mirror of
https://github.com/gradio-app/gradio.git
synced 2025-01-24 10:54:04 +08:00
Merge branch 'master' into port-issue
This commit is contained in:
Abubakar Abid
commit
d69ab86247
@ -18,7 +18,7 @@ jobs:
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. venv/bin/activate
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pip install --upgrade pip
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pip install -r gradio.egg-info/requires.txt
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pip install shap IPython comet_ml wandb mlflow
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pip install shap IPython comet_ml wandb mlflow tensorflow transformers
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pip install selenium==4.0.0a6.post2 coverage scikit-image
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- run:
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command: |
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@ -59,19 +59,19 @@ iface = gr.Interface(
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gr.inputs.Timeseries(x="time", y="value", optional=True),
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],
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outputs=[
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gr.outputs.Textbox(),
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gr.outputs.Label(),
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gr.outputs.Audio(),
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gr.outputs.Image(),
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gr.outputs.Video(),
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gr.outputs.KeyValues(),
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gr.outputs.HighlightedText(),
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gr.outputs.JSON(),
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gr.outputs.HTML(),
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gr.outputs.File(),
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gr.outputs.Dataframe(),
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gr.outputs.Carousel("image"),
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gr.outputs.Timeseries(x="time", y="value")
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gr.outputs.Textbox(label="Textbox"),
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gr.outputs.Label(label="Label"),
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gr.outputs.Audio(label="Audio"),
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gr.outputs.Image(label="Image"),
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gr.outputs.Video(label="Video"),
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gr.outputs.KeyValues(label="KeyValues"),
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gr.outputs.HighlightedText(label="HighlightedText"),
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gr.outputs.JSON(label="JSON"),
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gr.outputs.HTML(label="HTML"),
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gr.outputs.File(label="File"),
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gr.outputs.Dataframe(label="Dataframe"),
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gr.outputs.Carousel("image", label="Carousel"),
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gr.outputs.Timeseries(x="time", y="value", label="Timeseries")
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],
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theme="huggingface",
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title="Kitchen Sink",
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@ -1,11 +1,14 @@
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Metadata-Version: 1.0
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Metadata-Version: 2.1
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Name: gradio
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Version: 2.5.2
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Version: 2.5.3
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Summary: Python library for easily interacting with trained machine learning models
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Home-page: https://github.com/gradio-app/gradio-UI
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Author: Abubakar Abid
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Author-email: a12d@stanford.edu
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License: Apache License 2.0
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Description: UNKNOWN
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Keywords: machine learning,visualization,reproducibility
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Platform: UNKNOWN
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License-File: LICENSE
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UNKNOWN
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@ -1,15 +1,15 @@
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Flask-Cors>=3.0.8
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Flask-Login
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Flask>=1.1.1
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analytics-python
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ffmpy
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flask-cachebuster
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markdown2
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matplotlib
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numpy
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pandas
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paramiko
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pillow
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pycryptodome
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pydub
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matplotlib
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pandas
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pillow
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ffmpy
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markdown2
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pycryptodome
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requests
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paramiko
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analytics-python
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Flask>=1.1.1
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Flask-Cors>=3.0.8
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flask-cachebuster
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Flask-Login
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@ -28,6 +28,51 @@ def get_huggingface_interface(model_name, api_key, alias):
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return fp.name
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pipelines = {
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'audio-classification': {
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# example model: https://hf.co/ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition
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'inputs': inputs.Audio(label="Input", source="upload",
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type="filepath"),
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'outputs': outputs.Label(label="Class", type="confidences"),
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'preprocess': lambda i: base64.b64decode(i['data'].split(",")[1]), # convert the base64 representation to binary
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'postprocess': lambda r: {i["label"].split(", ")[0]: i["score"] for i in r.json()}
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},
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'automatic-speech-recognition': {
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# example model: https://hf.co/jonatasgrosman/wav2vec2-large-xlsr-53-english
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'inputs': inputs.Audio(label="Input", source="upload",
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type="filepath"),
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'outputs': outputs.Textbox(label="Output"),
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'preprocess': lambda i: base64.b64decode(i['data'].split(",")[1]), # convert the base64 representation to binary
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'postprocess': lambda r: r.json()["text"]
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},
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'feature-extraction': {
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# example model: hf.co/julien-c/distilbert-feature-extraction
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'inputs': inputs.Textbox(label="Input"),
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'outputs': outputs.Dataframe(label="Output"),
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: r.json()[0],
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},
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'fill-mask': {
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'inputs': inputs.Textbox(label="Input"),
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'outputs': outputs.Label(label="Classification", type="confidences"),
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: {i["token_str"]: i["score"] for i in r.json()}
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},
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'image-classification': {
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# Example: https://huggingface.co/google/vit-base-patch16-224
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'inputs': inputs.Image(label="Input Image", type="filepath"),
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'outputs': outputs.Label(label="Classification", type="confidences"),
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'preprocess': lambda i: base64.b64decode(i.split(",")[1]), # convert the base64 representation to binary
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'postprocess': lambda r: {i["label"].split(", ")[0]: i["score"] for i in r.json()}
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},
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# TODO: support image segmentation pipeline -- should we add a new output component type?
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# 'image-segmentation': {
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# # Example: https://hf.co/facebook/detr-resnet-50-panoptic
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# 'inputs': inputs.Image(label="Input Image", type="filepath"),
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# 'outputs': outputs.Image(label="Segmentation"),
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# 'preprocess': lambda i: base64.b64decode(i.split(",")[1]), # convert the base64 representation to binary
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# 'postprocess': lambda x: base64.b64encode(x.json()[0]["mask"]).decode('utf-8'),
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# },
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# TODO: also: support NER pipeline, object detection, table question answering
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'question-answering': {
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'inputs': [inputs.Textbox(label="Context", lines=7), inputs.Textbox(label="Question")],
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'outputs': [outputs.Textbox(label="Answer"), outputs.Label(label="Score")],
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@ -35,24 +80,23 @@ def get_huggingface_interface(model_name, api_key, alias):
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'postprocess': lambda r: (r.json()["answer"], r.json()["score"]),
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# 'examples': [['My name is Sarah and I live in London', 'Where do I live?']]
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},
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'text-generation': {
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'inputs': inputs.Textbox(label="Input"),
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'outputs': outputs.Textbox(label="Output"),
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: r.json()[0]["generated_text"],
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# 'examples': [['My name is Clara and I am']]
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},
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'summarization': {
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'inputs': inputs.Textbox(label="Input"),
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'outputs': outputs.Textbox(label="Summary"),
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: r.json()[0]["summary_text"]
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},
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'translation': {
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'text-classification': {
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'inputs': inputs.Textbox(label="Input"),
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'outputs': outputs.Textbox(label="Translation"),
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'outputs': outputs.Label(label="Classification", type="confidences"),
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: r.json()[0]["translation_text"]
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'postprocess': lambda r: {i["label"].split(", ")[0]: i["score"] for i in r.json()[0]}
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},
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'text-generation': {
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'inputs': inputs.Textbox(label="Input"),
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'outputs': outputs.Textbox(label="Output"),
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: r.json()[0]["generated_text"],
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},
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'text2text-generation': {
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'inputs': inputs.Textbox(label="Input"),
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@ -60,18 +104,11 @@ def get_huggingface_interface(model_name, api_key, alias):
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: r.json()[0]["generated_text"]
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},
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'text-classification': {
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'translation': {
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'inputs': inputs.Textbox(label="Input"),
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'outputs': outputs.Label(label="Classification", type="confidences"),
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'outputs': outputs.Textbox(label="Translation"),
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: {'Negative': r.json()[0][0]["score"],
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'Positive': r.json()[0][1]["score"]}
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},
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'fill-mask': {
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'inputs': inputs.Textbox(label="Input"),
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'outputs': outputs.Label(label="Classification", type="confidences"),
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: {i["token_str"]: i["score"] for i in r.json()}
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'postprocess': lambda r: r.json()[0]["translation_text"]
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},
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'zero-shot-classification': {
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'inputs': [inputs.Textbox(label="Input"),
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@ -84,26 +121,7 @@ def get_huggingface_interface(model_name, api_key, alias):
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'postprocess': lambda r: {r.json()["labels"][i]: r.json()["scores"][i] for i in
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range(len(r.json()["labels"]))}
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},
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'automatic-speech-recognition': {
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'inputs': inputs.Audio(label="Input", source="upload",
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type="filepath"),
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'outputs': outputs.Textbox(label="Output"),
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'preprocess': lambda i: {"inputs": i},
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'postprocess': lambda r: r.json()["text"]
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},
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'image-classification': {
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'inputs': inputs.Image(label="Input Image", type="filepath"),
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'outputs': outputs.Label(label="Classification", type="confidences"),
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'preprocess': lambda i: base64.b64decode(i.split(",")[1]), # convert the base64 representation to binary
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'postprocess': lambda r: {i["label"].split(", ")[0]: i["score"] for i in r.json()}
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},
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'feature-extraction': {
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# example model: hf.co/julien-c/distilbert-feature-extraction
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'inputs': inputs.Textbox(label="Input"),
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'outputs': outputs.Dataframe(label="Output"),
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'preprocess': lambda x: {"inputs": x},
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'postprocess': lambda r: r.json()[0],
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},
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# Non-HF pipelines
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'sentence-similarity': {
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# example model: hf.co/sentence-transformers/distilbert-base-nli-stsb-mean-tokens
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'inputs': [
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@ -134,7 +152,7 @@ def get_huggingface_interface(model_name, api_key, alias):
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}
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if p is None or not(p in pipelines):
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print("Warning: no interface information found")
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raise ValueError("Unsupported pipeline type: {}".format(type(p)))
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pipeline = pipelines[p]
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@ -221,3 +239,138 @@ repos = {
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"spaces": get_spaces_interface,
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}
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def load_from_pipeline(pipeline):
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"""
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Gets the appropriate Interface kwargs for a given Hugging Face transformers.Pipeline.
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pipeline (transformers.Pipeline): the transformers.Pipeline from which to create an interface
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Returns:
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(dict): a dictionary of kwargs that can be used to construct an Interface object
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"""
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try:
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import transformers
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except ImportError:
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raise ImportError("transformers not installed. Please try `pip install transformers`")
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if not isinstance(pipeline, transformers.Pipeline):
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raise ValueError("pipeline must be a transformers.Pipeline")
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|
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# Handle the different pipelines. The has_attr() checks to make sure the pipeline exists in the
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# version of the transformers library that the user has installed.
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if hasattr(transformers, 'AudioClassificationPipeline') and isinstance(pipeline, transformers.AudioClassificationPipeline):
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pipeline_info = {
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'inputs': inputs.Audio(label="Input", source="microphone",
|
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type="filepath"),
|
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'outputs': outputs.Label(label="Class", type="confidences"),
|
||||
'preprocess': lambda i: {"inputs": i},
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'postprocess': lambda r: {i["label"].split(", ")[0]: i["score"] for i in r}
|
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}
|
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elif hasattr(transformers, 'AutomaticSpeechRecognitionPipeline') and isinstance(pipeline, transformers.AutomaticSpeechRecognitionPipeline):
|
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pipeline_info = {
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'inputs': inputs.Audio(label="Input", source="microphone",
|
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type="filepath"),
|
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'outputs': outputs.Textbox(label="Output"),
|
||||
'preprocess': lambda i: {"inputs": i},
|
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'postprocess': lambda r: r["text"]
|
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}
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elif hasattr(transformers, 'FeatureExtractionPipeline') and isinstance(pipeline, transformers.FeatureExtractionPipeline):
|
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pipeline_info = {
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'inputs': inputs.Textbox(label="Input"),
|
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'outputs': outputs.Dataframe(label="Output"),
|
||||
'preprocess': lambda x: {"inputs": x},
|
||||
'postprocess': lambda r: r[0],
|
||||
}
|
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elif hasattr(transformers, 'FillMaskPipeline') and isinstance(pipeline, transformers.FillMaskPipeline):
|
||||
pipeline_info = {
|
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'inputs': inputs.Textbox(label="Input"),
|
||||
'outputs': outputs.Label(label="Classification", type="confidences"),
|
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'preprocess': lambda x: {"inputs": x},
|
||||
'postprocess': lambda r: {i["token_str"]: i["score"] for i in r}
|
||||
}
|
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elif hasattr(transformers, 'ImageClassificationPipeline') and isinstance(pipeline, transformers.ImageClassificationPipeline):
|
||||
pipeline_info = {
|
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'inputs': inputs.Image(label="Input Image", type="filepath"),
|
||||
'outputs': outputs.Label(label="Classification", type="confidences"),
|
||||
'preprocess': lambda i: {"images": i},
|
||||
'postprocess': lambda r: {i["label"].split(", ")[0]: i["score"] for i in r}
|
||||
}
|
||||
elif hasattr(transformers, 'QuestionAnsweringPipeline') and isinstance(pipeline, transformers.QuestionAnsweringPipeline):
|
||||
pipeline_info = {
|
||||
'inputs': [inputs.Textbox(label="Context", lines=7), inputs.Textbox(label="Question")],
|
||||
'outputs': [outputs.Textbox(label="Answer"), outputs.Label(label="Score")],
|
||||
'preprocess': lambda c, q: {"context": c, "question": q},
|
||||
'postprocess': lambda r: (r["answer"], r["score"]),
|
||||
}
|
||||
elif hasattr(transformers, 'SummarizationPipeline') and isinstance(pipeline, transformers.SummarizationPipeline):
|
||||
pipeline_info = {
|
||||
'inputs': inputs.Textbox(label="Input", lines=7),
|
||||
'outputs': outputs.Textbox(label="Summary"),
|
||||
'preprocess': lambda x: {"inputs": x},
|
||||
'postprocess': lambda r: r[0]["summary_text"]
|
||||
}
|
||||
elif hasattr(transformers, 'TextClassificationPipeline') and isinstance(pipeline, transformers.TextClassificationPipeline):
|
||||
pipeline_info = {
|
||||
'inputs': inputs.Textbox(label="Input"),
|
||||
'outputs': outputs.Label(label="Classification", type="confidences"),
|
||||
'preprocess': lambda x: [x],
|
||||
'postprocess': lambda r: {i["label"].split(", ")[0]: i["score"] for i in r}
|
||||
}
|
||||
elif hasattr(transformers, 'TextGenerationPipeline') and isinstance(pipeline, transformers.TextGenerationPipeline):
|
||||
pipeline_info = {
|
||||
'inputs': inputs.Textbox(label="Input"),
|
||||
'outputs': outputs.Textbox(label="Output"),
|
||||
'preprocess': lambda x: {"text_inputs": x},
|
||||
'postprocess': lambda r: r[0]["generated_text"],
|
||||
}
|
||||
elif hasattr(transformers, 'TranslationPipeline') and isinstance(pipeline, transformers.TranslationPipeline):
|
||||
pipeline_info = {
|
||||
'inputs': inputs.Textbox(label="Input"),
|
||||
'outputs': outputs.Textbox(label="Translation"),
|
||||
'preprocess': lambda x: [x],
|
||||
'postprocess': lambda r: r[0]["translation_text"]
|
||||
}
|
||||
elif hasattr(transformers, 'Text2TextGenerationPipeline') and isinstance(pipeline, transformers.Text2TextGenerationPipeline):
|
||||
pipeline_info = {
|
||||
'inputs': inputs.Textbox(label="Input"),
|
||||
'outputs': outputs.Textbox(label="Generated Text"),
|
||||
'preprocess': lambda x: [x],
|
||||
'postprocess': lambda r: r[0]["generated_text"]
|
||||
}
|
||||
elif hasattr(transformers, 'ZeroShotClassificationPipeline') and isinstance(pipeline, transformers.ZeroShotClassificationPipeline):
|
||||
pipeline_info = {
|
||||
'inputs': [inputs.Textbox(label="Input"),
|
||||
inputs.Textbox(label="Possible class names ("
|
||||
"comma-separated)"),
|
||||
inputs.Checkbox(label="Allow multiple true classes")],
|
||||
'outputs': outputs.Label(label="Classification", type="confidences"),
|
||||
'preprocess': lambda i, c, m: {"sequences": i,
|
||||
"candidate_labels": c, "multi_label": m},
|
||||
'postprocess': lambda r: {r["labels"][i]: r["scores"][i] for i in
|
||||
range(len(r["labels"]))}
|
||||
}
|
||||
else:
|
||||
raise ValueError("Unsupported pipeline type: {}".format(type(pipeline)))
|
||||
|
||||
# define the function that will be called by the Interface
|
||||
def fn(*params):
|
||||
data = pipeline_info["preprocess"](*params)
|
||||
# special cases that needs to be handled differently
|
||||
if isinstance(pipeline, (transformers.TextClassificationPipeline,
|
||||
transformers.Text2TextGenerationPipeline,
|
||||
transformers.TranslationPipeline)):
|
||||
data = pipeline(*data)
|
||||
else:
|
||||
data = pipeline(**data)
|
||||
# print("Before postprocessing", data)
|
||||
output = pipeline_info["postprocess"](data)
|
||||
return output
|
||||
|
||||
interface_info = pipeline_info.copy()
|
||||
interface_info["fn"] = fn
|
||||
del interface_info["preprocess"]
|
||||
del interface_info["postprocess"]
|
||||
|
||||
# define the title/description of the Interface
|
||||
interface_info["title"] = pipeline.model.__class__.__name__
|
||||
|
||||
return interface_info
|
||||
|
||||
|
||||
|
||||
@ -1021,6 +1021,8 @@ class Audio(InputComponent):
|
||||
return processing_utils.encode_file_to_base64(x, type="audio")
|
||||
|
||||
def serialize(self, x, called_directly):
|
||||
if x is None:
|
||||
return None
|
||||
if self.type == "filepath" or called_directly:
|
||||
name = x
|
||||
elif self.type == "file":
|
||||
|
||||
@ -22,7 +22,7 @@ from gradio import networking, strings, utils, encryptor, queue
|
||||
from gradio.inputs import get_input_instance
|
||||
from gradio.outputs import get_output_instance
|
||||
from gradio.interpretation import quantify_difference_in_label, get_regression_or_classification_value
|
||||
from gradio.external import load_interface
|
||||
from gradio.external import load_interface, load_from_pipeline
|
||||
|
||||
|
||||
class Interface:
|
||||
@ -59,6 +59,19 @@ class Interface:
|
||||
interface.api_mode = True # set api mode to true so that the interface will not preprocess/postprocess
|
||||
return interface
|
||||
|
||||
@classmethod
|
||||
def from_pipeline(cls, pipeline, **kwargs):
|
||||
"""
|
||||
Class method to construct an Interface from a Hugging Face transformers.Pipeline.
|
||||
pipeline (transformers.Pipeline):
|
||||
Returns:
|
||||
(gradio.Interface): a Gradio Interface object from the given Pipeline
|
||||
"""
|
||||
interface_info = load_from_pipeline(pipeline)
|
||||
kwargs = dict(interface_info, **kwargs)
|
||||
interface = cls(**kwargs)
|
||||
return interface
|
||||
|
||||
def __init__(self, fn, inputs=None, outputs=None, verbose=None, examples=None,
|
||||
examples_per_page=10, live=False, layout="unaligned", show_input=True, show_output=True,
|
||||
capture_session=None, interpretation=None, num_shap=2.0, theme=None, repeat_outputs_per_model=True,
|
||||
|
||||
@ -18,7 +18,7 @@ en = {
|
||||
"COLAB_DEBUG_TRUE": "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. "
|
||||
"To turn off, set debug=False in launch().",
|
||||
"COLAB_DEBUG_FALSE": "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()",
|
||||
"SHARE_LINK_MESSAGE": "\nThis share link will expire in 72 hours. To get longer links, send an email to: support@gradio.app",
|
||||
"SHARE_LINK_MESSAGE": "\nThis share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)",
|
||||
"PRIVATE_LINK_MESSAGE": "Since this is a private endpoint, this share link will never expire.",
|
||||
"INLINE_DISPLAY_BELOW": "Interface loading below...",
|
||||
"MEDIA_PERMISSIONS_IN_COLAB": "Your interface requires microphone or webcam permissions - this may cause issues in Colab. Use the External URL in case of issues.",
|
||||
|
||||
@ -1,18 +1,18 @@
|
||||
{
|
||||
"files": {
|
||||
"main.css": "/static/css/main.6ccf3d75.css",
|
||||
"main.css": "/static/css/main.1d47367f.css",
|
||||
"main.js": "/static/bundle.js",
|
||||
"index.html": "/index.html",
|
||||
"static/media/arrow-left.e497f657.svg": "/static/media/arrow-left.e497f657.svg",
|
||||
"static/media/arrow-right.ea6059fd.svg": "/static/media/arrow-right.ea6059fd.svg",
|
||||
"static/media/clear.33f9b5f3.svg": "/static/media/clear.33f9b5f3.svg",
|
||||
"static/media/edit.44bd4fe1.svg": "/static/media/edit.44bd4fe1.svg",
|
||||
"static/media/logo.411acfd1.svg": "/static/media/logo.411acfd1.svg",
|
||||
"static/media/arrow-left.794a4706.svg": "/static/media/arrow-left.794a4706.svg",
|
||||
"static/media/arrow-right.5a7d4ada.svg": "/static/media/arrow-right.5a7d4ada.svg",
|
||||
"static/media/clear.85cf6de8.svg": "/static/media/clear.85cf6de8.svg",
|
||||
"static/media/edit.c6b7d6f7.svg": "/static/media/edit.c6b7d6f7.svg",
|
||||
"static/media/logo.36a8f455.svg": "/static/media/logo.36a8f455.svg",
|
||||
"static/media/logo_loading.e93acd82.jpg": "/static/media/logo_loading.e93acd82.jpg"
|
||||
},
|
||||
"entrypoints": [
|
||||
"static/bundle.css",
|
||||
"static/css/main.6ccf3d75.css",
|
||||
"static/css/main.1d47367f.css",
|
||||
"static/bundle.js"
|
||||
]
|
||||
}
|
||||
@ -8,4 +8,4 @@
|
||||
window.config = {{ config|tojson }};
|
||||
} catch (e) {
|
||||
window.config = {};
|
||||
}</script><script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script><title>Gradio</title><link href="static/bundle.css" rel="stylesheet"><link href="static/css/main.6ccf3d75.css" rel="stylesheet"></head><body style="height:100%"><div id="root" style="height:100%"></div><script src="static/bundle.js"></script></body></html>
|
||||
}</script><script src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script><title>Gradio</title><link href="static/bundle.css" rel="stylesheet"><link href="static/css/main.1d47367f.css" rel="stylesheet"></head><body style="height:100%"><div id="root" style="height:100%"></div><script src="static/bundle.js"></script></body></html>
|
||||
2
setup.py
2
setup.py
@ -5,7 +5,7 @@ except ImportError:
|
||||
|
||||
setup(
|
||||
name='gradio',
|
||||
version='2.5.2',
|
||||
version='2.5.3',
|
||||
include_package_data=True,
|
||||
description='Python library for easily interacting with trained machine learning models',
|
||||
author='Abubakar Abid',
|
||||
|
||||
@ -2,6 +2,8 @@ import unittest
|
||||
import pathlib
|
||||
import gradio as gr
|
||||
import os
|
||||
import transformers
|
||||
|
||||
|
||||
"""
|
||||
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.
|
||||
@ -153,7 +155,7 @@ class TestLoadInterface(unittest.TestCase):
|
||||
io = gr.Interface(**interface_info)
|
||||
io.api_mode = True
|
||||
output = io("I am happy, I love you.")
|
||||
self.assertGreater(output['Positive'], 0.5)
|
||||
self.assertGreater(output['POSITIVE'], 0.5)
|
||||
|
||||
def test_image_classification_model(self):
|
||||
interface_info = gr.external.load_interface("models/google/vit-base-patch16-224")
|
||||
@ -176,6 +178,13 @@ class TestLoadInterface(unittest.TestCase):
|
||||
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_image_to_image_space(self):
|
||||
def assertIsFile(path):
|
||||
if not pathlib.Path(path).resolve().is_file():
|
||||
@ -187,5 +196,13 @@ class TestLoadInterface(unittest.TestCase):
|
||||
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()
|
||||
@ -101,7 +101,7 @@ class TestInterface(unittest.TestCase):
|
||||
def test_interface_load(self):
|
||||
io = Interface.load("models/distilbert-base-uncased-finetuned-sst-2-english", alias="sentiment_classifier")
|
||||
output = io("I am happy, I love you.")
|
||||
self.assertGreater(output['Positive'], 0.5)
|
||||
self.assertGreater(output['POSITIVE'], 0.5)
|
||||
|
||||
def test_interface_none_interp(self):
|
||||
interface = Interface(lambda x: x, "textbox", "label", interpretation=[None])
|
||||
|
||||
@ -14,11 +14,12 @@ sys.path.insert(0, GRADIO_DEMO_DIR)
|
||||
with open("demos.json") as demos_file:
|
||||
demo_port_sets = json.load(demos_file)
|
||||
|
||||
def launch_demo(demo_file):
|
||||
subprocess.call(f"python {demo_file}", shell=True)
|
||||
def launch_demo(demo_folder):
|
||||
subprocess.call(f"cd {demo_folder} && python run.py", shell=True)
|
||||
|
||||
for demo_name, port in demo_port_sets:
|
||||
demo_file = os.path.join(GRADIO_DEMO_DIR, demo_name, "run.py")
|
||||
demo_folder = os.path.join(GRADIO_DEMO_DIR, demo_name)
|
||||
demo_file = os.path.join(demo_folder, "run.py")
|
||||
with open(demo_file, 'r') as file:
|
||||
filedata = file.read()
|
||||
filedata = filedata.replace(
|
||||
@ -26,7 +27,7 @@ for demo_name, port in demo_port_sets:
|
||||
f'if __name__ == "__main__":\n iface.server_port={port}')
|
||||
with open(demo_file, 'w') as file:
|
||||
file.write(filedata)
|
||||
demo_thread = threading.Thread(target=launch_demo, args=(demo_file,))
|
||||
demo_thread = threading.Thread(target=launch_demo, args=(demo_folder,))
|
||||
demo_thread.start()
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
124
website/homepage/src/other_templates/kitchen_sink_template.html
Normal file
124
website/homepage/src/other_templates/kitchen_sink_template.html
Normal file
@ -0,0 +1,124 @@
|
||||
<!doctype html>
|
||||
|
||||
<html lang="en">
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<title>Gradio</title>
|
||||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/tailwindcss/2.2.19/tailwind.min.css"/>
|
||||
<link href="/gradio_static/bundle.css" rel="stylesheet">
|
||||
<style></style>
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div class="bg-gray-100 p-2">
|
||||
<div class="max-w-5xl mx-auto">
|
||||
<div class="mb-2 text-center text-gray-800 font-semibold">THEME</div>
|
||||
<div class="theme-set mb-2 flex flex-wrap gap-x-2 gap-y-0.5 justify-center"></div>
|
||||
<div class="mb-2 text-center text-gray-800 font-semibold">
|
||||
INPUTS (<a class="underline hover:text-blue-600" href="javascript:show_all_inputs();">all</a> / <a class="underline hover:text-blue-600" href="javascript:hide_all_inputs();">none</a>)
|
||||
</div>
|
||||
<div class="input-components mb-2 flex flex-wrap gap-x-2 gap-y-0.5 justify-center"></div>
|
||||
<div class="mb-2 text-center text-gray-800 font-semibold">
|
||||
OUTPUTS (<a class="underline hover:text-blue-600" href="javascript:show_all_outputs();">all</a> / <a class="underline hover:text-blue-600" href="javascript:hide_all_outputs();">none</a>)
|
||||
</div>
|
||||
<div class="output-components mb-2 flex flex-wrap gap-x-2 gap-y-0.5 justify-center"></div>
|
||||
</div>
|
||||
</div>
|
||||
<div id="target"></div>
|
||||
|
||||
<script>
|
||||
var set_theme = theme => {
|
||||
if (theme.startsWith("dark")) {
|
||||
document.querySelector("#target").classList.add("dark");
|
||||
theme = theme.substring(4);
|
||||
} else {
|
||||
document.querySelector("#target").classList.remove("dark");
|
||||
}
|
||||
document.querySelector(".gradio_bg").setAttribute("theme", theme);
|
||||
}
|
||||
var hidden_inputs = new Set();
|
||||
var hidden_outputs = new Set();
|
||||
var reset_component_visibility = () => {
|
||||
let style_element = document.querySelector("style");
|
||||
let style_html = "";
|
||||
if (hidden_inputs.size > 0) {
|
||||
let inputs_selected = Array(...hidden_inputs).map(i => `.panel:nth-child(1) .component:nth-child(${i + 1})`).join(", ");
|
||||
style_html += `${inputs_selected} { display: none !important }`;
|
||||
}
|
||||
if (hidden_outputs.size > 0) {
|
||||
let outputs_selected = Array(...hidden_outputs).map(i => `.panel:nth-child(2) .component:nth-child(${i + 2})`).join(", ");
|
||||
style_html += `${outputs_selected} { display: none !important }`;
|
||||
}
|
||||
style_element.innerHTML = style_html;
|
||||
}
|
||||
var toggle_input = index => {
|
||||
if (hidden_inputs.has(index)) {
|
||||
hidden_inputs.delete(index)
|
||||
} else {
|
||||
hidden_inputs.add(index)
|
||||
}
|
||||
reset_component_visibility();
|
||||
}
|
||||
var toggle_output = index => {
|
||||
if (hidden_outputs.has(index)) {
|
||||
hidden_outputs.delete(index)
|
||||
} else {
|
||||
hidden_outputs.add(index)
|
||||
}
|
||||
reset_component_visibility();
|
||||
}
|
||||
var show_all_inputs = () => {
|
||||
document.querySelectorAll(".input-components input").forEach(n => n.checked = true);
|
||||
hidden_inputs.clear();
|
||||
reset_component_visibility();
|
||||
}
|
||||
var hide_all_inputs = () => {
|
||||
document.querySelectorAll(".input-components input").forEach(n => n.checked = false);
|
||||
window.config.input_components.forEach((_, i) => hidden_inputs.add(i));
|
||||
reset_component_visibility();
|
||||
}
|
||||
var show_all_outputs = () => {
|
||||
document.querySelectorAll(".output-components input").forEach(n => n.checked = true);
|
||||
hidden_outputs.clear();
|
||||
reset_component_visibility();
|
||||
}
|
||||
var hide_all_outputs = () => {
|
||||
document.querySelectorAll(".output-components input").forEach(n => n.checked = false);
|
||||
window.config.output_components.forEach((_, i) => hidden_outputs.add(i));
|
||||
reset_component_visibility();
|
||||
}
|
||||
</script>
|
||||
<script>
|
||||
var demo_endpoint = "/demo";
|
||||
window.config = [];
|
||||
let themes = ["default", "huggingface", "grass", "peach", "darkdefault", "darkhuggingface", "darkgrass", "darkpeach"]
|
||||
let theme_html = "";
|
||||
for (let theme of themes) {
|
||||
theme_html += `<label><input type="radio" name="theme" onclick="set_theme('${theme}')"> ${theme}</label>`;
|
||||
}
|
||||
document.querySelector(".theme-set").innerHTML = theme_html;
|
||||
fetch('/demo/kitchen_sink/config')
|
||||
.then(response => response.json())
|
||||
.then(demo => {
|
||||
demo.target = "target";
|
||||
demo.endpoint = demo_endpoint + "/kitchen_sink/api/";
|
||||
window.config = demo;
|
||||
|
||||
let input_html = "";
|
||||
for (let [i, input_component] of demo["input_components"].entries()) {
|
||||
input_html += `<label><input type="checkbox" checked name="input" onchange="toggle_input(${i})"> ${input_component["label"]}</label>`;
|
||||
}
|
||||
document.querySelector(".input-components").innerHTML = input_html;
|
||||
let output_html = "";
|
||||
for (let [i, output_component] of demo["output_components"].entries()) {
|
||||
output_html += `<label><input type="checkbox" checked name="output" onchange="toggle_output(${i})"> ${output_component["label"]}</label>`;
|
||||
}
|
||||
document.querySelector(".output-components").innerHTML = output_html;
|
||||
});
|
||||
</script>
|
||||
<script async src="/gradio_static/bundle.js"></script>
|
||||
</body>
|
||||
|
||||
</html>
|
||||
@ -1,48 +0,0 @@
|
||||
<!doctype html>
|
||||
|
||||
<html lang="en">
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<title>Gradio</title>
|
||||
<link href="/gradio_static/bundle.css" rel="stylesheet">
|
||||
</head>
|
||||
|
||||
<body>
|
||||
<div style="font-family:Verdana, Geneva, Tahoma, sans-serif; display: flex; justify-content: center; gap: 2rem; background-color: lightgray; padding: 2rem">
|
||||
<label><input type="radio" name="theme" value="default"> default</label>
|
||||
<label><input type="radio" name="theme" value="huggingface"> huggingface</label>
|
||||
<label><input type="radio" name="theme" value="grass"> grass</label>
|
||||
<label><input type="radio" name="theme" value="peach"> peach</label>
|
||||
<label><input type="radio" name="theme" value="darkdefault"> darkdefault</label>
|
||||
<label><input type="radio" name="theme" value="darkhuggingface"> darkhuggingface</label>
|
||||
<label><input type="radio" name="theme" value="darkgrass"> darkgrass</label>
|
||||
<label><input type="radio" name="theme" value="darkpeach"> darkpeach</label>
|
||||
</div>
|
||||
<div id="target"></div>
|
||||
|
||||
<script>
|
||||
var demo_endpoint = "/demo";
|
||||
window.config = [];
|
||||
fetch('/demo/kitchen_sink/config')
|
||||
.then(response => response.json())
|
||||
.then(demo => {
|
||||
demo.target = "target";
|
||||
demo.endpoint = demo_endpoint + "/" + demo_name + "/api/";
|
||||
window.config = demo;
|
||||
});
|
||||
</script>
|
||||
<script async src="/gradio_static/bundle.js"></script>
|
||||
<script>
|
||||
var set_theme = theme => {
|
||||
if (theme.startswith("dark")) {
|
||||
document.querySelector("#target").classList.add("dark");
|
||||
theme = theme.substring(5);
|
||||
}
|
||||
document.querySelector(".gradio_bg").setAttribute(theme);
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
|
||||
</html>
|
||||
28
website/nginx.conf
Normal file
28
website/nginx.conf
Normal file
@ -0,0 +1,28 @@
|
||||
user nginx;
|
||||
worker_processes auto;
|
||||
|
||||
error_log /var/log/nginx/error.log warn;
|
||||
pid /var/run/nginx.pid;
|
||||
|
||||
|
||||
events {
|
||||
worker_connections 256;
|
||||
multi_accept on;
|
||||
}
|
||||
|
||||
http {
|
||||
include /etc/nginx/mime.types;
|
||||
default_type application/octet-stream;
|
||||
client_max_body_size 200M;
|
||||
client_body_buffer_size 20M;
|
||||
|
||||
server {
|
||||
listen 80;
|
||||
location / {
|
||||
proxy_pass http://localhost:8080/;
|
||||
}
|
||||
location /demo/ {
|
||||
proxy_pass http://localhost:8070/demo/;
|
||||
}
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user