Update app.py

app.py

@@ -1,5 +1,6 @@
 import pandas as pd
 import matplotlib.pyplot as plt
+import seaborn as sns
 import gradio as gr
 
 # Input data
@@ -36,90 +37,57 @@ columns = ["Model Configuration", "tinyArc", "tinyHellaswag", "tinyMMLU", "tinyT
 # Convert to DataFrame
 df_full = pd.DataFrame(data_full, columns=columns)
 
-def plot_average_scores():
-    df_full["Average Score"] = df_full.iloc[:, 1:].mean(axis=1)
-    df_avg_sorted = df_full.sort_values(by="Average Score", ascending=False)
-
-    plt.figure(figsize=(12, 8))
-    plt.barh(df_avg_sorted["Model Configuration"], df_avg_sorted["Average Score"])
-    plt.title("Average Performance of Models Across Tasks", fontsize=16)
-    plt.xlabel("Average Score", fontsize=14)
-    plt.ylabel("Model Configuration", fontsize=14)
-    plt.gca().invert_yaxis()
-    plt.grid(axis='x', linestyle='--', alpha=0.7)
-    plt.tight_layout()
-    plt.savefig("average_performance.png")
-    return "average_performance.png"
-
-def plot_task_performance():
-    df_full_melted = df_full.melt(id_vars="Model Configuration", var_name="Task", value_name="Score")
-
-    plt.figure(figsize=(14, 10))
-    for model in df_full["Model Configuration"]:
-        model_data = df_full_melted[df_full_melted["Model Configuration"] == model]
-        plt.plot(model_data["Task"], model_data["Score"], marker="o", label=model)
+def summary_statistics():
+    stats = df_full.iloc[:, 1:].describe().T  # Summary stats for each task
+    stats['Std Dev'] = df_full.iloc[:, 1:].std(axis=0)
+    return stats.reset_index()
 
-    plt.title("Performance of All Models Across Tasks", fontsize=16)
+def plot_distribution_boxplots():
+    plt.figure(figsize=(14, 8))
+    df_melted = df_full.melt(id_vars="Model Configuration", var_name="Task", value_name="Score")
+    sns.boxplot(x="Task", y="Score", data=df_melted)
+    plt.title("Score Distribution by Task", fontsize=16)
     plt.xlabel("Task", fontsize=14)
     plt.ylabel("Score", fontsize=14)
-    plt.xticks(rotation=45)
-    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=9)
     plt.grid(axis='y', linestyle='--', alpha=0.7)
     plt.tight_layout()
-    plt.savefig("task_performance.png")
-    return "task_performance.png"
+    plt.savefig("distribution_boxplots.png")
+    return "distribution_boxplots.png"
 
-def plot_task_specific_top_models():
-    top_models = df_full.iloc[:, :-1].set_index("Model Configuration").idxmax()
-    top_scores = df_full.iloc[:, :-1].set_index("Model Configuration").max()
-
-    results = pd.DataFrame({"Top Model": top_models, "Score": top_scores}).reset_index().rename(columns={"index": "Task"})
+def best_overall_model():
+    df_full["Average Score"] = df_full.iloc[:, 1:].mean(axis=1)
+    best_model = df_full.loc[df_full["Average Score"].idxmax()]
+    return best_model
 
-    plt.figure(figsize=(12, 6))
-    plt.bar(results["Task"], results["Score"])
-    plt.title("Task-Specific Top Models", fontsize=16)
-    plt.xlabel("Task", fontsize=14)
-    plt.ylabel("Score", fontsize=14)
-    plt.grid(axis="y", linestyle="--", alpha=0.7)
+def plot_heatmap():
+    plt.figure(figsize=(12, 8))
+    sns.heatmap(df_full.iloc[:, 1:], annot=True, cmap="YlGnBu", xticklabels=columns[1:], yticklabels=df_full["Model Configuration"])
+    plt.title("Performance Heatmap", fontsize=16)
     plt.tight_layout()
-    plt.savefig("task_specific_top_models.png")
-    return "task_specific_top_models.png"
-
-def top_3_models_per_task():
-    top_3_data = {
-        task: df_full.nlargest(3, task)[["Model Configuration", task]].values.tolist()
-        for task in df_full.columns[1:-1]
-    }
-    top_3_results = pd.DataFrame({
-        task: {
-            "Top 3 Models": [entry[0] for entry in top_3_data[task]],
-            "Scores": [entry[1] for entry in top_3_data[task]],
-        }
-        for task in top_3_data
-    }).T.rename_axis("Task").reset_index()
-    return top_3_results
+    plt.savefig("performance_heatmap.png")
+    return "performance_heatmap.png"
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Model Performance Analysis")
+    gr.Markdown("# Enhanced Model Performance Analysis")
 
     with gr.Row():
-        btn1 = gr.Button("Show Average Performance")
-        img1 = gr.Image(type="filepath")
-        btn1.click(plot_average_scores, inputs=None, outputs=img1)
+        btn1 = gr.Button("Show Summary Statistics")
+        stats_output = gr.Dataframe()
+        btn1.click(summary_statistics, outputs=stats_output)
 
     with gr.Row():
-        btn2 = gr.Button("Show Task Performance")
-        img2 = gr.Image(type="filepath")
-        btn2.click(plot_task_performance, inputs=None, outputs=img2)
+        btn2 = gr.Button("Plot Score Distributions")
+        dist_img = gr.Image(type="filepath")
+        btn2.click(plot_distribution_boxplots, outputs=dist_img)
 
     with gr.Row():
-        btn3 = gr.Button("Task-Specific Top Models")
-        img3 = gr.Image(type="filepath")
-        btn3.click(plot_task_specific_top_models, inputs=None, outputs=img3)
+        btn3 = gr.Button("Best Overall Model")
+        best_output = gr.Textbox()
+        btn3.click(best_overall_model, outputs=best_output)
 
     with gr.Row():
-        btn4 = gr.Button("Top 3 Models Per Task")
-        output4 = gr.Dataframe()
-        btn4.click(top_3_models_per_task, inputs=None, outputs=output4)
+        btn4 = gr.Button("Plot Performance Heatmap")
+        heatmap_img = gr.Image(type="filepath")
+        btn4.click(plot_heatmap, outputs=heatmap_img)
 
 demo.launch()