replace

app.py

@@ -1,147 +1,60 @@
-import io
-import random
-from typing import List, Tuple
 
-import aiohttp
+from vega_datasets import data
+from scipy import stats
+from bokeh.plotting import figure
+from bokeh.models import ColumnDataSource
 import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
+import numpy as np
+
+#import io
+#import random
+#from typing import List, Tuple
+
+#import aiohttp
+#import panel as pn
+#from PIL import Image
+#from transformers import CLIPModel, CLIPProcessor
 
 pn.extension(design="bootstrap", sizing_mode="stretch_width")
 
-ICON_URLS = {
-    "brand-github": "https://github.com/holoviz/panel",
-    "brand-twitter": "https://twitter.com/Panel_Org",
-    "brand-linkedin": "https://www.linkedin.com/company/panel-org",
-    "message-circle": "https://discourse.holoviz.org/",
-    "brand-discord": "https://discord.gg/AXRHnJU6sP",
-}
-
-
-async def random_url(_):
-    pet = random.choice(["cat", "dog"])
-    api_url = f"https://api.the{pet}api.com/v1/images/search"
-    async with aiohttp.ClientSession() as session:
-        async with session.get(api_url) as resp:
-            return (await resp.json())[0]["url"]
-
-
-@pn.cache
-def load_processor_model(
-    processor_name: str, model_name: str
-) -> Tuple[CLIPProcessor, CLIPModel]:
-    processor = CLIPProcessor.from_pretrained(processor_name)
-    model = CLIPModel.from_pretrained(model_name)
-    return processor, model
-
-
-async def open_image_url(image_url: str) -> Image:
-    async with aiohttp.ClientSession() as session:
-        async with session.get(image_url) as resp:
-            return Image.open(io.BytesIO(await resp.read()))
-
-
-def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
-    processor, model = load_processor_model(
-        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
-    )
-    inputs = processor(
-        text=class_items,
-        images=[image],
-        return_tensors="pt",  # pytorch tensors
-    )
-    outputs = model(**inputs)
-    logits_per_image = outputs.logits_per_image
-    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
-    return class_likelihoods[0]
-
-
-async def process_inputs(class_names: List[str], image_url: str):
-    """
-    High level function that takes in the user inputs and returns the
-    classification results as panel objects.
-    """
-    try:
-        main.disabled = True
-        if not image_url:
-            yield "##### ⚠️ Provide an image URL"
-            return
-    
-        yield "##### ⚙ Fetching image and running model..."
-        try:
-            pil_img = await open_image_url(image_url)
-            img = pn.pane.Image(pil_img, height=400, align="center")
-        except Exception as e:
-            yield f"##### 😔 Something went wrong, please try a different URL!"
-            return
-    
-        class_items = class_names.split(",")
-        class_likelihoods = get_similarity_scores(class_items, pil_img)
-    
-        # build the results column
-        results = pn.Column("##### 🎉 Here are the results!", img)
+
+source = data.seattle_weather()
+
+pn.extension()
+
+temp = sorted(source['temp_max'].values)
+
+max_bins = int(np.ceil(max(temp))+ 1 - np.floor(min(temp)))
+
+def create_plot(bandwidth=1.0, bins=max_bins):
+    plot = figure(width=300, height=300, toolbar_location=None)
     
-        for class_item, class_likelihood in zip(class_items, class_likelihoods):
-            row_label = pn.widgets.StaticText(
-                name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
-            )
-            row_bar = pn.indicators.Progress(
-                value=int(class_likelihood * 100),
-                sizing_mode="stretch_width",
-                bar_color="secondary",
-                margin=(0, 10),
-                design=pn.theme.Material,
-            )
-            results.append(pn.Column(row_label, row_bar))
-        yield results
-    finally:
-        main.disabled = False
-
-
-# create widgets
-randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
-
-image_url = pn.widgets.TextInput(
-    name="Image URL to classify",
-    value=pn.bind(random_url, randomize_url),
-)
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
-)
-
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
-)
-
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
-)
-
-# add footer
-footer_row = pn.Row(pn.Spacer(), align="center")
-for icon, url in ICON_URLS.items():
-    href_button = pn.widgets.Button(icon=icon, width=35, height=35)
-    href_button.js_on_click(code=f"window.open('{url}')")
-    footer_row.append(href_button)
-footer_row.append(pn.Spacer())
-
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
-)
-
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+    # Histogram
+    #bins = np.arange(np.floor(min(temp)), np.ceil(max(temp))+1, 1)
+    hist, edges = np.histogram(temp, bins=bins)
+
+    hist = hist / hist.sum()
+
+    quad = plot.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
+             fill_color="grey", line_color="white", alpha=0.5)
+
+    # density
+    kernel = stats.gaussian_kde(temp, bw_method=bandwidth)
+    x = np.linspace(min(temp), max(temp), 100)
+    y = kernel(x)
+    col_source = ColumnDataSource(data=dict(x=x, y=y))
+    line = plot.line('x', 'y', source=col_source, alpha=1.0)
+    return plot
+
+
+
+bw_widget = pn.widgets.FloatSlider(name="Bandwidth", value=1.0, start=0.03, end=2.0, step=0.02)
+bins_widget = pn.widgets.IntSlider(name="Number of Bins", value=max_bins, start=1, end=max_bins)
+
+bound_plot = pn.bind(create_plot, bandwidth=bw_widget, bins=bins_widget)
+
+first_app = pn.Column(bw_widget, bins_widget, bound_plot)
+
+first_app.servable()
+
+