Update app.py

app.py

@@ -1,147 +1,175 @@
-import io
-import random
-from typing import List, Tuple
+# -*- coding: utf-8 -*-
+"""Comparable_Win_Calculator.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",
-}
-
-
-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),
-)
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
-)
+Automatically generated by Colaboratory.
 
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
-)
+Original file is located at
+    https://colab.research.google.com/drive/12e5diH3JZC7rGY28DWuH6ysWHuyfEwNx
+"""
 
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
+import pandas as pd
+import numpy as np
+import panel as pn
+from pyecharts.charts.basic_charts.bar import Bar
+from pyecharts import options as opts
+import hvplot.pandas
+import math
+pn.extension('mathjax')
+pn.extension('tabulator')
+pn.extension('echarts')
+
+df_1 = pd.read_csv("primary.csv")
+df_2 = pd.read_csv("general.csv")
+
+# make dataframe pipline interactive
+idf_1 = df_1.interactive()
+idf_2 = df_2.interactive()
+
+# Using primary
+HD_list = [i for i in range(1,41)]
+
+"""# Create Table1"""
+
+def first_table(HD_selector,turnout_level):
+    pri_conser = df_1.loc[(df_1['HD'] == str(HD_selector)) & (df_1['Primary Voting'] == turnout_level)].Conservatives
+    pri_right = df_1.loc[(df_1['HD'] == str(HD_selector)) & (df_1['Primary Voting'] == turnout_level)].Right_Leaning
+    pri_left = df_1.loc[(df_1['HD'] == str(HD_selector)) & (df_1['Primary Voting'] == turnout_level)].Left_Leaning
+    pri_liberal = df_1.loc[(df_1['HD'] == str(HD_selector)) & (df_1['Primary Voting'] == turnout_level)].Liberals
+
+    gen_conser = df_2.loc[(df_2['HD'] == str(HD_selector)) & (df_2['General Voting'] == turnout_level)].Conservatives
+    gen_right = df_2.loc[(df_2['HD'] == str(HD_selector)) & (df_2['General Voting'] == turnout_level)].Right_Leaning
+    gen_left = df_2.loc[(df_2['HD'] == str(HD_selector)) & (df_2['General Voting'] == turnout_level)].Left_Leaning
+    gen_liberal = df_2.loc[(df_2['HD'] == str(HD_selector)) & (df_2['General Voting'] == turnout_level)].Liberals
+
+    table1_df = pd.DataFrame({
+        'Primary': [pri_conser, pri_right,pri_left,pri_liberal],
+        'Global': [gen_conser, gen_right,gen_left,gen_liberal],
+    }, index=['0-20 (Conservatives)','20-40 (Right Leaning Moderates)','40-70 (Left Leaning Moderates)','70-100 (Liberals)'])
+    df_widget = pn.widgets.Tabulator(table1_df,widths=130,theme_classes=['thead-dark', 'table-sm'])
+    return df_widget
+
+"""# Plot Graph1"""
+
+
+# Plot dashboard_1
+def dash_board_1(HD_selector_1,turnout_level_1,HD_selector_2,turnout_level_2,index):
+    pri_conser_1 = df_1.loc[(df_1['HD'] == str(HD_selector_1)) & (df_1['Primary Voting'] == turnout_level_1)].Conservatives
+    #print(type(pri_conser_1))
+    # print(type(pri_conser_1.item()))
+    pri_right_1 = (df_1.loc[(df_1['HD'] == str(HD_selector_1)) & (df_1['Primary Voting'] == turnout_level_1)].Right_Leaning)
+    # print(type(pri_right_1))
+    # print(type(pri_right_1.item()))
+    pri_left_1 = df_1.loc[(df_1['HD'] == str(HD_selector_1)) & (df_1['Primary Voting'] == turnout_level_1)].Left_Leaning
+    pri_liberal_1 = df_1.loc[(df_1['HD'] == str(HD_selector_1)) & (df_1['Primary Voting'] == turnout_level_1)].Liberals
+    total_1 = pri_conser_1 + pri_right_1 + pri_left_1+ pri_liberal_1
+    conser_percen_1 = round(100*pri_conser_1/total_1,2)
+    right_percen_1 = round(100*pri_right_1/total_1,2)
+    left_percen_1 = round(100*pri_left_1/total_1,2)
+    liberal_percen_1 = round(100*pri_liberal_1/total_1,2)
+    pri_conser_2 = (df_1.loc[(df_1['HD'] == str(HD_selector_2)) & (df_1['Primary Voting'] == turnout_level_2)].Conservatives)
+    pri_right_2 = (df_1.loc[(df_1['HD'] == str(HD_selector_2)) & (df_1['Primary Voting'] == turnout_level_2)].Right_Leaning)
+    pri_left_2 = (df_1.loc[(df_1['HD'] == str(HD_selector_2)) & (df_1['Primary Voting'] == turnout_level_2)].Left_Leaning)
+    pri_liberal_2 = (df_1.loc[(df_1['HD'] == str(HD_selector_2)) & (df_1['Primary Voting'] == turnout_level_2)].Liberals)
+    total_2 = pri_conser_2 + pri_right_2 + pri_left_2 + pri_liberal_2
+    conser_percen_2 = round(100*pri_conser_2/total_2,2)
+    right_percen_2 = round(100*pri_right_2/total_2,2)
+    left_percen_2 = round(100*pri_left_2/total_2,2)
+    liberal_percen_2 = round(100*pri_liberal_2/total_2,2)
+    center= ['50%','60%'], # Percentage of graph's distance to the left and top
+    #print([conser_percen_1,right_percen_1,left_percen_1,liberal_percen_1])
+    #print([pri_conser_1.item(), pri_right_1.item(),pri_left_1.item(),pri_liberal_1.item()])
+    bar_1= (Bar()
+              .add_xaxis(["(0-20)Conservative","(20-40)Right Leaning Moderates","(40-70)left Leaning Moderates","70-100 (Liberals)"])
+              .add_yaxis("HD"+str(HD_selector_1), [conser_percen_1.item(),right_percen_1.item(),left_percen_1.item(),liberal_percen_1.item()])
+              .add_yaxis("HD"+str(HD_selector_2), [conser_percen_2.item(),right_percen_2.item(),left_percen_2.item(),liberal_percen_2.item()])
+              .set_global_opts(title_opts=opts.TitleOpts(title="General Election District Comparison (%)",
+                                                          pos_left="center", pos_top="top"),
+                              legend_opts = opts.LegendOpts(type_="scroll", pos_left="70%", orient='vertical', pos_top="10%"),
+                               )
+         )
+    bar_2= (Bar()
+          .add_xaxis(["(0-20)Conservative","(20-40)Right Leaning Moderates","(40-70)left Leaning Moderates","70-100 (Liberals)"])
+          .add_yaxis("HD"+str(HD_selector_1), [pri_conser_1.item(), pri_right_1.item(),pri_left_1.item(),pri_liberal_1.item()])
+          .add_yaxis("HD"+str(HD_selector_2), [pri_conser_2.item(),pri_right_2.item(),pri_left_2.item(),pri_liberal_2.item()])
+          .set_global_opts(title_opts=opts.TitleOpts(title="General Election District Comparison (Raw Numbers)",
+                                                      pos_left="center", pos_top="top"),
+                          legend_opts = opts.LegendOpts(type_="scroll", pos_left="70%", orient='vertical', pos_top="10%"),
+                            )
+      )
+    bar_1 = pn.pane.ECharts(bar_1, width=1000, height=500)
+    bar_2 = pn.pane.ECharts(bar_2, width=1000, height=500)
+    bar_list = [bar_1,bar_2]
+    return bar_list[index]
+
+
+"""# Declare SideBar Elements"""
+
+# House District Selector Widget 1
+HD_selector_1 = pn.widgets.Select(name='HD Selector', options=HD_list)
+# turnout_level selection 1
+turnout_level_1 = pn.widgets.Select(name='Turnout Level', options=['High Prop', 'Low Prop'])
+
+# House District Selector Widget 2
+HD_selector_2 = pn.widgets.Select(name='HD Selector', options=HD_list)
+# turnout_level selection 2
+turnout_level_2 = pn.widgets.Select(name='Turnout Level', options=['High Prop', 'Low Prop'])
+
+title = """
+#  Comparable Win Calculator
+"""
+
+explaination_1 = """
+### Enter a house distirct and specify the turnout level.
+### The tables below will show the ideological composition of the low/high propensity electorate in your district in both the primary and the general.
+### Underneath you will see a graph conveying the same information in terms of percentages.
+"""
+
+widget_name_1 = """
+### Choose first House District
+"""
+
+widget_name_2 = """
+### High prop or Low prop
+"""
+
+widget_name_3 = """
+### Choose second House District
+"""
+
+widget_name_4 = """
+### High prop or Low prop
+"""
+
+sidebar = pn.layout.WidgetBox(
+    pn.pane.Markdown(title, margin=(0, 10)),
+    pn.pane.Markdown(widget_name_1, margin=(0, 10)),
+    HD_selector_1,
+    pn.pane.Markdown(widget_name_2, margin=(0, 10)),
+    turnout_level_1,
+    pn.pane.Markdown(widget_name_3, margin=(0, 10)),
+    HD_selector_2,
+    pn.pane.Markdown(widget_name_4, margin=(0, 10)),
+    turnout_level_2,
+    max_width=350,
+    sizing_mode='stretch_width'
 )
 
-# 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,
+"""# Main Panel"""
+
+main =  pn.Row(
+          pn.Column(
+              pn.pane.Markdown(explaination_1, margin=(0, 40)),
+              pn.layout.Divider(margin=(-10, 0, 0, 0),),
+              pn.Row(
+                  pn.bind(first_table,HD_selector_1,turnout_level_1),
+                  pn.bind(first_table,HD_selector_2,turnout_level_2),
+                ),
+              pn.Row(
+                pn.bind(dash_board_1,HD_selector_1,turnout_level_1,HD_selector_2,turnout_level_2,0),
+                pn.bind(dash_board_1,HD_selector_1,turnout_level_1,HD_selector_2,turnout_level_2,1),
+              ),
+          ),
+
 )
+overall = pn.Row(sidebar, main).servable()
 
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)