Replace demo app for Panel with beginnings of diffannotator demo

Currently the demo does notihing but add a widget and a string
to MaterialTheme based application.

Changed requirements.txt, commented out unused imports.

app.py

@@ -1,147 +1,49 @@
-import io
-import random
-from typing import List, Tuple
+#import datetime
+import json
+#import math
 
-import aiohttp
-import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
+# data analysis
+#import numpy as np
+#import pandas as pd
 
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
+# dashboard
+import panel as pn
 
-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",
-}
+# plotting
+#import seaborn as sns
+#from matplotlib.figure import Figure
 
 
-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"]
+# configure panel
+pn.extension('ipywidgets', design="material", sizing_mode='fixed')
 
+# configure app
+DATA_DIR = 'data'
+JSON_FILE = 'tensorflow.timeline.purpose-to-type.json'
 
+# get data, cached
 @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"
-    )
-    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)
-    
-        # 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,
-)
+def get_timeline_data():
+    with open(f'{DATA_DIR}/{JSON_FILE}', mode='r') as json_fp:
+        return json.load(json_fp)
 
-# 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())
+# TODO: include the rest of the app
 
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
+# widgets
+repos_widget = pn.widgets.Select(
+    name="repository",
+    value="tensorflow", options=["tensorflow"],
+    disabled=True
 )
 
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+# the application
+pn.template.MaterialTemplate(
+    site="diffannotator",
+    title="Panel Demo",
+    sidebar=[
+        repos_widget,  # disabled, and UNBOUND!
+    ],
+    main=[
+        pn.pane.Str("Plots would be shown here")
+    ],
+).servable()

requirements.txt

@@ -1,6 +1,11 @@
+# Dashboard tools
 panel
-jupyter
-transformers
+ipywidgets         # for matplotlib
+ipywidgets_bokeh   # for matplotlib, too
+#pyviz-comms   # https://panel.holoviz.org/how_to/notebook/notebook.html
+#watchfiles   # not needed when running `panel serve` without `--dev` option
+# Packages used by notebooks
+matplotlib
+seaborn
 numpy
-torch
-aiohttp
+pandas