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https://github.com/gradio-app/gradio.git
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Merge pull request #48 from gradio-app/abidlabs/test_launch
Abidlabs/test launch
This commit is contained in:
commit
945fee01a4
@ -3,13 +3,15 @@
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import gradio as gr
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from difflib import Differ
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def diff_texts(text1, text2):
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d = Differ()
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return [
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(token[2:], token[0]) for token in d.compare(text1, text2)
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]
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gr.Interface(
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io = gr.Interface(
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diff_texts,
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[
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gr.inputs.Textbox(lines=3, default="The quick brown fox jumped over the lazy dogs."),
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@ -20,4 +22,7 @@ gr.Interface(
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"-": "pink",
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" ": "none",
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})
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).launch()
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)
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io.test_launch()
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io.launch()
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|
@ -2,24 +2,26 @@
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import tensorflow as tf
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import gradio
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import os
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from tensorflow.keras.layers import *
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import gradio as gr
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from urllib.request import urlretrieve
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urlretrieve("https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5", "mnist-model.h5")
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model = tf.keras.models.load_model("mnist-model.h5")
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def recognize_digit(image):
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image = image.reshape(1, -1)
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prediction = model.predict(image).tolist()[0]
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return {str(i): prediction[i] for i in range(10)}
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gr.Interface(
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io = gr.Interface(
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recognize_digit,
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"sketchpad",
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gradio.outputs.Label(num_top_classes=3),
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live=True,
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capture_session=True,
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).launch()
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)
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io.test_launch()
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io.launch()
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|
@ -7,11 +7,14 @@ import random
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def filter_records(records, gender):
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return records[records['gender'] == gender]
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gr.Interface(filter_records,
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io = gr.Interface(filter_records,
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[
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gr.inputs.Dataframe(headers=["name", "age", "gender"], datatype=["str", "number", "str"], row_count=5),
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gr.inputs.Dropdown(["M", "F", "O"])
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],
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"dataframe",
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description="Enter gender as 'M', 'F', or 'O' for other."
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).launch()
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)
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io.test_launch()
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io.launch()
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@ -2,10 +2,10 @@
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import gradio as gr
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import numpy as np
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import random
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notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
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def generate_tone(note, octave, duration):
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sr = 48000
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a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
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@ -13,13 +13,16 @@ def generate_tone(note, octave, duration):
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duration = int(duration)
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audio = np.linspace(0, duration, duration * sr)
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audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
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return (sr, audio)
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return sr, audio
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gr.Interface(
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io = gr.Interface(
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generate_tone,
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[
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gr.inputs.Dropdown(notes, type="index"),
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gr.inputs.Slider(4, 6, step=1),
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gr.inputs.Textbox(type="number", default=1, label="Duration in seconds")
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], "audio").launch()
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], "audio")
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io.test_launch()
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io.launch()
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|
@ -1,15 +1,13 @@
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# Demo: (Image) -> (Image)
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import gradio as gr
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from time import time
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from PIL import Image
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def image_mod(image):
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return image.rotate(45)
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gr.Interface(image_mod,
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io = gr.Interface(image_mod,
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gr.inputs.Image(type="pil"),
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gr.outputs.Image(type="pil"),
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examples=[
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@ -18,4 +16,7 @@ gr.Interface(image_mod,
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["images/lion.jpg"],
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],
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live=True,
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).launch(share=True)
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)
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io.test_launch()
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io.launch()
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|
@ -1,13 +1,17 @@
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# Demo: (Dataframe) -> (Dataframe)
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import gradio as gr
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import numpy as np
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import random
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def transpose(matrix):
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return matrix.T
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gr.Interface(transpose,
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gr.inputs.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
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"numpy"
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).launch()
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io = gr.Interface(
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transpose,
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gr.inputs.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
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"numpy"
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)
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io.test_launch()
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io.launch()
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|
@ -2,10 +2,14 @@
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import gradio as gr
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import numpy as np
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import random
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def reverse_audio(audio):
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sr, data = audio
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return (sr, np.flipud(data))
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gr.Interface(reverse_audio, "microphone", "audio").launch()
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io = gr.Interface(reverse_audio, "microphone", "audio")
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io.test_launch()
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io.launch()
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@ -1,25 +1,28 @@
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# Demo: (Slider, Dropdown, Radio, CheckboxGroup, Checkbox) -> (Textbox)
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import gradio as gr
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import numpy as np
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def sentence_builder(quantity, animal, place, activity_list, morning):
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return f"""The {quantity} {animal}s went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""
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gr.Interface(sentence_builder,
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[
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gr.inputs.Slider(2, 20),
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gr.inputs.Dropdown(["cat", "dog", "bird"]),
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gr.inputs.Radio(["park", "zoo", "road"]),
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gr.inputs.CheckboxGroup(["ran", "swam", "ate", "slept"]),
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gr.inputs.Checkbox(label="Is it the morning?"),
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],
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"text",
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examples=[
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[2, "cat", "park", ["ran", "swam"], True],
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[4, "dog", "zoo", ["ate", "swam"], False],
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[10, "bird", "road", ["ran"], False],
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[8, "cat", "zoo", ["ate"], True],
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],
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).launch()
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io = gr.Interface(
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sentence_builder,
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[
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gr.inputs.Slider(2, 20),
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gr.inputs.Dropdown(["cat", "dog", "bird"]),
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gr.inputs.Radio(["park", "zoo", "road"]),
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gr.inputs.CheckboxGroup(["ran", "swam", "ate", "slept"]),
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gr.inputs.Checkbox(label="Is it the morning?"),
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],
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"text",
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examples=[
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[2, "cat", "park", ["ran", "swam"], True],
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[4, "dog", "zoo", ["ate", "swam"], False],
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[10, "bird", "road", ["ran"], False],
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[8, "cat", "zoo", ["ate"], True],
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])
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io.test_launch()
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io.launch()
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@ -4,7 +4,6 @@ import gradio as gr
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy import signal
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from scipy.io import wavfile
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def spectrogram(audio):
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@ -15,4 +14,7 @@ def spectrogram(audio):
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return plt
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gr.Interface(spectrogram, "audio", "plot").launch()
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io = gr.Interface(spectrogram, "audio", "plot
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io.test_launch()
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io.launch()
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@ -1,7 +1,6 @@
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# Demo: (Radio, CheckboxGroup, Slider, Checkbox, Dropdown) -> (Image)
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import gradio as gr
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import random
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import matplotlib.pyplot as plt
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import numpy as np
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@ -21,15 +20,15 @@ def stock_forecast(final_year, companies, noise, show_legend, point_style):
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return plt
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gr.Interface(stock_forecast,
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[
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gr.inputs.Radio([2025, 2030, 2035, 2040],
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label="Project to:"),
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gr.inputs.CheckboxGroup(
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["Google", "Microsoft", "Gradio"]),
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gr.inputs.Slider(1, 100),
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"checkbox",
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gr.inputs.Dropdown(["cross", "line", "circle"], label="Style"),
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],
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gr.outputs.Image(plot=True, label="forecast")
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).launch()
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io = gr.Interface(
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stock_forecast,
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[
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gr.inputs.Radio([2025, 2030, 2035, 2040], label="Project to:"),
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gr.inputs.CheckboxGroup(["Google", "Microsoft", "Gradio"]),
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gr.inputs.Slider(1, 100),
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"checkbox",
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gr.inputs.Dropdown(["cross", "line", "circle"], label="Style")],
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gr.outputs.Image(plot=True, label="forecast"))
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io.test_launch()
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io.launch()
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@ -6,6 +6,7 @@ import gradio as gr
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nlp = spacy.load("en_core_web_sm")
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def text_analysis(text):
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doc = nlp(text)
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html = displacy.render(doc, style="dep", page=True)
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@ -21,10 +22,14 @@ def text_analysis(text):
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return pos_tokens, pos_count, html
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gr.Interface(
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io = gr.Interface(
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text_analysis,
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gr.inputs.Textbox(placeholder="Enter sentence here..."),
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[
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"highlight", "key_values", "html"
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]
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).launch()
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)
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io.test_launch()
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io.launch()
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|
@ -1,9 +1,13 @@
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# Demo: (Image) -> (Image)
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import gradio as gr
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import numpy as np
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def snap(image):
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return np.flipud(image)
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gr.Interface(snap, gr.inputs.Image(shape=(100,100), image_mode="L", source="webcam"), "image").launch()
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io = gr.Interface(snap, gr.inputs.Image(shape=(100,100), image_mode="L", source="webcam"), "image")
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io.test_launch()
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io.launch()
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|
@ -3,6 +3,7 @@
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import gradio as gr
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from zipfile import ZipFile
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def zip_to_json(file_obj):
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files = []
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with ZipFile(file_obj.name) as zfile:
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@ -14,4 +15,8 @@ def zip_to_json(file_obj):
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})
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return files
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gr.Interface(zip_to_json, "file", "json").launch()
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io = gr.Interface(zip_to_json, "file", "json")
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io.test_launch()
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io.launch()
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|
@ -3,10 +3,15 @@
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import gradio as gr
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from zipfile import ZipFile
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def zip_two_files(file1, file2):
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with ZipFile('tmp.zip', 'w') as zipObj:
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zipObj.write(file1.name, "file1")
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zipObj.write(file2.name, "file2")
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return "tmp.zip"
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gr.Interface(zip_two_files, ["file", "file"], "file").launch()
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io = gr.Interface(zip_two_files, ["file", "file"], "file")
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io.test_launch()
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io.launch()
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|
@ -15,17 +15,19 @@ import base64
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import numpy as np
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import PIL
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import scipy.io.wavfile
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from gradio import processing_utils
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from gradio import processing_utils, test_data
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import pandas as pd
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import math
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import tempfile
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class InputComponent(Component):
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"""
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Input Component. All input components subclass this.
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"""
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pass
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class Textbox(InputComponent):
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"""
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Component creates a textbox for user to enter input. Provides a string (or number is `type` is "float") as an argument to the wrapped function.
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@ -33,7 +35,7 @@ class Textbox(InputComponent):
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"""
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def __init__(self, lines=1, placeholder=None, default=None, numeric=False, type="str", label=None):
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'''
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"""
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Parameters:
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lines (int): number of line rows to provide in textarea.
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placeholder (str): placeholder hint to provide behind textarea.
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@ -41,7 +43,7 @@ class Textbox(InputComponent):
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numeric (bool): DEPRECATED. Whether the input should be parsed as a number instead of a string.
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type (str): Type of value to be returned by component. "str" returns a string, "number" returns a float value.
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label (str): component name in interface.
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'''
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"""
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self.lines = lines
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self.placeholder = placeholder
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self.default = default
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@ -50,6 +52,13 @@ class Textbox(InputComponent):
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self.type = "number"
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else:
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self.type = type
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if default is None:
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self.test_input = {
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"str": "the quick brown fox jumped over the lazy dog",
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"number": 786.92,
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}[type]
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else:
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self.test_input = default
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super().__init__(label)
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def get_template_context(self):
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@ -77,7 +86,6 @@ class Textbox(InputComponent):
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raise ValueError("Unknown type: " + self.type + ". Please choose from: 'str', 'number'.")
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class Slider(InputComponent):
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"""
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Component creates a slider that ranges from `minimum` to `maximum`. Provides a number as an argument to the wrapped function.
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@ -101,6 +109,7 @@ class Slider(InputComponent):
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step = 10 ** power
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self.step = step
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self.default = minimum if default is None else default
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self.test_input = self.default
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super().__init__(label)
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def get_template_context(self):
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@ -126,10 +135,11 @@ class Checkbox(InputComponent):
|
||||
"""
|
||||
|
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def __init__(self, label=None):
|
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'''
|
||||
"""
|
||||
Parameters:
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label (str): component name in interface.
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||||
'''
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||||
"""
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self.test_input = True
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super().__init__(label)
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@classmethod
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@ -154,6 +164,7 @@ class CheckboxGroup(InputComponent):
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'''
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||||
self.choices = choices
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||||
self.type = type
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||||
self.test_input = self.choices
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||||
super().__init__(label)
|
||||
|
||||
def get_template_context(self):
|
||||
@ -186,6 +197,7 @@ class Radio(InputComponent):
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'''
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self.choices = choices
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||||
self.type = type
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||||
self.test_input = self.choices[0]
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||||
super().__init__(label)
|
||||
|
||||
def get_template_context(self):
|
||||
@ -202,6 +214,7 @@ class Radio(InputComponent):
|
||||
else:
|
||||
raise ValueError("Unknown type: " + self.type + ". Please choose from: 'value', 'index'.")
|
||||
|
||||
|
||||
class Dropdown(InputComponent):
|
||||
"""
|
||||
Component creates a dropdown of which only one can be selected. Provides string representing selected choice as an argument to the wrapped function.
|
||||
@ -217,6 +230,7 @@ class Dropdown(InputComponent):
|
||||
'''
|
||||
self.choices = choices
|
||||
self.type = type
|
||||
self.test_input = self.choices[0]
|
||||
super().__init__(label)
|
||||
|
||||
def get_template_context(self):
|
||||
@ -248,7 +262,7 @@ class Image(InputComponent):
|
||||
invert_colors (bool): whether to invert the image as a preprocessing step.
|
||||
source (str): Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools.
|
||||
tool (str): Tools used for editing. "editor" allows a full screen editor, "select" provides a cropping and zoom tool.
|
||||
type (str): Type of value to be returned by component. "numpy" returns a numpy array with shape (width, height, 3), "pil" returns a PIL image object, "file" returns a temporary file object whose path can be retrieved by file_obj.name.
|
||||
type (str): Type of value to be returned by component. "numpy" returns a numpy array with shape (width, height, 3) and values from 0 to 255, "pil" returns a PIL image object, "file" returns a temporary file object whose path can be retrieved by file_obj.name.
|
||||
label (str): component name in interface.
|
||||
'''
|
||||
self.shape = shape
|
||||
@ -257,6 +271,7 @@ class Image(InputComponent):
|
||||
self.tool = tool
|
||||
self.type = type
|
||||
self.invert_colors = invert_colors
|
||||
self.test_input = test_data.BASE64_IMAGE
|
||||
super().__init__(label)
|
||||
|
||||
@classmethod
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||||
@ -293,6 +308,8 @@ class Image(InputComponent):
|
||||
file_obj = tempfile.NamedTemporaryFile()
|
||||
im.save(file_obj.name)
|
||||
return file_obj
|
||||
else:
|
||||
raise ValueError("Unknown type: " + self.type + ". Please choose from: 'numpy', 'pil', 'file'.")
|
||||
|
||||
def process_example(self, example):
|
||||
if os.path.exists(example):
|
||||
@ -318,14 +335,15 @@ class Audio(InputComponent):
|
||||
"""
|
||||
|
||||
def __init__(self, source="upload", type="numpy", label=None):
|
||||
'''
|
||||
"""
|
||||
Parameters:
|
||||
source (str): Source of audio. "upload" creates a box where user can drop an audio file, "microphone" creates a microphone input.
|
||||
type (str): Type of value to be returned by component. "numpy" returns a 2-set tuple with an integer sample_rate and the data numpy.array of shape (samples, 2), "file" returns a temporary file object whose path can be retrieved by file_obj.name, "mfcc" returns the mfcc coefficients of the input audio.
|
||||
label (str): component name in interface.
|
||||
'''
|
||||
"""
|
||||
self.source = source
|
||||
self.type = type
|
||||
self.test_input = test_data.BASE64_AUDIO
|
||||
super().__init__(label)
|
||||
|
||||
def get_template_context(self):
|
||||
@ -367,6 +385,7 @@ class File(InputComponent):
|
||||
label (str): component name in interface.
|
||||
'''
|
||||
self.type = type
|
||||
self.test_input = None
|
||||
super().__init__(label)
|
||||
|
||||
@classmethod
|
||||
@ -391,7 +410,7 @@ class Dataframe(InputComponent):
|
||||
"""
|
||||
|
||||
def __init__(self, headers=None, row_count=3, col_count=3, datatype="str", type="pandas", label=None):
|
||||
'''
|
||||
"""
|
||||
Parameters:
|
||||
headers (List[str]): Header names to dataframe.
|
||||
row_count (int): Limit number of rows for input.
|
||||
@ -399,14 +418,17 @@ class Dataframe(InputComponent):
|
||||
datatype (Union[str, List[str]]): Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", and "date".
|
||||
type (str): Type of value to be returned by component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for a Python array.
|
||||
label (str): component name in interface.
|
||||
'''
|
||||
"""
|
||||
self.headers = headers
|
||||
self.datatype = datatype
|
||||
self.row_count = row_count
|
||||
self.col_count = len(headers) if headers else col_count
|
||||
self.type = type
|
||||
super().__init__(label)
|
||||
sample_values = {"str": "abc", "number": 786, "bool": True, "date": "02/08/1993"}
|
||||
column_dtypes = [datatype]*self.col_count if isinstance(datatype, str) else datatype
|
||||
self.test_input = [[sample_values[c] for c in column_dtypes] for _ in range(row_count)]
|
||||
|
||||
super().__init__(label)
|
||||
|
||||
def get_template_context(self):
|
||||
return {
|
||||
|
@ -193,19 +193,19 @@ class Interface:
|
||||
|
||||
return config
|
||||
|
||||
def process(self, raw_input):
|
||||
def process(self, raw_input, predict_fn=None):
|
||||
"""
|
||||
:param raw_input: a list of raw inputs to process and apply the
|
||||
prediction(s) on.
|
||||
:param predict_fn: which function to process. If not provided, all of the model functions are used.
|
||||
:return:
|
||||
processed output: a list of processed outputs to return as the
|
||||
prediction(s).
|
||||
duration: a list of time deltas measuring inference time for each
|
||||
prediction fn.
|
||||
"""
|
||||
processed_input = [input_interface.preprocess(
|
||||
raw_input[i]) for i, input_interface in
|
||||
enumerate(self.input_interfaces)]
|
||||
processed_input = [input_interface.preprocess(raw_input[i])
|
||||
for i, input_interface in enumerate(self.input_interfaces)]
|
||||
predictions = []
|
||||
durations = []
|
||||
for predict_fn in self.predict:
|
||||
@ -253,6 +253,22 @@ class Interface:
|
||||
thread.keep_running = False
|
||||
networking.url_ok(path_to_local_server)
|
||||
|
||||
def test_launch(self):
|
||||
for predict_fn in self.predict:
|
||||
print("Test launching: {}()...".format(predict_fn.__name__), end=' ')
|
||||
|
||||
raw_input = []
|
||||
for input_interface in self.input_interfaces:
|
||||
if input_interface.test_input is None: # If no test input is defined for that input interface
|
||||
print("SKIPPED")
|
||||
break
|
||||
else: # If a test input is defined for each interface object
|
||||
raw_input.append(input_interface.test_input)
|
||||
else:
|
||||
self.process(raw_input)
|
||||
print("PASSED")
|
||||
continue
|
||||
|
||||
def launch(self, inline=None, inbrowser=None, share=False, debug=False):
|
||||
"""
|
||||
Parameters
|
||||
|
2
gradio/test_data.py
Normal file
2
gradio/test_data.py
Normal file
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user