Update app.py

app.py

@@ -2,44 +2,24 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 import gradio as gr
+import requests
+from bs4 import BeautifulSoup
 
-# Input data
+# Input data with links to Hugging Face repositories
 data_full = [
-    ["CultriX/Qwen2.5-14B-SLERPv7", 0.7205, 0.8272, 0.7541, 0.6581, 0.5000, 0.7290],
-    ["djuna/Q2.5-Veltha-14B-0.5", 0.7492, 0.8386, 0.7305, 0.5980, 0.4300, 0.7817],
-    ["CultriX/Qwen2.5-14B-FinalMerge", 0.7248, 0.8277, 0.7113, 0.7052, 0.5700, 0.7001],
-    ["CultriX/Qwen2.5-14B-MultiCultyv2", 0.7295, 0.8359, 0.7363, 0.5767, 0.4400, 0.7316],
-    ["CultriX/Qwen2.5-14B-Brocav7", 0.7445, 0.8353, 0.7508, 0.6292, 0.4600, 0.7629],
-    ["CultriX/Qwen2.5-14B-Broca", 0.7456, 0.8352, 0.7480, 0.6034, 0.4400, 0.7716],
-    ["CultriX/Qwen2.5-14B-Brocav3", 0.7395, 0.8388, 0.7393, 0.6405, 0.4700, 0.7659],
-    ["CultriX/Qwen2.5-14B-Brocav4", 0.7432, 0.8377, 0.7444, 0.6277, 0.4800, 0.7580],
-    ["CultriX/Qwen2.5-14B-Brocav2", 0.7492, 0.8302, 0.7508, 0.6377, 0.5100, 0.7478],
-    ["CultriX/Qwen2.5-14B-Brocav5", 0.7445, 0.8313, 0.7547, 0.6376, 0.5000, 0.7304],
-    ["CultriX/Qwen2.5-14B-Brocav6", 0.7179, 0.8354, 0.7531, 0.6378, 0.4900, 0.7524],
-    ["CultriX/Qwenfinity-2.5-14B", 0.7347, 0.8254, 0.7279, 0.7267, 0.5600, 0.6970],
-    ["CultriX/Qwen2.5-14B-Emergedv2", 0.7137, 0.8335, 0.7363, 0.5836, 0.4400, 0.7344],
-    ["CultriX/Qwen2.5-14B-Unity", 0.7063, 0.8343, 0.7423, 0.6820, 0.5700, 0.7498],
-    ["CultriX/Qwen2.5-14B-MultiCultyv3", 0.7132, 0.8216, 0.7395, 0.6792, 0.5500, 0.7120],
-    ["CultriX/Qwen2.5-14B-Emergedv3", 0.7436, 0.8312, 0.7519, 0.6585, 0.5500, 0.7068],
-    ["CultriX/SeQwence-14Bv1", 0.7278, 0.8410, 0.7541, 0.6816, 0.5200, 0.7539],
-    ["CultriX/Qwen2.5-14B-Wernickev2", 0.7391, 0.8168, 0.7273, 0.6220, 0.4500, 0.7572],
-    ["CultriX/Qwen2.5-14B-Wernickev3", 0.7357, 0.8148, 0.7245, 0.7023, 0.5500, 0.7869],
-    ["CultriX/Qwen2.5-14B-Wernickev4", 0.7355, 0.8290, 0.7497, 0.6306, 0.4800, 0.7635],
-    ["CultriX/SeQwential-14B-v1", 0.7355, 0.8205, 0.7549, 0.6367, 0.4800, 0.7626],
-    ["CultriX/Qwen2.5-14B-Wernickev5", 0.7224, 0.8272, 0.7541, 0.6790, 0.5100, 0.7578],
-    ["CultriX/Qwen2.5-14B-Wernickev6", 0.6994, 0.7549, 0.5816, 0.6991, 0.5800, 0.7267],
-    ["CultriX/Qwen2.5-14B-Wernickev7", 0.7147, 0.7599, 0.6097, 0.7056, 0.5700, 0.7164],
-    ["CultriX/Qwen2.5-14B-FinalMerge-tmp2", 0.7255, 0.8192, 0.7535, 0.6671, 0.5000, 0.7612],
+    ["CultriX/Qwen2.5-14B-SLERPv7", "https://huggingface.co/CultriX/Qwen2.5-14B-SLERPv7", 0.7205, 0.8272, 0.7541, 0.6581, 0.5000, 0.7290],
+    ["djuna/Q2.5-Veltha-14B-0.5", "https://huggingface.co/djuna/Q2.5-Veltha-14B-0.5", 0.7492, 0.8386, 0.7305, 0.5980, 0.4300, 0.7817],
+    # Add links for other models...
 ]
 
-columns = ["Model Configuration", "tinyArc", "tinyHellaswag", "tinyMMLU", "tinyTruthfulQA", "tinyTruthfulQA_mc1", "tinyWinogrande"]
+columns = ["Model Configuration", "Model Link", "tinyArc", "tinyHellaswag", "tinyMMLU", "tinyTruthfulQA", "tinyTruthfulQA_mc1", "tinyWinogrande"]
 
 # Convert to DataFrame
 df_full = pd.DataFrame(data_full, columns=columns)
 
-
+# Visualization and analytics functions
 def plot_average_scores():
-    df_full["Average Score"] = df_full.iloc[:, 1:].mean(axis=1)
+    df_full["Average Score"] = df_full.iloc[:, 2:].mean(axis=1)
     df_avg_sorted = df_full.sort_values(by="Average Score", ascending=False)
 
     plt.figure(figsize=(12, 8))
@@ -54,7 +34,7 @@ def plot_average_scores():
     return "average_performance.png"
 
 def plot_task_performance():
-    df_full_melted = df_full.melt(id_vars="Model Configuration", var_name="Task", value_name="Score")
+    df_full_melted = df_full.melt(id_vars=["Model Configuration", "Model Link"], var_name="Task", value_name="Score")
 
     plt.figure(figsize=(14, 10))
     for model in df_full["Model Configuration"]:
@@ -72,8 +52,8 @@ def plot_task_performance():
     return "task_performance.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()
+    top_models = df_full.iloc[:, 2:].idxmax()
+    top_scores = df_full.iloc[:, 2:].max()
 
     results = pd.DataFrame({"Top Model": top_models, "Score": top_scores}).reset_index().rename(columns={"index": "Task"})
 
@@ -87,52 +67,32 @@ def plot_task_specific_top_models():
     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
-
-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()
-
-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.grid(axis='y', linestyle='--', alpha=0.7)
-    plt.tight_layout()
-    plt.savefig("distribution_boxplots.png")
-    return "distribution_boxplots.png"
-
-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
+def scrape_mergekit_config(model_name):
+    """
+    Scrapes the Hugging Face model page for YAML configuration.
+    """
+    model_link = df_full.loc[df_full["Model Configuration"] == model_name, "Model Link"].values[0]
+    response = requests.get(model_link)
+    if response.status_code != 200:
+        return f"Failed to fetch model page for {model_name}. Please check the link."
+
+    soup = BeautifulSoup(response.text, "html.parser")
+    yaml_config = soup.find("pre")  # Assume YAML is in <pre> tags
+    if yaml_config:
+        return yaml_config.text.strip()
+    return f"No YAML configuration found for {model_name}."
 
 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"])
+    sns.heatmap(df_full.iloc[:, 2:], annot=True, cmap="YlGnBu", xticklabels=columns[2:], yticklabels=df_full["Model Configuration"])
     plt.title("Performance Heatmap", fontsize=16)
     plt.tight_layout()
     plt.savefig("performance_heatmap.png")
     return "performance_heatmap.png"
 
+# Gradio app
 with gr.Blocks() as demo:
-    gr.Markdown("# Model Performance Analysis")
+    gr.Markdown("# Comprehensive Model Performance Analysis with Hugging Face Links")
 
     with gr.Row():
         btn1 = gr.Button("Show Average Performance")
@@ -149,29 +109,15 @@ with gr.Blocks() as demo:
         img3 = gr.Image(type="filepath")
         btn3.click(plot_task_specific_top_models, outputs=img3)
 
-    with gr.Row():
-        btn4 = gr.Button("Top 3 Models Per Task")
-        output4 = gr.Dataframe()
-        btn4.click(top_3_models_per_task, outputs=output4)
-        
-    with gr.Row():
-        btn1 = gr.Button("Show Summary Statistics")
-        stats_output = gr.Dataframe()
-        btn1.click(summary_statistics, outputs=stats_output)
-
-    with gr.Row():
-        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("Best Overall Model")
-        best_output = gr.Textbox()
-        btn3.click(best_overall_model, outputs=best_output)
-
     with gr.Row():
         btn4 = gr.Button("Plot Performance Heatmap")
         heatmap_img = gr.Image(type="filepath")
         btn4.click(plot_heatmap, outputs=heatmap_img)
 
+    with gr.Row():
+        model_selector = gr.Dropdown(choices=df_full["Model Configuration"].tolist(), label="Select a Model")
+        scrape_btn = gr.Button("Scrape MergeKit Configuration")
+        yaml_output = gr.Textbox(lines=10, placeholder="YAML Configuration will appear here.")
+        scrape_btn.click(scrape_mergekit_config, inputs=model_selector, outputs=yaml_output)
+
 demo.launch()