UI

app.py

@@ -1,59 +1,78 @@
 from pathlib import Path
 from typing import List, Dict, Tuple
 import matplotlib.colors as mpl_colors
+
 import pandas as pd
 import seaborn as sns
 import shinyswatch
-import run
 
 from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
-from transformers import SamModel, SamConfig, SamProcessor
-import torch
 
 sns.set_theme()
 
 www_dir = Path(__file__).parent.resolve() / "www"
 
+df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
+numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
+species: List[str] = df["Species"].unique().tolist()
+species.sort()
+
 app_ui = ui.page_fillable(
     shinyswatch.theme.minty(),
     ui.layout_sidebar(
         ui.sidebar(
-            ui.input_file("image_input", "Upload image: ", multiple=True),
+            # Artwork by @allison_horst
+            ui.input_selectize(
+                "xvar",
+                "X variable",
+                numeric_cols,
+                selected="Bill Length (mm)",
+            ),
+            ui.input_selectize(
+                "yvar",
+                "Y variable",
+                numeric_cols,
+                selected="Bill Depth (mm)",
+            ),
+            
+        ),
+        ui.output_ui("value_boxes"),
+        ui.output_plot("scatter", fill=True),
+        ui.help_text(
+            "Artwork by ",
+            ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
+            class_="text-end",
         ),
-        ui.output_image("image"),
-        ui.output_image("image_output"),
-        ui.output_image("single_patch_prediction"),
-        ui.output_image("single_patch_prob")
     ),
 )
 
 
 def server(input: Inputs, output: Outputs, session: Session):
-    @output
-    @render.image
-    def image():
-        here = Path(__file__).parent
-        if input.image_input():
-            src = input.image_input()[0]['datapath']
-            img = {"src": src, "width": "500px"} 
-            return img
-        return None
-
-    @output
-    @render.image
-    def image_output():
-        here = Path(__file__).parent
-        if input.image_input():
-            src = input.image_input()[0]['datapath']
-            img = {"src": src, "width": "500px"} 
-            x = run.pred(src)
-            print(x)
-            return img
-        return None
+    # @reactive.ui
+
+        return 
+
+
+# "darkorange", "purple", "cyan4"
+colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
+colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
+
+palette: Dict[str, Tuple[float, float, float]] = {
+    "Adelie": colors[0],
+    "Chinstrap": colors[1],
+    "Gentoo": colors[2],
+    "default": sns.color_palette()[0],  # type: ignore
+}
+
+bg_palette = {}
+# Use `sns.set_style("whitegrid")` to help find approx alpha value
+for name, col in palette.items():
+    # Adjusted n_colors until `axe` accessibility did not complain about color contrast
+    bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1])  # type: ignore
 
 
 app = App(
     app_ui,
     server,
     static_assets=str(www_dir),
-)
+)