Refactor app.py and update requirements.txt

app.py

@@ -1,147 +1,53 @@
-import io
-import random
-from typing import List, Tuple
+# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
-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",
-}
+# %% auto 0
+__all__ = ['csv_file', 'data', 'variable_widget', 'window_widget', 'sigma_widget', 'bound_plot', 'first_app', 'transform_data',
+           'create_plot']
 
+# %% app.ipynb 1
+import panel as pn
+import hvplot.pandas
+import pandas as pd
+import numpy as np
 
-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
+# %% app.ipynb 2
+pn.extension(design='material')
 
+csv_file = ("https://raw.githubusercontent.com/holoviz/panel/main/examples/assets/occupancy.csv")
+data = pd.read_csv(csv_file, parse_dates=["date"], index_col="date")
 
-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()))
+data.tail()
 
+# %% app.ipynb 3
+def transform_data(variable, window, sigma):
+    ''' Calculates the rolling average and the outliers '''
+    avg = data[variable].rolling(window=window).mean()
+    residual = data[variable] - avg
+    std = residual.rolling(window=window).std()
+    outliers = np.abs(residual) > std * sigma
+    return avg, avg[outliers]
 
-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(variable="Temperature", window=30, sigma=10):
+    ''' Plots the rolling average and the outliers '''
+    avg, highlight = transform_data(variable, window, sigma)
+    return avg.hvplot(height=300, width=400, legend=False) * highlight.hvplot.scatter(
+        color="orange", padding=0.1, legend=False
     )
-    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,
-)
+# %% app.ipynb 4
+create_plot(variable='Temperature', window=20, sigma=10)
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
-)
+# %% app.ipynb 5
+variable_widget = pn.widgets.Select(name="variable", value="Temperature", options=list(data.columns))
+window_widget = pn.widgets.IntSlider(name="window", value=30, start=1, end=60)
+sigma_widget = pn.widgets.IntSlider(name="sigma", value=10, start=0, end=20)
 
-# 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())
+# %% app.ipynb 6
+bound_plot = pn.bind(create_plot, variable=variable_widget, window=window_widget, sigma=sigma_widget)
 
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
-)
+# %% app.ipynb 7
+first_app = pn.Column(variable_widget, window_widget, sigma_widget, 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)
+first_app.servable()

requirements.txt

@@ -2,5 +2,4 @@ panel
 jupyter
 transformers
 numpy
-torch
 aiohttp