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Multiple_Object_Detector_PASCAL_2007 / app.py

Update app.py

d9ed304 over 3 years ago

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from fastai.vision.all import *
import gradio as gr

def get_x(): return _
def get_y(): return _

learn = load_learner("export.pkl")

labels = learn.dls.vocab
def infer(img):
    img = PILImage.create(img)
    _pred, _pred_w_idx, probs = learn.predict(img)
    # gradio doesn't support tensors, so converting to float
    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
    return labels_probs

# get the inputs
inputs = gr.inputs.Image(shape=(192, 192))

# the app outputs two segmented images
outputs = gr.outputs.Label(num_top_classes=3)

EXAMPLES_PATH = Path('./examples')
examples = [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()]

# it's good practice to pass examples, description and a title to guide users
title = 'Multiple Object Detector'
description = 'This app detects objects that appear in the image'
article = "Author: <a href=\"https://huggingface.co/archietram\">Archie Tram</a>. "
intf = gr.Interface(fn=infer, inputs=inputs, outputs=outputs, examples=examples, title=title, description=description, article=article)
intf.launch(inline=False)

	from fastai.vision.all import *
	import gradio as gr

	def get_x(): return _
	def get_y(): return _

	learn = load_learner("export.pkl")

	labels = learn.dls.vocab
	def infer(img):
	img = PILImage.create(img)
	_pred, _pred_w_idx, probs = learn.predict(img)
	# gradio doesn't support tensors, so converting to float
	labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
	return labels_probs

	# get the inputs
	inputs = gr.inputs.Image(shape=(192, 192))

	# the app outputs two segmented images
	outputs = gr.outputs.Label(num_top_classes=3)

	EXAMPLES_PATH = Path('./examples')
	examples = [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()]

	# it's good practice to pass examples, description and a title to guide users
	title = 'Multiple Object Detector'
	description = 'This app detects objects that appear in the image'
	article = "Author: <a href=\"https://huggingface.co/archietram\">Archie Tram</a>. "
	intf = gr.Interface(fn=infer, inputs=inputs, outputs=outputs, examples=examples, title=title, description=description, article=article)
	intf.launch(inline=False)