Update app.py

app.py

@@ -19,29 +19,43 @@ size_map = json.load(open("size_map.json"))
 raw_data = pd.read_csv("./tagged_data.csv")
 
 def plot_scatter(cat, x, y, col):
-    if cat != "All":
-        data = raw_data[raw_data["Category"] == cat]
-    else:
-        data = raw_data
-    # Group and normalize the data
-    grouped_cat = data.groupby(["model", "tag"]).size().reset_index(name="count").sort_values(by="count", ascending=False)
-    grouped_cat["count"] = grouped_cat.groupby(["model"])["count"].transform(lambda x: x / x.sum())
+    # minimal example
+    pivot_df = grouped_cat.pivot(index='model', columns='tag', values='count').fillna(0).reset_index()
 
-    # Pivot the data for stacking
-    pivot_df = grouped_cat.pivot(index='model', columns='tag', values='count').fillna(0)
-    # pivot_df = pivot_df.sort_values(by="A", ascending=False)
-    # add color vis
     if col == "Size":
-        pivot_df[col] = pivot_df.index.map(size_map)
-        grouped_cat = grouped_cat.dropna(inplace=True)
+        pivot_df[col] = pivot_df["model"].map(size_map)
     else:
-        pivot_df[col] = pivot_df.index.str.split("/").str[0]
+        pivot_df[col] = pivot_df["model"].str.split("/").str[0]
+    
+    fig = px.scatter_3d(pivot_df, x=x, y=y, z=z,
+        hover_name="model",
+        title=f'{x} vs {y} vs {z}',
+        color=col,
+        color_continuous_scale="agsunset")
 
-    # Create an interactive scatter plot
-    fig = px.scatter(pivot_df, x=x, y=y, hover_name=pivot_df.index, title=f'{x} vs {y}', color=col, color_continuous_scale="agsunset")
+    # if cat != "All":
+    #     data = raw_data[raw_data["Category"] == cat]
+    # else:
+    #     data = raw_data
+    # # Group and normalize the data
+    # grouped_cat = data.groupby(["model", "tag"]).size().reset_index(name="count").sort_values(by="count", ascending=False)
+    # grouped_cat["count"] = grouped_cat.groupby(["model"])["count"].transform(lambda x: x / x.sum())
 
-    # Show the plot
-    return fig
+    # # Pivot the data for stacking
+    # pivot_df = grouped_cat.pivot(index='model', columns='tag', values='count').fillna(0)
+    # # pivot_df = pivot_df.sort_values(by="A", ascending=False)
+    # # add color vis
+    # if col == "Size":
+    #     pivot_df[col] = pivot_df.index.map(size_map)
+    #     grouped_cat = grouped_cat.dropna(inplace=True)
+    # else:
+    #     pivot_df[col] = pivot_df.index.str.split("/").str[0]
+
+    # # Create an interactive scatter plot
+    # fig = px.scatter(pivot_df, x=x, y=y, hover_name=pivot_df.index, title=f'{x} vs {y}', color=col, color_continuous_scale="agsunset")
+
+    # # Show the plot
+    # return fig
 
 # Tab 3
 def plot_scatter_tab3(subcat, col):