Update app.py

app.py

@@ -1,147 +1,63 @@
-import io
-import random
-from typing import List, Tuple
+# Import panel and vega datasets
 
-import aiohttp
 import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
+import vega_datasets
 
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
+# Enable Panel extensions
+pn.extension()
+maincol = pn.Column()
+# Define a function to create and return a plot
 
-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",
-}
+df2_approve = df2[df2['choice'] == 'approve']
 
+def create_plot(subgroup, date_range, moving_av_window):
 
-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"
+    # Apply any required transformations to the data in pandas
+    filtered_data = df2_approve[df2_approve['subgroup'] == subgroup]
+    start_date, end_date = pd.to_datetime(date_range[0]), pd.to_datetime(date_range[1])
+    filtered_data = filtered_data[(filtered_data['timestamp'] >= start_date) & (filtered_data['timestamp'] <= end_date)]
+    filtered_data['rate_change'] = filtered_data['rate'].rolling(window=moving_av_window).mean()
+    
+    # Line chart
+    base_line = alt.Chart(filtered_data).mark_line(color='red', size=2).encode(
+        x='timestamp:T',
+        y=alt.Y('rate_change:Q', scale=alt.Scale(domain=[30, 60]))
     )
-    inputs = processor(
-        text=class_items,
-        images=[image],
-        return_tensors="pt",  # pytorch tensors
+    # Scatter plot with individual polls
+    base_scatter = alt.Chart(filtered_data).mark_point(size=2, opacity=0.7, color="gray").encode(
+        x='timestamp:T',
+        y=alt.Y('rate:Q'),
     )
-    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)
+    # Put them togetehr
+    plot = (base_scatter + base_line)
+    # Return the combined chart
     
-        # build the results column
-        results = pn.Column("##### 🎉 Here are the results!", img)
+    return plot
     
-        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",
-)
+# Create the selection widget
+subgroup_widget = pn.widgets.Select(name="Select", options=['Adults', 'All polls', 'Voters'])
 
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
+# Create the slider for the date range
+date_slider = pn.widgets.DateRangeSlider(
+    name='Date Range Slider',
+    start = pd.to_datetime('2021-01-26'), 
+    end = pd.to_datetime('2023-02-14'),
+    value = (pd.to_datetime('2021-01-26'), pd.to_datetime('2023-02-14'))
 )
 
-# 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 moving average window
+window_widget = pn.widgets.IntSlider(name="Moving average window", value=1, start=1, end=100, step=1)
 
-# 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())
+# Bind the widgets to the create_plot function
+bound_plot=pn.bind(create_plot, subgroup=subgroup_widget, date_range=date_slider, moving_av_window=window_widget)
 
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
-)
+# Combine everything in a Panel Column to create an app
+maincol.append(subgroup_widget)
+maincol.append(date_slider)
+maincol.append(window_widget)
+maincol.append(bound_plot)
 
-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
+maincol.servable()