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1 Parent(s): 913507e

Bokeh Native Functionality in Strimlit

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Files changed (1) hide show
  1. app.py +29 -59
app.py CHANGED
@@ -1,64 +1,34 @@
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- import streamlit as st
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- import random
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- import numpy as np
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  from bokeh.plotting import figure
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- from bokeh.embed import components
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  from bokeh.models import ColumnDataSource, HoverTool
 
 
 
 
 
 
 
 
 
 
 
 
 
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- def generate_interactive_plot_html():
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- # Generar datos de ejemplo
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- N = 10
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- x = np.random.rand(N)
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- y = np.random.rand(N)
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-
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- # Dos im谩genes de ejemplo codificadas en base64:
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- red_image = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAF/wK+bNykAAAAAElFTkSuQmCC"
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- blue_image = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mN8/f8/AwAI/AL+fB/9AAAAAElFTkSuQmCC"
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-
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- # Asignar a cada punto una imagen aleatoria y una etiqueta
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- images = [random.choice([red_image, blue_image]) for _ in range(N)]
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- labels = ["subset1" if img == red_image else "subset2" for img in images]
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-
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- # Crear el ColumnDataSource para Bokeh
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- source = ColumnDataSource(data=dict(x=x, y=y, img=images, label=labels))
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-
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- # Crear la figura
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- p = figure(title="Interactive Plot with Hover Images", tools="hover", width=600, height=600)
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- p.scatter('x', 'y', size=15, source=source, fill_color="navy", alpha=0.5)
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-
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- # Configurar el HoverTool para mostrar la etiqueta e imagen
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- hover = p.select_one(HoverTool)
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- hover.tooltips = """
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- <div>
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- <div>
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- <span style="font-size: 12px; font-weight: bold;">Label: @label</span>
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- </div>
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- <div>
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- <img src="@img" alt="Image" style="width:100px;"/>
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- </div>
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- </div>
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- """
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-
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- # Obtener el script y el div del plot
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- script, div = components(p)
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-
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- # Construir el HTML completo, incluyendo los recursos de BokehJS versi贸n 1.4.0
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- html = f"""
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- <html>
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- <head>
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- <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bokehjs@1.4.0/build/css/bokeh.min.css" type="text/css">
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- <script src="https://cdn.jsdelivr.net/npm/bokehjs@1.4.0/build/js/lib/bokeh.min.js"></script>
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- </head>
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- <body>
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- {div}
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- {script}
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- </body>
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- </html>
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- """
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- return html
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- # Genera el HTML del plot interactivo
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- plot_html = generate_interactive_plot_html()
 
 
 
 
 
 
 
 
 
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- # Usa el componente de Streamlit para incrustar el HTML sin sanitizaci贸n
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- st.components.v1.html(plot_html, height=700)
 
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+ # Ejemplo usando st.bokeh_chart en lugar de st.components.v1.html
 
 
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  from bokeh.plotting import figure
 
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  from bokeh.models import ColumnDataSource, HoverTool
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+ import streamlit as st
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+ import numpy as np
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+ import random
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+
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+ # Generar datos
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+ N = 10
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+ x = np.random.rand(N)
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+ y = np.random.rand(N)
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+
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+ red_image = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAF/wK+bNykAAAAAElFTkSuQmCC"
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+ blue_image = "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mN8/f8/AwAI/AL+fB/9AAAAAElFTkSuQmCC"
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+ images = [random.choice([red_image, blue_image]) for _ in range(N)]
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+ labels = ["subset1" if img == red_image else "subset2" for img in images]
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+ source = ColumnDataSource(data=dict(x=x, y=y, img=images, label=labels))
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+ p = figure(title="Interactive Plot with Hover Images", tools="hover", width=600, height=600)
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+ p.scatter('x', 'y', size=15, source=source, fill_color="navy", alpha=0.5)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ hover = p.select_one(HoverTool)
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+ hover.tooltips = """
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+ <div>
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+ <div>
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+ <span style="font-size: 12px; font-weight: bold;">Label: @label</span>
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+ </div>
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+ <div>
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+ <img src="@img" alt="Image" style="width:100px;"/>
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+ </div>
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+ </div>
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+ """
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+ st.bokeh_chart(p)