interface_info)
|
|
io.api_mode = True
|
|
filename = io("chest")
|
|
self.assertTrue(filename.endswith(".jpg") or filename.endswith(".jpeg"))
|
|
|
|
def test_image_to_image_space(self):
|
|
def assertIsFile(path):
|
|
if not pathlib.Path(path).resolve().is_file():
|
|
raise AssertionError("File does not exist: %s" % str(path))
|
|
|
|
interface_info = gr.external.load_interface("spaces/abidlabs/image-identity")
|
|
io = gr.Interface(**interface_info)
|
|
io.api_mode = True
|
|
output = io("test/test_data/lion.jpg")
|
|
assertIsFile(output)
|
|
|
|
|
|
class TestLoadFromPipeline(unittest.TestCase):
|
|
def test_question_answering(self):
|
|
p = transformers.pipeline("question-answering")
|
|
io = gr.Interface.from_pipeline(p)
|
|
output = io(
|
|
"My name is Sylvain and I work at Hugging Face in Brooklyn",
|
|
"Where do I work?",
|
|
)
|
|
self.assertIsNotNone(output)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|