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 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], ] 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[:, 2:].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", "Model Link"], 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) plt.title("Performance of All Models Across Tasks", 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" def plot_task_specific_top_models(): 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"}) 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) plt.tight_layout() plt.savefig("task_specific_top_models.png") return "task_specific_top_models.png" 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 
 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[:, 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("# Comprehensive Model Performance Analysis with Hugging Face Links")

    with gr.Row():
        btn1 = gr.Button("Show Average Performance")
        img1 = gr.Image(type="filepath")
        btn1.click(plot_average_scores, outputs=img1)

    with gr.Row():
        btn2 = gr.Button("Show Task Performance")
        img2 = gr.Image(type="filepath")
        btn2.click(plot_task_performance, outputs=img2)

    with gr.Row():
        btn3 = gr.Button("Task-Specific Top Models")
        img3 = gr.Image(type="filepath")
        btn3.click(plot_task_specific_top_models, outputs=img3)

    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()