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synced 2025-10-29 13:06:04 +02:00
69 строки
2.1 KiB
Python
69 строки
2.1 KiB
Python
import ipywidgets
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from IPython.display import display
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import misinformation.faces as faces
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import misinformation.text as text
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class JSONContainer:
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"""Expose a Python dictionary as a JSON document in JupyterLab
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rich display rendering.
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"""
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def __init__(self, data={}):
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self._data = data
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def _repr_json_(self):
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return self._data
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def explore_analysis(mydict, identify="faces"):
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# dictionary mapping the type of analysis to be explored
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identify_dict = {
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"faces": faces.facial_expression_analysis,
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"text-on-image": text.detect_text,
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}
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# create a list containing the image ids for the widget
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# image_paths = [mydict[key]["filename"] for key in mydict.keys()]
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image_ids = [key for key in mydict.keys()]
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# Create an image selector widget
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image_select = ipywidgets.Select(
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options=image_ids, layout=ipywidgets.Layout(width="20%"), rows=20
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)
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# Set up the facial recognition output widget
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output = ipywidgets.Output(layout=ipywidgets.Layout(width="30%"))
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# Set up the image selection and display widget
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image_widget = ipywidgets.Box(
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children=[],
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layout=ipywidgets.Layout(width="50%"),
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)
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# Register the tab switch logic
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def switch(_):
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# Clear existing output
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image_widget.children = ()
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output.clear_output()
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# Create the new content
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image_widget.children = (
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ipywidgets.Image.from_file(mydict[image_select.value]["filename"]),
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)
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# This output widget absorbes print statements that are messing with
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# the widget output and cannot be disabled through the API.
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with faces.NocatchOutput():
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mydict[image_select.value] = identify_dict[identify](
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mydict[image_select.value]
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)
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with output:
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display(JSONContainer(mydict[image_select.value]))
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# Register the handler and trigger it immediately
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image_select.observe(switch, names=("value",), type="change")
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switch(None)
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# Show the combined widget
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return ipywidgets.HBox([image_select, image_widget, output])
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