modify plots by includint %

app.py

@@ -19,7 +19,7 @@ class GenderPredictorApp:
         histo_chart = gr.Plot()
         data_output =gr.Dataframe(headers=None)
         # name_buttom = gr.Button("Predict")
-        interface2_fn = gr.Interface(self.predict_github_url, inputs=name, outputs=[pie_chart_output, data_output,histo_chart], title="GitGender: Exploring Global Gender Disparities in Public Code Contributions")
+        interface2_fn = gr.Interface(self.predict_github_url, inputs=name, outputs=[pie_chart_output, histo_chart,data_output], title="GitGender: Exploring Global Gender Disparities in Public Code Contributions")
         demo = gr.TabbedInterface([interface1_fn, interface2_fn], ["Test Model", "Exploring Diversity in GitHub Repositories"])
         self.demo = demo
                 
@@ -42,8 +42,8 @@ class GenderPredictorApp:
         df,first_commit_dates = commit_info.get_first_commit_dates()
         first_commit_dates[['Predicted_Gender', 'Confidence']] = first_commit_dates['Author'].apply(lambda name: pd.Series(self.gender_predictor.predict_gender(name)))
         first_commit_dates['Predicted_Gender'] = first_commit_dates['Predicted_Gender'].replace({0: "Male", 1: "Female", 2: "Unknown"})
-        counts = first_commit_dates['Predicted_Gender'].value_counts()
-
+        Gender_Percentage=plot.get_gender_percentage(first_commit_dates)
+        
         
         # ******************************
         merged_df = df.merge(first_commit_dates[["Author","Predicted_Gender","Confidence"]], on=["Author"])
@@ -52,10 +52,9 @@ class GenderPredictorApp:
         
         
         
-        Gender_Percentage=plot.get_gender_percentage(first_commit_dates)
         fig=plot.get_commits_per_gender(commit_per_gender_counts)
         # Convert the chart to HTML and return it
-        return Gender_Percentage,first_commit_dates[["Author","Author_Timezone","Predicted_Gender"]],fig
+        return Gender_Percentage,fig,first_commit_dates[["Author","Author_Timezone","Predicted_Gender"]]
     def launch(self):
         self.demo.launch()
 

plot.py

@@ -2,17 +2,32 @@ import plotly.graph_objects as go
 from plotly.subplots import make_subplots
 
 def get_commits_per_gender(gender_counts):
-    male_count=gender_counts[gender_counts["Predicted_Gender"]=="Male"]
-    female_count=gender_counts[gender_counts["Predicted_Gender"]=="Female"]
+    gender_counts=gender_counts[gender_counts["Predicted_Gender"]!="Unknown"]
+
+    grouped = gender_counts.groupby('Year').agg({'Count': 'sum'})
+    grouped['Male Percentage'] = gender_counts[gender_counts['Predicted_Gender'] == 'Male'].groupby('Year')['Count'].sum() / grouped['Count'] * 100
+    grouped['Female Percentage'] = gender_counts[gender_counts['Predicted_Gender'] == 'Female'].groupby('Year')['Count'].sum() / grouped['Count'] * 100
+    grouped=grouped.fillna(0)
+    merged_gender_counts = grouped.merge(gender_counts[["Year","Predicted_Gender"]], on=["Year"])
+    
+    male_count=merged_gender_counts[merged_gender_counts["Predicted_Gender"]=="Male"]
+    female_count=merged_gender_counts[merged_gender_counts["Predicted_Gender"]=="Female"]
     fig = make_subplots(rows=1, cols=1, shared_xaxes=True)
+    # Add bars for Male and Female counts
     fig.add_trace(
-        go.Bar(x=male_count['Year'], y=male_count['Count'], name='Male'),
+        go.Bar(x=male_count['Year'], y=male_count['Count'], name='Male',
+            hovertemplate='Male Percentage: %{customdata:.2f}',  # Use customdata for the hovertemplate
+            customdata=male_count['Male Percentage']),  # Use the 'Male Percentage' column for customdata
         row=1, col=1
     )
+
     fig.add_trace(
-        go.Bar(x=female_count['Year'], y=female_count['Count'], name='Female'),
+        go.Bar(x=female_count['Year'], y=female_count['Count'], name='Female',
+            hovertemplate='Female Percentage: %{customdata:.2f}',  # Use customdata for the hovertemplate
+            customdata=female_count['Female Percentage']),  # Use the 'Female Percentage' column for customdata
         row=1, col=1
     )
+
     fig.update_layout(
         height=400,
         xaxis=dict(title="gender commits per year"),
@@ -25,4 +40,5 @@ def get_gender_percentage(df):
     counts = df['Predicted_Gender'].value_counts()
     colors = ["blue", "pink", "gray"]
     Gender_Percentage_plot = go.Figure(data=[go.Pie(labels=df['Predicted_Gender'].unique(), values=counts, marker=dict(colors=colors))])
-    return Gender_Percentage_plot
+    return Gender_Percentage_plot
+