Adjusting graph configurations

app.py

@@ -86,12 +86,12 @@ with demo:
             with gr.Row():
                 gr.Markdown(f"Market id = {best_market_id}")
             with gr.Row():
-                with gr.Column(scale=1, min_width=300):
+                with gr.Column(min_width=350):
                     gr.Markdown("# Evolution of outcomes probability based on tokens")
                     best_market_tokens_dist = get_based_tokens_distribution(
                         best_market_id, markets_data
                     )
-                with gr.Column(scale=2, min_width=300):
+                with gr.Column(min_width=350):
                     gr.Markdown("# Evolution of outcomes probability based on votes")
                     best_market_votes_dist = get_based_votes_distribution(
                         best_market_id, markets_data
@@ -103,12 +103,12 @@ with demo:
                 gr.Markdown(f"Market id = {worst_market_id}")
 
             with gr.Row():
-                with gr.Column(scale=1, min_width=300):
+                with gr.Column(min_width=350):
                     # gr.Markdown("# Evolution of outcomes probability based on tokens")
                     worst_market_tokens_dist = get_based_tokens_distribution(
                         worst_market_id, markets_data
                     )
-                with gr.Column(scale=2, min_width=300):
+                with gr.Column(min_width=350):
                     worst_market_votes_dist = get_based_votes_distribution(
                         worst_market_id, markets_data
                     )

tabs/dist_gap.py

@@ -21,7 +21,7 @@ def get_distribution_plot(markets_data: pd.DataFrame):
     # observations in a dataset, analogous to a histogram. KDE represents the data using a
     # continuous probability density curve in one or more dimensions.
     sns.set_theme(palette="viridis")
-    plt.figure(figsize=(25, 10))
+    plt.figure(figsize=(10, 5))
 
     plot = sns.kdeplot(markets_data, x="dist_gap_perc", fill=True)
     # TODO Add title and labels
@@ -31,8 +31,8 @@ def get_distribution_plot(markets_data: pd.DataFrame):
 
 def get_kde_with_trades(markets_data: pd.DataFrame):
     """Function to paint the density plot of the metric in terms of the number of trades"""
-    plot = sns.kdeplot(markets_data, x="dist_gap_perc", y="total_trades", fill=True)
-
+    plt = sns.kdeplot(markets_data, x="dist_gap_perc", y="total_trades", fill=True)
+    plt.ylabel("Total number of trades per market")
     return gr.Plot(value=plot.get_figure())
 
 
@@ -46,7 +46,7 @@ def get_correlation_map(markets_data: pd.DataFrame):
     correlation_matrix = data.corr()
 
     # Create a figure and axis
-    plt.figure(figsize=(10, 8))
+    # plt.figure(figsize=(10, 8))
 
     # Create the heatmap
     heatmap = sns.heatmap(