Update app.py

app.py

@@ -1,26 +1,157 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-def chatbot_response(user_message):
-    # Load the pre-trained model and tokenizer
-    model_name = "your_pretrained_model_name"  # Replace with the name of the pre-trained model you want to use
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-
-    # Tokenize the user's message and generate the response
-    inputs = tokenizer.encode("User: " + user_message, return_tensors="pt")
-    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    return response
-
-if __name__ == '__main__':
-    print("Chatbot: Hello! I'm your chatbot. Type 'exit' to end the conversation.")
-    
-    while True:
-        user_input = input("You: ")
-        
-        if user_input.lower() == 'exit':
-            break
-
-        response = chatbot_response(user_input)
-        print("Chatbot:", response)
+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 shiny.experimental as x
+from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
+
+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 = x.ui.page_fillable(
+    shinyswatch.theme.minty(),
+    x.ui.layout_sidebar(
+        x.ui.sidebar(
+            # 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.input_checkbox_group(
+                "species", "Filter by species", species, selected=species
+            ),
+            ui.hr(),
+            ui.input_switch("by_species", "Show species", value=True),
+            ui.input_switch("show_margins", "Show marginal plots", value=True),
+        ),
+        ui.output_ui("value_boxes"),
+        x.ui.output_plot("scatter", fill=True),
+        ui.help_text(
+            "Artwork by ",
+            ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
+            class_="text-end",
+        ),
+        fill=True,
+        fillable=True,
+    ),
+)
+
+
+def server(input: Inputs, output: Outputs, session: Session):
+    @reactive.Calc
+    def filtered_df() -> pd.DataFrame:
+        """Returns a Pandas data frame that includes only the desired rows"""
+
+        # This calculation "req"uires that at least one species is selected
+        req(len(input.species()) > 0)
+
+        # Filter the rows so we only include the desired species
+        return df[df["Species"].isin(input.species())]
+
+    @output
+    @render.plot
+    def scatter():
+        """Generates a plot for Shiny to display to the user"""
+
+        # The plotting function to use depends on whether margins are desired
+        plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
+
+        plotfunc(
+            data=filtered_df(),
+            x=input.xvar(),
+            y=input.yvar(),
+            palette=palette,
+            hue="Species" if input.by_species() else None,
+            hue_order=species,
+            legend=False,
+        )
+
+    @output
+    @render.ui
+    def value_boxes():
+        df = filtered_df()
+
+        def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
+            return x.ui.value_box(
+                title,
+                count,
+                {"class_": "pt-1 pb-0"},
+                showcase=x.ui.bind_fill_role(
+                    ui.tags.img(
+                        {"style": "object-fit:contain;"},
+                        src=showcase_img,
+                    ),
+                    item=True,
+                ),
+                theme_color=None,
+                style=f"background-color: {bgcol};",
+                height="90px",
+                full_screen=True,
+            )
+
+        if not input.by_species():
+            return penguin_value_box(
+                "Penguins",
+                len(df.index),
+                bg_palette["default"],
+                # Artwork by @allison_horst
+                showcase_img="penguins.png",
+            )
+
+        value_boxes = [
+            penguin_value_box(
+                name,
+                len(df[df["Species"] == name]),
+                bg_palette[name],
+                # Artwork by @allison_horst
+                showcase_img=f"{name}.png",
+            )
+            for name in species
+            # Only include boxes for _selected_ species
+            if name in input.species()
+        ]
+
+        return x.ui.layout_column_wrap(1 / len(value_boxes), *value_boxes)
+
+
+# "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),
+)