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module = hub.Module(name = "ace2p")

res = module.segmentation(paths = ["img1.jpg"], visualization = True, output_dir = 'ace2p_output')

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module = hub.Module(name="humanseg_lite", version = "1.1.1")

res = module.segment(paths = ["img1.jpg"], visualization = True)

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module = hub.Module(name="openpose_body_estimation")

res = module.predict( img = "img1.jpg", visualization = True, save_path = 'New')

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module = hub.Module(name="ultra_light_fast_generic_face_detector_1mb_640")

res = module.face_detection(paths = ["img1.jpg"], visualization = True, output_dir = 'DETECTED') 

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import paddlehub as hub

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!pip install --upgrade paddlepaddle
!pip install --upgrade paddlehub

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# save the model
model.save("Intent_Classification.h5")

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sentence = "An Artist released a new music album "
tokens = Tokenizer.texts_to_sequences([sentence])
tokens = pad_sequences(tokens, maxlen = 6000)
prediction = model.predict(np.array(tokens))
pred = np.argmax(prediction)
classes = ['BookRestaurant','GetWeather','PlayMusic','RateBook']
classes[pred]

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sentence = "this novel deserves a rating of 10"
tokens = Tokenizer.texts_to_sequences([sentence])
tokens = pad_sequences(tokens, maxlen = 6000)
prediction = model.predict(np.array(tokens))
pred = np.argmax(prediction)
classes = ['BookRestaurant','GetWeather','PlayMusic','RateBook']
classes[pred]

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sentence = "is it raining ?"
tokens = Tokenizer.texts_to_sequences([sentence])
tokens = pad_sequences(tokens, maxlen = 6000)
prediction = model.predict(np.array(tokens))
pred = np.argmax(prediction)
classes = ['BookRestaurant','GetWeather','PlayMusic','RateBook']
classes[pred]