Paste Question 4 for Lab 7

Deploy an interactive chart for the moving average of the approval rate for Joe Biden

app.py

@@ -1,147 +1,66 @@
-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",
-}
-
+import vega_datasets
 
-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
+# Enable Panel extensions
+pn.extension(design='bootstrap')
+pn.extension('vega')
+template = pn.template.BootstrapTemplate(
+    title='Nan-Hsin Lin',
+)
 
+# Define a function to create and return a plot
 
-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 create_plot(subgroup, date_range, moving_av_window):
 
+    # Apply any required transformations to the data in pandas
+    df3 = df2[df2['choice'] == 'approve'].copy()
+    df3 = df3[df3['subgroup'] == subgroup]
+    df3['smoothed_rate'] = df3['rate'].rolling(moving_av_window).mean().shift(-int(moving_av_window/2))
+    start_date, end_date = date_range
+    df3 = df3[(df3['timestamp'] >= np.datetime64(start_date)) & (df3['timestamp'] <= np.datetime64(end_date))]
 
-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"
+    # Line chart
+    rate_line = alt.Chart(df3).mark_line(strokeWidth=2, color='red').encode(
+        x=alt.X('timestamp:T', axis=alt.Axis(title=None)),
+        y=alt.Y('average(smoothed_rate):Q', axis=alt.Axis(title='move_avg'), scale=alt.Scale(domain=[30, 60]))
     )
-    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]
 
+    # Scatter plot with individual polls
+    rate_scatter = alt.Chart(df3).mark_point(color='grey', size=2, opacity=0.7).encode(
+        x=alt.X('timestamp:T', axis=alt.Axis(title=None)),
+        y=alt.Y('average(rate):Q', axis=alt.Axis(title='approve'), scale=alt.Scale(domain=[30, 60])), 
+    )
 
-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
+    # Put them together
+    plot = alt.layer(rate_line, rate_scatter).configure_view(strokeWidth=0)
     
-        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)
-    
-        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)
+    # Return the combined chart
+    return plot
+
+# Create the selection widget
+select = pn.widgets.Select(name='Select', options=df2['subgroup'].unique().tolist())
+
+# Create the slider for the date range
+dateSlider = pn.widgets.DateRangeSlider(name='Date Range Slider', 
+                                    start=df2['timestamp'].min(), 
+                                    end=df2['timestamp'].max(),
+                                    value=(df2['timestamp'].min(), df2['timestamp'].max()))
+
+# Create the slider for the moving average window
+avgSlider = pn.widgets.IntSlider(name='Moving average window', start=1, end=100, value=1)
+
+# Bind the widgets to the create_plot function
+plot_widgets = pn.Row(pn.bind(create_plot, select, dateSlider, avgSlider))
+
+# Combine everything in a Panel Column to create an app
+maincol = pn.Column()
+maincol.append("# SI649 Lab07")
+maincol.append("Hello! This is **Nan**. I love information visualization! Email me: [nanhsin@umich.edu](mailto:nanhsin@umich.edu)")
+maincol.append(plot_widgets)
+maincol.append(select)
+maincol.append(dateSlider)
+maincol.append(avgSlider)
+template.main.append(maincol)
+
+# set the app to be servable
+template.servable(title="Nan-Hsin Lin")