map + toggle: need to cleanup code

app.py

@@ -1,8 +1,8 @@
-from dash import Dash, html, dcc, Input, Output
+from dash import Dash, html, dcc, Input, Output, State
 import pandas as pd
 import dash_mantine_components as dmc
 from graphs.model_market_share import create_stacked_area_chart, create_world_map, create_range_slider
-from graphs.leaderboard import create_leaderboard
+from graphs.leaderboard import create_leaderboard, get_top_n_leaderboard, render_table, render_table_content
 from graphs.model_characteristics import create_concentration_chart, create_line_plot
 from graphs.tree import generate_model_treemap
 
@@ -266,8 +266,7 @@ def update_world_map(value):
         start_time = pd.to_datetime(value[0], unit='s').strftime('%Y-%m-%d')
         end_time = pd.to_datetime(value[1], unit='s').strftime('%Y-%m-%d')
         updated_fig = create_world_map(
-            country_concentration_df, "time", "metric", "value",
-            start_time=start_time, end_time=end_time
+            filtered_df
         )
         updated_fig.update_layout(font_family="Inter")
         return updated_fig
@@ -309,6 +308,80 @@ def update_stacked_area(value):
         return updated_fig
     return model_market_share_area
 
+@app.callback(
+    Output("top_countries-table", "children"),
+    Output("top_countries-toggle", "children"),
+    Input("top_countries-toggle", "n_clicks"),
+    State("top_countries-toggle", "children"),
+)
+def update_top_countries(n_clicks, current_label):
+    print(n_clicks, current_label)
+    # Handle initial page load
+    if current_label is None:
+        current_label = "▼ Show Top 50"
+
+    if n_clicks == 0:
+        top_n = 10
+        new_label = current_label
+    elif "Show Top 50" in current_label:
+        top_n, new_label = 50, "▼ Show Top 100"
+    elif "Show Top 100" in current_label:
+        top_n, new_label = 100, "▲ Show Less"
+    else:
+        top_n, new_label = 10, "▼ Show Top 50"
+
+    df, download_df = get_top_n_leaderboard(filtered_df, "org_country_single", top_n)
+    return render_table_content(df, download_df, chip_color="#FCE8E6", filename="top_countries"), new_label
+
+
+@app.callback(
+    Output("top_developers-table", "children"),
+    Output("top_developers-toggle", "children"),
+    Input("top_developers-toggle", "n_clicks"),
+    State("top_developers-toggle", "children"),
+)
+def update_top_developers(n_clicks, current_label):
+    # Handle initial page load
+    if current_label is None:
+        current_label = "▼ Show More"
+
+    if n_clicks == 0:
+        top_n = 10
+        new_label = current_label
+    elif "Show Top 50" in current_label:
+        top_n, new_label = 50, "▼ Show Top 100"
+    elif "Show Top 100" in current_label:
+        top_n, new_label = 100, "▲ Show Less"
+    else:
+        top_n, new_label = 10, "▼ Show Top 50"
+
+    df, download_df = get_top_n_leaderboard(filtered_df, "author", top_n)
+    return render_table_content(df, download_df, chip_color="#E6F4EA", filename="top_developers"), new_label
+
+@app.callback(
+    Output("top_models-table", "children"),
+    Output("top_models-toggle", "children"),
+    Input("top_models-toggle", "n_clicks"),
+    State("top_models-toggle", "children"),
+)
+def update_top_models(n_clicks, current_label):
+    # Handle initial page load
+    if current_label is None:
+        current_label = "▼ Show More"
+
+    if n_clicks == 0:
+        top_n = 10
+        new_label = current_label
+    elif "Show Top 50" in current_label:
+        top_n, new_label = 50, "▼ Show Top 100"
+    elif "Show Top 100" in current_label:
+        top_n, new_label = 100, "▲ Show Less"
+    else:
+        top_n, new_label = 10, "▼ Show Top 50"
+
+    df, download_df = get_top_n_leaderboard(filtered_df, "model", top_n)
+    return render_table_content(df, download_df, chip_color="#E8F0FE", filename="top_models"), new_label
+
 # Run the app
 if __name__ == '__main__':
     app.run(debug=True)

graphs/leaderboard.py

@@ -2,8 +2,21 @@ import pandas as pd
 from dash import html, dcc
 import base64
 
-def create_leaderboard(filtered_df, start_time=None, top_n=10):
-    country_icon_map = {
+button_style = {
+            "display": "inline-block",
+            "marginBottom": "10px",
+            "marginRight": "15px",
+            "marginTop": "30px",
+            "padding": "6px 16px",
+            "backgroundColor": "#2196F3",
+            "color": "white",
+            "borderRadius": "6px",
+            "textDecoration": "none",
+            "fontWeight": "bold",
+            "fontSize": "14px"
+        }
+
+country_icon_map = {
         "USA": "🇺🇸",
         "China": "🇨🇳",
         "Germany": "🇩🇪",
@@ -23,245 +36,144 @@ def create_leaderboard(filtered_df, start_time=None, top_n=10):
         "International/Online": "🌐",
     }
 
-    company_icon_map = {
-        "google": "../assets/icons/google.png",
-        "distilbert": "../assets/icons/hugging-face.png",
-        "sentence-transformers": "../assets/icons/hugging-face.png",
-        "facebook": "../assets/icons/meta.png",
-        "openai": "../assets/icons/openai.png",
-    }
-
-    meta_cols_map = {
-        "org_country_single": ["org_country_single"],
-        "author": ["org_country_single", "author", "merged_country_groups_single"],
-        "model": ["org_country_single", "author", "merged_country_groups_single", "merged_modality", "downloads"]
-    }
-
-    # Filter by time
-    if start_time is not None:
-        filtered_df = filtered_df[(filtered_df["created"] >= start_time) & (filtered_df["time"] >= start_time)]
-
-    if filtered_df.empty:
-        return html.Div("No data in selected range")
-    
-    # Merge HF and USA
-    filtered_df["org_country_single"] = filtered_df["org_country_single"].replace({"HF": "United States of America"})
-    # Merge International and Online
-    filtered_df["org_country_single"] = filtered_df["org_country_single"].replace({"International": "International/Online", "Online": "International/Online"})
-
-    # Function to get top N leaderboard
-    def get_top_n_leaderboard(group_col, top_n=10):
-        top = filtered_df.groupby(group_col)["downloads"].sum().nlargest(top_n).reset_index().rename(columns={group_col: "Name", "downloads": "Total Value"})
-        total_value = top["Total Value"].sum()
-        top["% of total"] = top["Total Value"] / total_value * 100 if total_value else 0
-
-        # Create a downloadable version of the leaderboard
-        download_top = top.copy()
-        download_top["Total Value"] = download_top["Total Value"].astype(int)
-        download_top["% of total"] = download_top["% of total"].round(2)
+company_icon_map = {
+    "google": "../assets/icons/google.png",
+    "distilbert": "../assets/icons/hugging-face.png",
+    "sentence-transformers": "../assets/icons/hugging-face.png",
+    "facebook": "../assets/icons/meta.png",
+    "openai": "../assets/icons/openai.png",
+}
 
-        top["Name"].replace("User", "user", inplace=True)
+meta_cols_map = {
+    "org_country_single": ["org_country_single"],
+    "author": ["org_country_single", "author", "merged_country_groups_single"],
+    "model": ["org_country_single", "author", "merged_country_groups_single", "merged_modality", "downloads"]
+}
 
-        # All relevant metadata columns
-        meta_cols = meta_cols_map.get(group_col, [])
-        # Collect all metadata per top n for each category (country, author, model)
-        meta_map = {}
-        download_map = {}
-        for name in top["Name"]:
-            name_data = filtered_df[filtered_df[group_col] == name]
-            meta_map[name] = {}
-            download_map[name] = {}
-            for col in meta_cols:
-                if col in name_data.columns:
-                    unique_vals = name_data[col].unique()
-                    meta_map[name][col] = list(unique_vals)
-                    download_map[name][col] = list(unique_vals)
-        
-        # Function to build metadata chips
-        def build_metadata(nm):
-            meta = meta_map.get(nm, {})
-            chips = []
-            # Countries
-            for c in meta.get("org_country_single", []):
-                if c == "United States of America":
-                    c = "USA"
-                if c == "user":
-                    c = "User"
-                chips.append((country_icon_map.get(c, ""), c))
-            # Author
-            for a in meta.get("author", []):
-                icon = company_icon_map.get(a, "")
-                if icon == "":
-                    if meta.get("merged_country_groups_single", ["User"])[0] != "User":
-                        icon = "🏢"
-                    else:
-                        icon = "👤"
-                chips.append((icon, a))
-            # Downloads
-            # Sum downloads if multiple entries
-            total_downloads = sum(d for d in meta.get("downloads", []) if pd.notna(d))  # Check if d is not NaN
-            if total_downloads:
-                chips.append(("⬇️", f"{int(total_downloads):,}"))
+# Chip renderer
+def chip(text, bg_color="#F0F0F0"):
+    return html.Span(
+        text,
+        style={
+            "backgroundColor": bg_color,
+            "padding": "4px 10px",
+            "borderRadius": "12px",
+            "margin": "2px",
+            "display": "inline-flex",
+            "alignItems": "center",
+            "fontSize": "14px"
+        }
+    )
 
-            # Modality
-            for m in meta.get("merged_modality", []):
-                chips.append(("", m))
-        
-            # Estimated Parameters
-            for p in meta.get("estimated_parameters", []):
-                if pd.notna(p):  # Check if p is not NaN
-                    if p >= 1e9:
-                        p_str = f"{p/1e9:.1f}B"
-                    elif p >= 1e6:
-                        p_str = f"{p/1e6:.1f}M"
-                    elif p >= 1e3:
-                        p_str = f"{p/1e3:.1f}K"
-                    else:
-                        p_str = str(p)
-                    chips.append(("⚙️", p_str))
-            return chips
-        
-        # Function to create downloadable dataframe
-        def build_download_metadata(nm):
-            meta = download_map.get(nm, {})
-            download_info = {}
-            for col in meta_cols:
-                # don't add empty columns
-                if col not in meta or not meta[col]:
-                    continue
-                vals = meta.get(col, [])
-                if vals:
-                    # Join list into a single string for CSV
-                    download_info[col] = ", ".join(str(v) for v in vals)
-                else:
-                    download_info[col] = ""
-            return download_info
-        
-        # Apply metadata builder to top dataframe
-        top["Metadata"] = top["Name"].map(build_metadata)
-        download_info_list = [build_download_metadata(nm) for nm in download_top["Name"]]
-        download_info_df = pd.DataFrame(download_info_list)
-        download_top = pd.concat([download_top, download_info_df], axis=1)
+# Progress bar for % of total
+def progress_bar(percent, bar_color="#4CAF50"):
+    return html.Div(
+        style={
+            "position": "relative",
+            "backgroundColor": "#E0E0E0",
+            "borderRadius": "8px",
+            "height": "20px",
+            "width": "100%",
+            "overflow": "hidden",
+        },
+        children=[
+            html.Div(
+                style={
+                    "backgroundColor": bar_color,
+                    "width": f"{percent}%",
+                    "height": "100%",
+                    "borderRadius": "8px",
+                    "transition": "width 0.5s",
+                }
+            ),
+            html.Div(
+                f"{percent:.1f}%",
+                style={
+                    "position": "absolute",
+                    "top": 0,
+                    "left": "50%",
+                    "transform": "translateX(-50%)",
+                    "color": "black",
+                    "fontWeight": "bold",
+                    "fontSize": "12px",
+                    "lineHeight": "20px",
+                    "textAlign": "center",
+                }
+            )
+        ]
+    )
 
-        return top[["Name", "Metadata", "% of total"]], download_top
+# Helper to convert DataFrame to CSV and encode for download
+def df_to_download_link(df, filename):
+    csv_string = df.to_csv(index=False)
+    b64 = base64.b64encode(csv_string.encode()).decode()
+    return html.Div(
+        html.A(
+        "Download CSV",
+        id=f"download-{filename}",
+        download=f"{filename}.csv",
+        href=f"data:text/csv;base64,{b64}",
+        target="_blank",
+        style=button_style
+        ),
+        style={"textAlign": "right"}
+    )
 
-    # Build leaderboards
-    top_countries, download_top_countries = get_top_n_leaderboard("org_country_single", top_n)
-    top_developers, download_top_developers = get_top_n_leaderboard("author", top_n)
-    top_models, download_top_models = get_top_n_leaderboard("model", top_n)
+# Render multiple chips in one row
+def render_chips(metadata_list, chip_color="#F0F0F0"):
+    chips = []
+    for icon, name in metadata_list:
+        if isinstance(icon, str) and icon.endswith(('.png', '.jpg', '.jpeg', '.svg')):
+            chips.append(
+                html.Span([
+                    html.Img(src=icon, style={"height": "18px", "marginRight": "6px"}),
+                    name
+                ],
+                style={
+                    "backgroundColor": chip_color,
+                    "padding": "4px 10px",
+                    "borderRadius": "12px",
+                    "margin": "2px",
+                    "display": "inline-flex",
+                    "alignItems": "left",
+                    "fontSize": "14px"
+                })
+            )
+        else:
+            chips.append(chip(f"{icon} {name}", chip_color))
+    return html.Div(
+        chips,
+        style={"display": "flex", "flexWrap": "wrap", "justifyContent": "left"}
+    )
 
-    # Chip renderer
-    def chip(text, bg_color="#F0F0F0"):
-        return html.Span(
-            text,
-            style={
-                "backgroundColor": bg_color,
-                "padding": "4px 10px",
-                "borderRadius": "12px",
-                "margin": "2px",
-                "display": "inline-flex",
-                "alignItems": "center",
-                "fontSize": "14px"
-            }
-        )
-
-    # Render multiple chips in one row
-    def render_chips(metadata_list, chip_color="#F0F0F0"):
-        chips = []
-        for icon, name in metadata_list:
-            if isinstance(icon, str) and icon.endswith(('.png', '.jpg', '.jpeg', '.svg')):
-                chips.append(
-                    html.Span([
-                        html.Img(src=icon, style={"height": "18px", "marginRight": "6px"}),
-                        name
-                    ],
-                    style={
-                        "backgroundColor": chip_color,
-                        "padding": "4px 10px",
-                        "borderRadius": "12px",
-                        "margin": "2px",
-                        "display": "inline-flex",
-                        "alignItems": "left",
-                        "fontSize": "14px"
-                    })
-                )
-            else:
-                chips.append(chip(f"{icon} {name}", chip_color))
-        return html.Div(
-            chips,
-            style={"display": "flex", "flexWrap": "wrap", "justifyContent": "left"}
-        )
-
-    # Progress bar for % of total
-    def progress_bar(percent, bar_color="#4CAF50"):
-        return html.Div(
-            style={
-                "position": "relative",
-                "backgroundColor": "#E0E0E0",
-                "borderRadius": "8px",
-                "height": "20px",
-                "width": "100%",
-                "overflow": "hidden",
-            },
-            children=[
-                html.Div(
-                    style={
-                        "backgroundColor": bar_color,
-                        "width": f"{percent}%",
-                        "height": "100%",
-                        "borderRadius": "8px",
-                        "transition": "width 0.5s",
-                    }
-                ),
-                html.Div(
-                    f"{percent:.1f}%",
-                    style={
-                        "position": "absolute",
-                        "top": 0,
-                        "left": "50%",
-                        "transform": "translateX(-50%)",
-                        "color": "black",
-                        "fontWeight": "bold",
-                        "fontSize": "12px",
-                        "lineHeight": "20px",
-                        "textAlign": "center",
-                    }
-                )
-            ]
-        )
-    
-    # Helper to convert DataFrame to CSV and encode for download
-    def df_to_download_link(df, filename):
-        csv_string = df.to_csv(index=False)
-        b64 = base64.b64encode(csv_string.encode()).decode()
-        return html.Div(
-            html.A(
-            "Download CSV",
-            id=f"download-{filename}",
-            download=f"{filename}.csv",
-            href=f"data:text/csv;base64,{b64}",
-            target="_blank",
-            style={
-                "display": "inline-block",
-                "marginBottom": "10px",
-                "marginRight": "15px",
-                "marginTop": "30px",
-                "padding": "6px 16px",
-                "backgroundColor": "#2196F3",
-                "color": "white",
-                "borderRadius": "6px",
-                "textDecoration": "none",
-                "fontWeight": "bold",
-                "fontSize": "14px"
-            }
-            ),
-            style={"textAlign": "right"}
-        )
+def render_table_content(df, download_df, chip_color="#F0F0F0", bar_color="#4CAF50", filename="data"):
+    return html.Div([
+        html.Table([
+                html.Thead(html.Tr([
+                    html.Th("Rank", style={"backgroundColor": "#F0F0F0", "textAlign": "left"}),
+                    html.Th("Name", style={"backgroundColor": "#F0F0F0", "textAlign": "left"}),
+                    html.Th("Metadata", style={"backgroundColor": "#F0F0F0", "textAlign": "left", "marginRight": "10px"}),
+                    html.Th("% of Total", style={"backgroundColor": "#F0F0F0", "textAlign": "left"})
+                ])),
+                html.Tbody([
+                    html.Tr([
+                        html.Td(idx+1, style={"textAlign": "center"}),
+                        html.Td(row["Name"], style={"textAlign": "left"}),
+                        html.Td(render_chips(row["Metadata"], chip_color)),
+                        html.Td(progress_bar(row["% of total"], bar_color), style={"textAlign": "center"})
+                    ]) for idx, row in df.iterrows()
+                ])
+            ], style={"borderCollapse": "collapse", "width": "100%"}),
+        ])
 
-    # Table renderer
-    def render_table(df, download_df, title, chip_color="#F0F0F0", bar_color="#4CAF50", filename="data"):
-        return html.Div([
-            html.H4(title, style={"textAlign": "left", "marginBottom": "10px", "fontSize": "20px"}),
+# Table renderer
+def render_table(df, download_df, title, chip_color="#F0F0F0", bar_color="#4CAF50", filename="data"):
+    return html.Div(id=f"{filename}-div", children=[
+        html.Div([
+            html.H4(title, style={"textAlign": "left", "marginBottom": "10px", "fontSize": "20px", "display": "inline-block"}),
+            df_to_download_link(download_df, filename)
+        ], style={"display": "flex", "alignItems": "center", "justifyContent": "space-between"}),
+        html.Div(id=f"{filename}-table", children=[
             html.Table([
                 html.Thead(html.Tr([
                     html.Th("Rank", style={"backgroundColor": "#F0F0F0", "textAlign": "left"}),
@@ -277,9 +189,131 @@ def create_leaderboard(filtered_df, start_time=None, top_n=10):
                         html.Td(progress_bar(row["% of total"], bar_color), style={"textAlign": "center"})
                     ]) for idx, row in df.iterrows()
                 ])
-            ], style={"borderCollapse": "collapse", "width": "100%"}),
-            df_to_download_link(download_df, filename),
-        ], style={"marginBottom": "20px"})
+            ], style={"borderCollapse": "collapse", "width": "100%", "border": "none"}),
+        ]),
+        html.Div([
+            html.Button(
+                "▼ Show Top 50",
+                id=f"{filename}-toggle",
+                n_clicks=0,
+                style={**button_style, "border": "none"}
+            )
+        ], style={"marginTop": "5px", "textAlign": "left"})
+    ], style={"marginBottom": "20px"})
+
+# Function to get top N leaderboard
+def get_top_n_leaderboard(filtered_df, group_col, top_n=10):
+    top = filtered_df.groupby(group_col)["downloads"].sum().nlargest(top_n).reset_index().rename(columns={group_col: "Name", "downloads": "Total Value"})
+    total_value = top["Total Value"].sum()
+    top["% of total"] = top["Total Value"] / total_value * 100 if total_value else 0
+
+    # Create a downloadable version of the leaderboard
+    download_top = top.copy()
+    download_top["Total Value"] = download_top["Total Value"].astype(int)
+    download_top["% of total"] = download_top["% of total"].round(2)
+
+    top["Name"].replace("User", "user", inplace=True)
+
+    # All relevant metadata columns
+    meta_cols = meta_cols_map.get(group_col, [])
+    # Collect all metadata per top n for each category (country, author, model)
+    meta_map = {}
+    download_map = {}
+    for name in top["Name"]:
+        name_data = filtered_df[filtered_df[group_col] == name]
+        meta_map[name] = {}
+        download_map[name] = {}
+        for col in meta_cols:
+            if col in name_data.columns:
+                unique_vals = name_data[col].unique()
+                meta_map[name][col] = list(unique_vals)
+                download_map[name][col] = list(unique_vals)
+    
+    # Function to build metadata chips
+    def build_metadata(nm):
+        meta = meta_map.get(nm, {})
+        chips = []
+        # Countries
+        for c in meta.get("org_country_single", []):
+            if c == "United States of America":
+                c = "USA"
+            if c == "user":
+                c = "User"
+            chips.append((country_icon_map.get(c, ""), c))
+        # Author
+        for a in meta.get("author", []):
+            icon = company_icon_map.get(a, "")
+            if icon == "":
+                if meta.get("merged_country_groups_single", ["User"])[0] != "User":
+                    icon = "🏢"
+                else:
+                    icon = "👤"
+            chips.append((icon, a))
+        # Downloads
+        # Sum downloads if multiple entries
+        total_downloads = sum(d for d in meta.get("downloads", []) if pd.notna(d))  # Check if d is not NaN
+        if total_downloads:
+            chips.append(("⬇️", f"{int(total_downloads):,}"))
+
+        # Modality
+        for m in meta.get("merged_modality", []):
+            chips.append(("", m))
+    
+        # Estimated Parameters
+        for p in meta.get("estimated_parameters", []):
+            if pd.notna(p):  # Check if p is not NaN
+                if p >= 1e9:
+                    p_str = f"{p/1e9:.1f}B"
+                elif p >= 1e6:
+                    p_str = f"{p/1e6:.1f}M"
+                elif p >= 1e3:
+                    p_str = f"{p/1e3:.1f}K"
+                else:
+                    p_str = str(p)
+                chips.append(("⚙️", p_str))
+        return chips
+    
+    # Function to create downloadable dataframe
+    def build_download_metadata(nm):
+        meta = download_map.get(nm, {})
+        download_info = {}
+        for col in meta_cols:
+            # don't add empty columns
+            if col not in meta or not meta[col]:
+                continue
+            vals = meta.get(col, [])
+            if vals:
+                # Join list into a single string for CSV
+                download_info[col] = ", ".join(str(v) for v in vals)
+            else:
+                download_info[col] = ""
+        return download_info
+    
+    # Apply metadata builder to top dataframe
+    top["Metadata"] = top["Name"].map(build_metadata)
+    download_info_list = [build_download_metadata(nm) for nm in download_top["Name"]]
+    download_info_df = pd.DataFrame(download_info_list)
+    download_top = pd.concat([download_top, download_info_df], axis=1)
+
+    return top[["Name", "Metadata", "% of total"]], download_top
+
+def create_leaderboard(filtered_df, start_time=None, top_n=10):
+    # Filter by time
+    if start_time is not None:
+        filtered_df = filtered_df[(filtered_df["created"] >= start_time) & (filtered_df["time"] >= start_time)]
+
+    if filtered_df.empty:
+        return html.Div("No data in selected range")
+    
+    # Merge HF and USA
+    filtered_df["org_country_single"] = filtered_df["org_country_single"].replace({"HF": "United States of America"})
+    # Merge International and Online
+    filtered_df["org_country_single"] = filtered_df["org_country_single"].replace({"International": "International/Online", "Online": "International/Online"})
+
+    # Build leaderboards
+    top_countries, download_top_countries = get_top_n_leaderboard(filtered_df, "org_country_single", top_n)
+    top_developers, download_top_developers = get_top_n_leaderboard(filtered_df, "author", top_n)
+    top_models, download_top_models = get_top_n_leaderboard(filtered_df, "model", top_n)
 
     # Layout with 3 stacked tables
     layout = html.Div([

graphs/model_market_share.py

@@ -234,7 +234,13 @@ def create_world_map(
         specs=[[{"type": "geo"}]],
     )
 
-    downloads_by_country = df.groupby('org_country_single')['downloads'].sum().reset_index()
+    downloads_by_country = (
+        df.groupby(['org_country_single', 'country_code'])['downloads']
+        .sum()
+        .reset_index()
+    )
+
+    print(downloads_by_country.columns)
     
     # Prepare top countries for annotation
     total_downloads = float(downloads_by_country['downloads'].sum())
@@ -246,9 +252,11 @@ def create_world_map(
         hover_text.append(
             f"<b>{row['org_country_single']}</b><br>"
             f"Avg Downloads: {row['pct']:.1f}% of total<br>"
-            f"Avg Value: {row['downloads']:.6f}"
         )
 
+    linear_ticks = [0.01, 0.1, 10, 50, 100]  # percent values
+    log_ticks = np.log10(linear_ticks) # what you're actually plotting
+
     # Add choropleth to plot
     fig.add_trace(
         go.Choropleth(
@@ -268,6 +276,8 @@ def create_world_map(
             ],
             colorbar=dict(
                 title="Avg % of Total Downloads",
+                tickvals=log_ticks, # positions in log space
+                ticktext=[f"{t}%" for t in linear_ticks],  # labels shown
                 tickfont=dict(size=12),
                 len=0.6,
                 x=1.02,