Update app.py

app.py

@@ -1,147 +1,138 @@
-import io
-import random
-from typing import List, Tuple
-
-import aiohttp
+# Importing the useful libraries and modules
+import pandas as pd
+import numpy as np
 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
-
-
-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),
+import hvplot.pandas
+
+pn.extension("tabulator")
+
+# -------------- Load the data -----------------------------
+data = pd.read_csv("clean_data.csv")
+
+# -------------- Make DataFrame Pipeline Interactive -----------
+idf = data.interactive()
+
+# ------------- CO2 emission over time by continent Plot ------------------
+
+# Year Slider
+year_slider = pn.widgets.IntSlider(name="Year slider", start=1750, end=2020, step=5, value=1850)
+
+# Radio Buttons for CO2 Measures
+yaxis_co2 = pn.widgets.RadioButtonGroup(
+    name="Y axis",
+    options=["co2", "co2_per_capita",],
+    button_type="success"
+)
+
+# Y-axis Options
+yaxis_co2_source = pn.widgets.RadioButtonGroup(
+    name="Y axis",
+    options=["coal_co2", "oil_co2", "gas_co2"],
+    button_type="success"
 )
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
+
+# Continent Options
+continents = ["World", "Asia", "Oceania", "Europe", "Africa", "North America", "South America", "Atarctica"]
+
+# Defining the Pipeline
+co2_pipeline = (
+    idf[
+        (idf.year <= year_slider) &
+        (idf.country.isin(continents))
+    ]
+    .groupby(["country", "year"])[yaxis_co2].mean()
+    .to_frame()
+    .reset_index()
+    .sort_values(by="year")
+    .reset_index(drop=True)
 )
 
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
+# Creating the Plot
+co2_plot = co2_pipeline.hvplot(
+    x = "year", 
+    by = "country", 
+    y=yaxis_co2, 
+    line_width = 2, 
+    title = "CO2 emission by continent")
+
+
+# ------------- Table-CO2 emission over time by continent -----------------
+
+# Creating the Table    
+co2_table = co2_pipeline.pipe(pn.widgets.Tabulator, pagination="remote", page_size = 10, sizing_mode = "stretch_width")
+
+# ------------- CO2 vs GDP scatterplot -----------------------------------
+
+# Creating the Pipeline
+co2_vs_gdp_scatterplot_pipeline = (
+    idf[
+        (idf.year == year_slider) &
+        (~ (idf.country.isin(continents)))
+    ].groupby(["country", "year", "gdp_per_capita"])["co2"].mean()
+    .to_frame().reset_index().sort_values(by="year").reset_index(drop=True)
+)
+
+# Creating the Plot
+co2_vs_gdp_scatterplot = co2_vs_gdp_scatterplot_pipeline.hvplot(x="gdp_per_capita", 
+                                                                y="co2", 
+                                                                by="country", 
+                                                                size=80, 
+                                                                kind="scatter", 
+                                                                alpha=0.7, 
+                                                                legend=False, 
+                                                                height=500, 
+                                                                width=500)
+
+# ------------- Bar chart with CO2 sources by continent ------------------
+
+# Y-axis Content
+yaxis_co2_source = pn.widgets.RadioButtonGroup(
+    name="Y axis",
+    options=["coal_co2", "oil_co2", "gas_co2"],
+    button_type="success"
 )
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
+# Continent Options
+continents_excl_world = ["Asia", "Oceania", "Europe", "Africa", "North America", "South America", "Antarctica"]
+
+# Creating the Pipeline
+co2_source_bar_pipeline = (
+    idf[
+        (idf.year == year_slider) & 
+        (idf.country.isin(continents_excl_world))
+    ].groupby(["year", "country"])[yaxis_co2_source].sum()
+    .to_frame()
+    .reset_index()
+    .sort_values(by="year")
+    .reset_index(drop=True)
 )
 
-# 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 Plot
+co2_source_bar_plot = co2_source_bar_pipeline.hvplot(kind="bar", 
+                                                     x="country", 
+                                                     y=yaxis_co2_source, 
+                                                     title="CO2 source by continent")
+
+# ------------------------------ Creating the Dashboard --------------------------------
+
+# Layout using Template
+template = pn.template.FastListTemplate(
+    title='World CO2 emission dashboard', 
+    sidebar=[pn.pane.Markdown("# CO2 Emissions and Climate Change"), 
+             pn.pane.Markdown("#### Carbon dioxide emissions are the primary driver of global climate change. It’s widely recognised that to avoid the worst impacts of climate change, the world needs to urgently reduce emissions. But, how this responsibility is shared between regions, countries, and individuals has been an endless point of contention in international discussions."), 
+             pn.pane.PNG('climate_day.png', sizing_mode='scale_both'),
+             pn.pane.Markdown("## Settings"),   
+             year_slider],
+    main=[pn.Row(pn.Column(yaxis_co2, 
+                           co2_plot.panel(width=700), margin=(0,25)), 
+                 co2_table.panel(width=500)), 
+          pn.Row(pn.Column(co2_vs_gdp_scatterplot.panel(width=600), margin=(0,25)), 
+                 pn.Column(yaxis_co2_source, co2_source_bar_plot.panel(width=600)))],
+    accent_base_color="#88d8b0",
+    header_background="#88d8b0",
 )
 
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+# ----------------- Run the Dashboard ------------------------
+
+if __name__ == "__main__":
+    template.show()