Update app.py

app.py

@@ -1,147 +1,107 @@
-import io
-import random
-from typing import List, Tuple
-
-import aiohttp
+import vega_datasets
 import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
+import pandas as pd
+import altair as alt
+from vega_datasets import data
+
 
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
+pn.extension(design='bootstrap')
 
-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",
-}
+# we want to use vega, tell Panel to load up what we need
+pn.extension('vega')
 
 
-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"]
+template = pn.template.BootstrapTemplate(
+    title='SI649 Lab7 Viz4',
+)
 
 
-@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
+# Import panel and vega datasets
+# load data
+df1 = pd.read_csv(
+    "https://raw.githubusercontent.com/dallascard/SI649_public/main/altair_hw3/approval_polllist.csv")
+df2 = pd.read_csv(
+    "https://raw.githubusercontent.com/dallascard/SI649_public/main/altair_hw3/approval_topline.csv")
 
+# Enable Panel extensions
+pn.extension()
 
-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()))
+# Define a function to create and return a plot
 
 
-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
+def create_plot(subgroup, date_range, moving_av_window):
+
+    # Apply any required transformations to the data in pandas
+    # print(pd.to_datetime(df2['timestamp']), type(date_range[0]))
+
+    # df2_filtered = df2[(df2['subgroup'] == subgroup) & (df2['timestamp'] >= date_range[0]) & (df2['timestamp'] <= date_range[1])]
+    df2_filtered = df2[(df2['subgroup'] == subgroup) & (df2['timestamp'] >= pd.Timestamp(
+        date_range[0])) & (df2['timestamp'] <= pd.Timestamp(date_range[1]))]
+
+    # Calculate the moving average
+    # df2_filtered['moving_rate'] = df2_filtered['rate'].rolling(window=moving_av_window).mean()
+    df2_filtered['moving_rate'] = df2_filtered['rate'].rolling(
+        window=moving_av_window).mean().shift(-moving_av_window//2)
+    # df2_filtered.loc[:, 'moving_rate'] = df2_filtered['rate'].rolling(window=moving_av_window).mean()
+#
+    # Line chart for moving average with df2_filtered
+    line = alt.Chart(df2_filtered).mark_line().encode(
+        x='timestamp:T',
+        y='moving_rate:Q',
+        color=alt.value('red')
+    ).transform_filter(
+        # keep only approved polls
+        alt.FieldOneOfPredicate(field='choice', oneOf=['approve'])
     )
-    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)
-    
-        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,
-)
+    # Scatter plot with individual polls
+    scatter = alt.Chart(df2_filtered).mark_circle().encode(
+        x='timestamp:T',
+        y='rate:Q',
+        color=alt.value('gray'),
+        opacity=alt.value(0.7),
+        tooltip=['timestamp:T', 'rate:Q'],
+        size=alt.value(10)
+    ).transform_filter(
+        # keep only approved polls
+        alt.FieldOneOfPredicate(field='choice', oneOf=['approve'])
+    )
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
-)
+    # Put them together
+    plot = scatter + line
+    # change y axis range of the plot to 30 to 60
 
-# 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,
+    # plot = plot + line
+    plot = plot.properties(title="Approval Ratings for Joe Biden")
+    plot = plot.encode(
+        y=alt.Y('rate:Q', scale=alt.Scale(domain=[30, 60]))
+    )
+    # Return the combined chart
+    return plot
+
+
+# Create the selection widget using subgroup column
+selection_subgroup = pn.widgets.Select(
+    name='Subgroup', options=df2.subgroup.unique().tolist())
+
+# Create the slider for the date range
+# df2['timestamp'] = pd.to_datetime(df2['timestamp'])
+date_range_slider = pn.widgets.DateRangeSlider(name='Date Range', start=df2.timestamp.min(
+), end=df2.timestamp.max(), value=(df2.timestamp.min(), df2.timestamp.max()))
+
+# Create the slider for the moving average window
+moving_av_window_slider = pn.widgets.IntSlider(
+    name='Moving Average Window', start=1, end=30, step=1, value=1)
+# Bind the widgets to the create_plot function
+create_plot_wgt = pn.bind(create_plot, subgroup=selection_subgroup,
+                          date_range=date_range_slider, moving_av_window=moving_av_window_slider)
+
+# Combine everything in a Panel Column to create an app
+main_col = pn.Column(
+    create_plot_wgt,
+    selection_subgroup,
+    date_range_slider,
+    moving_av_window_slider,
 )
-
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+# set the app to be servable
+main_col.servable()