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d3371ec7d3
* changes * added workflow * fix action * fix action * fix action * fixed delete none * Delete benchmark-queue.yml * Delete benchmark_queue.py * changelog * fixing tests * format
560 lines
21 KiB
Python
560 lines
21 KiB
Python
import json
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import os
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import sys
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import textwrap
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import warnings
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from pathlib import Path
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from unittest.mock import MagicMock, patch
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import pytest
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from fastapi.testclient import TestClient
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import gradio
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import gradio as gr
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from gradio import media_data
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from gradio.context import Context
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from gradio.exceptions import InvalidApiName
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from gradio.external import (
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TooManyRequestsError,
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cols_to_rows,
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get_tabular_examples,
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use_websocket,
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)
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from gradio.external_utils import get_pred_from_ws
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"""
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WARNING: These tests have an external dependency: namely that Hugging Face's
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Hub and Space APIs do not change, and they keep their most famous models up.
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So if, e.g. Spaces is down, then these test will not pass.
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These tests actually test gr.Interface.load() and gr.Blocks.load() but are
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included in a separate file because of the above-mentioned dependency.
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"""
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os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
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# Mark the whole module as flaky
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pytestmark = pytest.mark.flaky
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class TestLoadInterface:
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def test_audio_to_audio(self):
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model_type = "audio-to-audio"
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interface = gr.Interface.load(
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name="speechbrain/mtl-mimic-voicebank",
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src="models",
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alias=model_type,
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Audio)
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assert isinstance(interface.output_components[0], gr.Audio)
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def test_question_answering(self):
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model_type = "image-classification"
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interface = gr.Blocks.load(
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name="lysandre/tiny-vit-random",
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src="models",
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alias=model_type,
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Image)
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assert isinstance(interface.output_components[0], gr.Label)
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def test_text_generation(self):
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model_type = "text_generation"
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interface = gr.Interface.load(
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"models/gpt2", alias=model_type, description="This is a test description"
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Textbox)
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assert any(
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"This is a test description" in d["props"].get("value", "")
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for d in interface.get_config_file()["components"]
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)
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def test_summarization(self):
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model_type = "summarization"
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interface = gr.Interface.load(
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"models/facebook/bart-large-cnn", api_key=None, alias=model_type
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Textbox)
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def test_translation(self):
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model_type = "translation"
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interface = gr.Interface.load(
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"models/facebook/bart-large-cnn", api_key=None, alias=model_type
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Textbox)
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def test_text2text_generation(self):
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model_type = "text2text-generation"
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interface = gr.Interface.load(
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"models/sshleifer/tiny-mbart", api_key=None, alias=model_type
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Textbox)
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def test_text_classification(self):
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model_type = "text-classification"
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interface = gr.Interface.load(
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"models/distilbert-base-uncased-finetuned-sst-2-english",
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api_key=None,
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alias=model_type,
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Label)
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def test_fill_mask(self):
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model_type = "fill-mask"
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interface = gr.Interface.load(
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"models/bert-base-uncased", api_key=None, alias=model_type
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Label)
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def test_zero_shot_classification(self):
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model_type = "zero-shot-classification"
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interface = gr.Interface.load(
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"models/facebook/bart-large-mnli", api_key=None, alias=model_type
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.input_components[1], gr.Textbox)
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assert isinstance(interface.input_components[2], gr.Checkbox)
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assert isinstance(interface.output_components[0], gr.Label)
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def test_automatic_speech_recognition(self):
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model_type = "automatic-speech-recognition"
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interface = gr.Interface.load(
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"models/facebook/wav2vec2-base-960h", api_key=None, alias=model_type
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Audio)
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assert isinstance(interface.output_components[0], gr.Textbox)
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def test_image_classification(self):
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model_type = "image-classification"
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interface = gr.Interface.load(
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"models/google/vit-base-patch16-224", api_key=None, alias=model_type
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Image)
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assert isinstance(interface.output_components[0], gr.Label)
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def test_feature_extraction(self):
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model_type = "feature-extraction"
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interface = gr.Interface.load(
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"models/sentence-transformers/distilbert-base-nli-mean-tokens",
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api_key=None,
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alias=model_type,
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Dataframe)
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def test_sentence_similarity(self):
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model_type = "text-to-speech"
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interface = gr.Interface.load(
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"models/julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train",
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api_key=None,
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alias=model_type,
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Audio)
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def test_text_to_speech(self):
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model_type = "text-to-speech"
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interface = gr.Interface.load(
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"models/julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train",
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api_key=None,
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alias=model_type,
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Audio)
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def test_text_to_image(self):
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model_type = "text-to-image"
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interface = gr.Interface.load(
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"models/osanseviero/BigGAN-deep-128", api_key=None, alias=model_type
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)
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assert interface.__name__ == model_type
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assert isinstance(interface.input_components[0], gr.Textbox)
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assert isinstance(interface.output_components[0], gr.Image)
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def test_english_to_spanish(self):
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with pytest.warns(UserWarning):
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io = gr.Interface.load("spaces/abidlabs/english_to_spanish", title="hi")
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assert isinstance(io.input_components[0], gr.Textbox)
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assert isinstance(io.output_components[0], gr.Textbox)
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def test_sentiment_model(self):
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io = gr.Interface.load("models/distilbert-base-uncased-finetuned-sst-2-english")
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try:
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output = io("I am happy, I love you")
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assert json.load(open(output))["label"] == "POSITIVE"
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except TooManyRequestsError:
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pass
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def test_image_classification_model(self):
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io = gr.Blocks.load(name="models/google/vit-base-patch16-224")
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try:
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output = io("gradio/test_data/lion.jpg")
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assert json.load(open(output))["label"] == "lion"
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except TooManyRequestsError:
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pass
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def test_translation_model(self):
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io = gr.Blocks.load(name="models/t5-base")
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try:
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output = io("My name is Sarah and I live in London")
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assert output == "Mein Name ist Sarah und ich lebe in London"
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except TooManyRequestsError:
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pass
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def test_numerical_to_label_space(self):
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io = gr.Interface.load("spaces/abidlabs/titanic-survival")
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try:
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output = io("male", 77, 10)
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assert json.load(open(output))["label"] == "Perishes"
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except TooManyRequestsError:
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pass
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def test_image_to_text(self):
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io = gr.Interface.load("models/nlpconnect/vit-gpt2-image-captioning")
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try:
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output = io("gradio/test_data/lion.jpg")
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assert isinstance(output, str)
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except TooManyRequestsError:
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pass
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def test_conversational(self):
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io = gr.Interface.load("models/microsoft/DialoGPT-medium")
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app, _, _ = io.launch(prevent_thread_lock=True)
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client = TestClient(app)
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assert app.state_holder == {}
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response = client.post(
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"/api/predict/",
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json={"session_hash": "foo", "data": ["Hi!", None], "fn_index": 0},
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)
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output = response.json()
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assert isinstance(output["data"], list)
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assert isinstance(output["data"][0], list)
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assert isinstance(app.state_holder["foo"], dict)
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def test_speech_recognition_model(self):
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io = gr.Interface.load("models/facebook/wav2vec2-base-960h")
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try:
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output = io("gradio/test_data/test_audio.wav")
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assert output is not None
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except TooManyRequestsError:
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pass
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app, _, _ = io.launch(prevent_thread_lock=True, show_error=True)
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client = TestClient(app)
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resp = client.post(
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"api/predict",
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json={"fn_index": 0, "data": [media_data.BASE64_AUDIO], "name": "sample"},
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)
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try:
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if resp.status_code != 200:
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warnings.warn("Request for speech recognition model failed!")
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if (
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"Could not complete request to HuggingFace API"
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in resp.json()["error"]
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):
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pass
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else:
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assert False
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else:
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assert resp.json()["data"] is not None
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finally:
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io.close()
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def test_text_to_image_model(self):
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io = gr.Interface.load("models/osanseviero/BigGAN-deep-128")
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try:
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filename = io("chest")
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assert filename.endswith(".jpg") or filename.endswith(".jpeg")
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except TooManyRequestsError:
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pass
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def test_private_space(self):
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api_key = "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" # Intentionally revealing this key for testing purposes
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io = gr.Interface.load(
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"spaces/gradio-tests/not-actually-private-space", api_key=api_key
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)
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try:
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output = io("abc")
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assert output == "abc"
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except TooManyRequestsError:
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pass
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def test_private_space_audio(self):
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api_key = "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" # Intentionally revealing this key for testing purposes
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io = gr.Interface.load(
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"spaces/gradio-tests/not-actually-private-space-audio", api_key=api_key
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)
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try:
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output = io(media_data.BASE64_AUDIO["name"])
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assert output.endswith(".wav")
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except TooManyRequestsError:
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pass
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def test_multiple_spaces_one_private(self):
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api_key = "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" # Intentionally revealing this key for testing purposes
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with gr.Blocks():
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gr.Interface.load(
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"spaces/gradio-tests/not-actually-private-space", api_key=api_key
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)
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gr.Interface.load(
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"spaces/gradio/test-loading-examples",
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)
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assert Context.access_token == api_key
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def test_loading_files_via_proxy_works(self):
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api_key = "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" # Intentionally revealing this key for testing purposes
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io = gr.Interface.load(
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"spaces/gradio-tests/test-loading-examples-private", api_key=api_key
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)
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app, _, _ = io.launch(prevent_thread_lock=True)
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test_client = TestClient(app)
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r = test_client.get(
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"/proxy=https://gradio-tests-test-loading-examples-private.hf.space/file=/tmp/tmpdd31pyri/Bunnyp0c5zw__.obj"
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)
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assert r.status_code == 200
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class TestLoadInterfaceWithExamples:
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def test_interface_load_examples(self, tmp_path):
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test_file_dir = Path(Path(__file__).parent, "test_files")
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with patch("gradio.helpers.CACHED_FOLDER", tmp_path):
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gr.Interface.load(
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name="models/google/vit-base-patch16-224",
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examples=[Path(test_file_dir, "cheetah1.jpg")],
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cache_examples=False,
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)
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def test_interface_load_cache_examples(self, tmp_path):
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test_file_dir = Path(Path(__file__).parent, "test_files")
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with patch("gradio.helpers.CACHED_FOLDER", tmp_path):
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gr.Interface.load(
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name="models/google/vit-base-patch16-224",
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examples=[Path(test_file_dir, "cheetah1.jpg")],
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cache_examples=True,
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)
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def test_root_url(self):
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demo = gr.Interface.load("spaces/gradio/test-loading-examples")
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assert all(
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[
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c["props"]["root_url"]
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== "https://gradio-test-loading-examples.hf.space/"
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for c in demo.get_config_file()["components"]
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]
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)
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def test_root_url_deserialization(self):
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demo = gr.Interface.load("spaces/gradio/simple_gallery")
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path_to_files = demo("test")
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assert (Path(path_to_files) / "captions.json").exists()
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def test_interface_with_examples(self):
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# This demo has the "fake_event" correctly removed
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demo = gr.Interface.load("spaces/freddyaboulton/calculator")
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assert demo(2, "add", 3) == 5
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# This demo still has the "fake_event". both should work
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demo = gr.Interface.load("spaces/abidlabs/test-calculator-2")
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assert demo(2, "add", 4) == 6
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def test_get_tabular_examples_replaces_nan_with_str_nan():
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readme = """
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---
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tags:
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- sklearn
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- skops
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- tabular-classification
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widget:
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structuredData:
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attribute_0:
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- material_7
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- material_7
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- material_7
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measurement_2:
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- 14.206
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- 15.094
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- .nan
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---
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"""
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mock_response = MagicMock()
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mock_response.status_code = 200
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mock_response.text = textwrap.dedent(readme)
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with patch("gradio.external.requests.get", return_value=mock_response):
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examples = get_tabular_examples("foo-model")
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assert examples["measurement_2"] == [14.206, 15.094, "NaN"]
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def test_cols_to_rows():
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assert cols_to_rows({"a": [1, 2, "NaN"], "b": [1, "NaN", 3]}) == (
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["a", "b"],
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[[1, 1], [2, "NaN"], ["NaN", 3]],
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)
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assert cols_to_rows({"a": [1, 2, "NaN", 4], "b": [1, "NaN", 3]}) == (
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["a", "b"],
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[[1, 1], [2, "NaN"], ["NaN", 3], [4, "NaN"]],
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)
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assert cols_to_rows({"a": [1, 2, "NaN"], "b": [1, "NaN", 3, 5]}) == (
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["a", "b"],
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[[1, 1], [2, "NaN"], ["NaN", 3], ["NaN", 5]],
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)
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assert cols_to_rows({"a": None, "b": [1, "NaN", 3, 5]}) == (
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["a", "b"],
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[["NaN", 1], ["NaN", "NaN"], ["NaN", 3], ["NaN", 5]],
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)
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assert cols_to_rows({"a": None, "b": None}) == (["a", "b"], [])
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def check_dataframe(config):
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input_df = next(
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c for c in config["components"] if c["props"].get("label", "") == "Input Rows"
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)
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assert input_df["props"]["headers"] == ["a", "b"]
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assert input_df["props"]["row_count"] == (1, "dynamic")
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assert input_df["props"]["col_count"] == (2, "fixed")
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def check_dataset(config, readme_examples):
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# No Examples
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if not any(readme_examples.values()):
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assert not any([c for c in config["components"] if c["type"] == "dataset"])
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else:
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dataset = next(c for c in config["components"] if c["type"] == "dataset")
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assert dataset["props"]["samples"] == [[cols_to_rows(readme_examples)[1]]]
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def test_load_blocks_with_default_values():
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io = gr.Interface.load("spaces/abidlabs/min-dalle")
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assert isinstance(io.get_config_file()["components"][0]["props"]["value"], list)
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io = gr.Interface.load("spaces/abidlabs/min-dalle-later")
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assert isinstance(io.get_config_file()["components"][0]["props"]["value"], list)
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io = gr.Interface.load("spaces/freddyaboulton/dataframe_load")
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assert io.get_config_file()["components"][0]["props"]["value"] == {
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"headers": ["a", "b"],
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"data": [[1, 4], [2, 5], [3, 6]],
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}
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@pytest.mark.parametrize(
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"hypothetical_readme",
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[
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{"a": [1, 2, "NaN"], "b": [1, "NaN", 3]},
|
|
{"a": [1, 2, "NaN", 4], "b": [1, "NaN", 3]},
|
|
{"a": [1, 2, "NaN"], "b": [1, "NaN", 3, 5]},
|
|
{"a": None, "b": [1, "NaN", 3, 5]},
|
|
{"a": None, "b": None},
|
|
],
|
|
)
|
|
def test_can_load_tabular_model_with_different_widget_data(hypothetical_readme):
|
|
with patch(
|
|
"gradio.external.get_tabular_examples", return_value=hypothetical_readme
|
|
):
|
|
io = gr.Interface.load("models/scikit-learn/tabular-playground")
|
|
check_dataframe(io.config)
|
|
check_dataset(io.config, hypothetical_readme)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"config, dependency, answer",
|
|
[
|
|
({"version": "3.3", "enable_queue": True}, {"queue": True}, True),
|
|
({"version": "3.3", "enable_queue": False}, {"queue": None}, False),
|
|
({"version": "3.3", "enable_queue": True}, {"queue": None}, True),
|
|
({"version": "3.3", "enable_queue": True}, {"queue": False}, False),
|
|
({"enable_queue": True}, {"queue": False}, False),
|
|
({"version": "3.2", "enable_queue": False}, {"queue": None}, False),
|
|
({"version": "3.2", "enable_queue": True}, {"queue": None}, True),
|
|
({"version": "3.2", "enable_queue": True}, {"queue": False}, False),
|
|
({"version": "3.1.3", "enable_queue": True}, {"queue": None}, False),
|
|
({"version": "3.1.3", "enable_queue": False}, {"queue": True}, False),
|
|
],
|
|
)
|
|
def test_use_websocket_after_315(config, dependency, answer):
|
|
assert use_websocket(config, dependency) == answer
|
|
|
|
|
|
class AsyncMock(MagicMock):
|
|
async def __call__(self, *args, **kwargs):
|
|
return super(AsyncMock, self).__call__(*args, **kwargs)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_get_pred_from_ws():
|
|
mock_ws = AsyncMock(name="ws")
|
|
messages = [
|
|
json.dumps({"msg": "estimation"}),
|
|
json.dumps({"msg": "send_data"}),
|
|
json.dumps({"msg": "process_generating"}),
|
|
json.dumps({"msg": "process_completed", "output": {"data": ["result!"]}}),
|
|
]
|
|
mock_ws.recv.side_effect = messages
|
|
data = json.dumps({"data": ["foo"], "fn_index": "foo"})
|
|
hash_data = json.dumps({"session_hash": "daslskdf", "fn_index": "foo"})
|
|
output = await get_pred_from_ws(mock_ws, data, hash_data)
|
|
assert output == {"data": ["result!"]}
|
|
mock_ws.send.assert_called_once_with(data)
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_get_pred_from_ws_raises_if_queue_full():
|
|
mock_ws = AsyncMock(name="ws")
|
|
messages = [json.dumps({"msg": "queue_full"})]
|
|
mock_ws.recv.side_effect = messages
|
|
data = json.dumps({"data": ["foo"], "fn_index": "foo"})
|
|
hash_data = json.dumps({"session_hash": "daslskdf", "fn_index": "foo"})
|
|
with pytest.raises(gradio.Error, match="Queue is full!"):
|
|
await get_pred_from_ws(mock_ws, data, hash_data)
|
|
|
|
|
|
@pytest.mark.skipif(
|
|
sys.version_info < (3, 8),
|
|
reason="Mocks of async context manager don't work for 3.7",
|
|
)
|
|
def test_respect_queue_when_load_from_config():
|
|
with patch("websockets.connect"):
|
|
with patch(
|
|
"gradio.external_utils.get_pred_from_ws", return_value={"data": ["foo"]}
|
|
):
|
|
interface = gr.Interface.load("spaces/freddyaboulton/saymyname")
|
|
assert interface("bob") == "foo"
|
|
|
|
|
|
def test_raise_value_error_when_api_name_invalid():
|
|
with pytest.raises(InvalidApiName):
|
|
demo = gr.Blocks.load(name="spaces/gradio/hello_world")
|
|
demo("freddy", api_name="route does not exist")
|
|
|
|
|
|
def test_use_api_name_in_call_method():
|
|
# Interface
|
|
demo = gr.Blocks.load(name="spaces/gradio/hello_world")
|
|
assert demo("freddy", api_name="predict") == "Hello freddy!"
|
|
|
|
# Blocks demo with multiple functions
|
|
app = gr.Blocks.load(name="spaces/gradio/multiple-api-name-test")
|
|
assert app(15, api_name="minus_one") == 14
|
|
assert app(4, api_name="double") == 8
|