ccm commited on
Commit
6e082a5
1 Parent(s): ed154bd

Generate and display endpoints

Browse files
Files changed (1) hide show
  1. app.py +18 -1
app.py CHANGED
@@ -242,9 +242,10 @@ density_options = ["{:.2f}".format(x) for x in numpy.linspace(0.1, 1, 10)]
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  thickness_options = [str(int(x)) for x in numpy.linspace(0, 10, 11)]
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  interpolation_options = [str(int(x)) for x in [3, 5, 10, 20]]
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  def interpolate(t1, t2, d1, d2, th1, th2, steps):
 
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  decoder_model_boxes = tensorflow.keras.models.load_model('data/decoder_model_boxes', compile=False)
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- # # compile=False ignores a warning from tensorflow, can be removed to see warning
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  # # import the encoder model architecture
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  json_file_loaded = open('data/model.json', 'r')
@@ -293,16 +294,32 @@ def interpolate(t1, t2, d1, d2, th1, th2, steps):
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  return transition_region
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  with gradio.Blocks() as demo:
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  with gradio.Row():
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  with gradio.Column():
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  t1 = gradio.Dropdown(endpoint_types, label="Type 1", value=random.choice(endpoint_types))
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  d1 = gradio.Dropdown(density_options, label="Density 1", value=random.choice(density_options))
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  th1 = gradio.Dropdown(thickness_options, label="Thickness 1", value=random.choice(thickness_options))
 
 
 
 
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  with gradio.Column():
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  t2 = gradio.Dropdown(endpoint_types, label="Type 2", value=random.choice(endpoint_types))
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  d2 = gradio.Dropdown(density_options, label="Density 2", value=random.choice(density_options))
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  th2 = gradio.Dropdown(thickness_options, label="Thickness 2", value=random.choice(thickness_options))
 
 
 
 
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  steps = gradio.Dropdown(interpolation_options, label="Interpolation Length", value=random.choice(interpolation_options))
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  btn = gradio.Button("Run")
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  img = gradio.Image(label="Transition")
 
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  thickness_options = [str(int(x)) for x in numpy.linspace(0, 10, 11)]
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  interpolation_options = [str(int(x)) for x in [3, 5, 10, 20]]
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+
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  def interpolate(t1, t2, d1, d2, th1, th2, steps):
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+ # Load the decoder model
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  decoder_model_boxes = tensorflow.keras.models.load_model('data/decoder_model_boxes', compile=False)
 
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  # # import the encoder model architecture
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  json_file_loaded = open('data/model.json', 'r')
 
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  return transition_region
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+ def generate_unit_cell(t, d, th):
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+ number_1 = globals()[t](int(th), float(d), 28)
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+
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+ # resize the array to match the prediction size requirement
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+ number_1_expand = np.expand_dims(np.expand_dims(number_1, axis=2), axis=0)
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+
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+ return number_1_expand[0, :, :, 0]
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+
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  with gradio.Blocks() as demo:
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  with gradio.Row():
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  with gradio.Column():
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  t1 = gradio.Dropdown(endpoint_types, label="Type 1", value=random.choice(endpoint_types))
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  d1 = gradio.Dropdown(density_options, label="Density 1", value=random.choice(density_options))
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  th1 = gradio.Dropdown(thickness_options, label="Thickness 1", value=random.choice(thickness_options))
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+ img1 = gradio.Image(label="Endpoint 1")
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+ t1.change(fn=generate_unit_cell, inputs=[t1, d1, th1], outputs=[img1])
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+ d1.change(fn=generate_unit_cell, inputs=[t1, d1, th1], outputs=[img1])
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+ th1.change(fn=generate_unit_cell, inputs=[t1, d1, th1], outputs=[img1])
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  with gradio.Column():
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  t2 = gradio.Dropdown(endpoint_types, label="Type 2", value=random.choice(endpoint_types))
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  d2 = gradio.Dropdown(density_options, label="Density 2", value=random.choice(density_options))
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  th2 = gradio.Dropdown(thickness_options, label="Thickness 2", value=random.choice(thickness_options))
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+ img2 = gradio.Image(label="Endpoint 2")
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+ t2.change(fn=generate_unit_cell, inputs=[t2, d2, th2], outputs=[img1])
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+ d2.change(fn=generate_unit_cell, inputs=[t2, d2, th2], outputs=[img1])
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+ th2.change(fn=generate_unit_cell, inputs=[t2, d2, th2], outputs=[img1])
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  steps = gradio.Dropdown(interpolation_options, label="Interpolation Length", value=random.choice(interpolation_options))
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  btn = gradio.Button("Run")
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  img = gradio.Image(label="Transition")