Update app.py

app.py

@@ -7,141 +7,75 @@ 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
+# imports we will use
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
 
+import altair as alt
+import pandas as pd
+from vega_datasets import data as vega_data
+import panel as pn
+import datetime as dt
+
+#load data 
+df2=pd.read_csv("https://raw.githubusercontent.com/dallascard/SI649_public/main/altair_hw3/approval_topline.csv")
+df2['timestamp']=pd.to_datetime(df2['timestamp'])
+df2=pd.melt(df2, id_vars=['president', 'subgroup', 'timestamp'], value_vars=['approve','disapprove']).rename(columns={'variable':'choice', 'value':'rate'})
+
+# Enable Panel extensions
+pn.extension(design ='bootstrap')
+pn.extension('vega')
+
+# Define a function to create and return a plot
+df2 = df2[df2['choice'] =='approve']
+def create_plot(subgroup, date_range, moving_av_window):
+
+    # Apply any required transformations to the data in pandas
+    filtered = df2[df2['subgroup'] == subgroup]
+    filtered = filtered[(filtered['timestamp'].dt.date >= date_range[0]) & (filtered['timestamp'].dt.date <= date_range[1])]
+    filtered['smoothed'] = filtered['rate'].rolling(window=moving_av_window, min_periods=1).mean().shift(-1)
+
+    # Line chart
+    line = alt.Chart(filtered).mark_line(color='red').encode(
+        x=alt.X('timestamp:T'),
+        y = alt.Y('smoothed').scale(domain=(30, 60))
+    )
 
-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()))
+    # Scatter plot with individual polls
+    scatter = alt.Chart(filtered).mark_point(filled=True, color='gray', size = 6).encode(
+        x = alt.X('timestamp:T'),
+        y = alt.Y('rate').scale(domain=(30, 60)),
+    )
 
+    # Put them together
+    plot = line + scatter
 
-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)
+    # Return the combined chart
+    return plot
     
-        # 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,
-)
+# Create the selection widget
+select = pn.widgets.Select(name='Select', options=['All polls','Adults','Voters'])
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
+# Create the slider for the date range
+# Reference https://panel.holoviz.org/reference/widgets/DateRangeSlider.html
+date_range_slider = pn.widgets.DateRangeSlider(
+    name='Date Range Slider',
+    start=dt.datetime(2021, 1, 26).date(), end=dt.datetime(2023, 2, 14).date(),
+    value=(dt.datetime(2021, 1, 26).date(), dt.datetime(2023, 2, 14).date()),
+    step=2
 )
 
-# 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,
-)
+# Create the slider for the moving average window
+# Reference Int Slider: https://panel.holoviz.org/reference/widgets/IntSlider.html
+win_slider = pn.widgets.IntSlider(name='Moving Average Window', start=1, end=80, step=1, value=20)
+
+# Bind the widgets to the create_plot function
+bplot = pn.bind(create_plot, subgroup=select, 
+                date_range=date_range_slider,moving_av_window = win_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)
+# Combine everything in a Panel Column to create an app
+# Reference: https://panel.holoviz.org/how_to/streamlit_migration/interactivity.html
+viz4 = pn.Column(bplot,select,date_range_slider,win_slider)
+# set the app to be servable
+viz4.show()