Update app.py

app.py

@@ -1,9 +1,9 @@
+import requests
+from bs4 import BeautifulSoup
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 import gradio as gr
-import requests
-from bs4 import BeautifulSoup
 import io
 import os
 import base64
@@ -13,11 +13,12 @@ from io import BytesIO
 import tempfile
 import sys
 
-
 # --------------------------------------------------------------------
-# PART 1: YOUR EXISTING (TINY) DATA & PLOTS
+# PART 1: TINY DATA + PLOTS
 # --------------------------------------------------------------------
 
+# This dataframe is your “tiny” version of model performance data.
+# Used for plotting & demonstration in the Gradio app.
 data_full = [
     ['CultriX/Qwen2.5-14B-SLERPv7', 'https://huggingface.co/CultriX/Qwen2.5-14B-SLERPv7', 0.7205, 0.8272, 0.7541, 0.6581, 0.5, 0.729],
     ['djuna/Q2.5-Veltha-14B-0.5', 'https://huggingface.co/djuna/Q2.5-Veltha-14B-0.5', 0.7492, 0.8386, 0.7305, 0.598, 0.43, 0.7817],
@@ -44,10 +45,12 @@ data_full = [
     ['CultriX/Qwen2.5-14B-Wernickev6', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev6', 0.6994, 0.7549, 0.5816, 0.6991, 0.58, 0.7267],
     ['CultriX/Qwen2.5-14B-Wernickev7', 'https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev7', 0.7147, 0.7599, 0.6097, 0.7056, 0.57, 0.7164],
     ['CultriX/Qwen2.5-14B-FinalMerge-tmp2', 'https://huggingface.co/CultriX/Qwen2.5-14B-FinalMerge-tmp2', 0.7255, 0.8192, 0.7535, 0.6671, 0.5, 0.7612],
-    ['CultriX/Qwen2.5-14B-BrocaV8', 'https://huggingface.co/CultriX/Qwen2.5-14B-BrocaV8', 0.7415, 0.8396, 0.7334, 0.5785, 0.4300, 0.7646],
+    ['CultriX/Qwen2.5-14B-BrocaV8', 'https://huggingface.co/CultriX/Qwen2.5-14B-BrocaV8', 0.7415, 0.8396, 0.7334, 0.5785, 0.43, 0.7646],
+]
+columns = [
+    "Model Configuration", "Model Link", "tinyArc", "tinyHellaswag",
+    "tinyMMLU", "tinyTruthfulQA", "tinyTruthfulQA_mc1", "tinyWinogrande"
 ]
-columns = ["Model Configuration", "Model Link", "tinyArc", "tinyHellaswag", 
-           "tinyMMLU", "tinyTruthfulQA", "tinyTruthfulQA_mc1", "tinyWinogrande"]
 df_full = pd.DataFrame(data_full, columns=columns)
 
 def plot_average_scores():
@@ -75,8 +78,10 @@ def plot_average_scores():
     return pil_image, temp_image_file.name
 
 def plot_task_performance():
-    df_full_melted = df_full.melt(id_vars=["Model Configuration", "Model Link"], 
-                                  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=(16, 12))
     for model in df_full["Model Configuration"]:
@@ -127,8 +132,13 @@ def plot_task_specific_top_models():
 
 def plot_heatmap():
     plt.figure(figsize=(14, 10))
-    sns.heatmap(df_full.iloc[:, 2:], annot=True, cmap="YlGnBu", 
-                xticklabels=columns[2:], 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()
     
@@ -143,7 +153,15 @@ def plot_heatmap():
     return pil_image, temp_image_file.name
 
 def scrape_mergekit_config(model_name):
-    model_link = df_full.loc[df_full["Model Configuration"] == model_name, "Model Link"].values[0]
+    """
+    For the *tiny* table’s model links. 
+    Scrapes <pre> tags on the huggingface model page to find a YAML config.
+    """
+    df_row = df_full.loc[df_full["Model Configuration"] == model_name]
+    if df_row.empty:
+        return f"No data found for model {model_name}."
+
+    model_link = df_row["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."
@@ -155,12 +173,18 @@ def scrape_mergekit_config(model_name):
     return f"No YAML configuration found for {model_name}."
 
 def download_yaml(yaml_content, model_name):
+    """
+    Let users download the scraped YAML if it exists. 
+    """
     if "No YAML configuration found" in yaml_content or "Failed to fetch model page" in yaml_content:
         return None
     filename = f"{model_name.replace('/', '_')}_config.yaml"
     return gr.File(value=yaml_content.encode(), filename=filename)
 
 def scrape_model_page(model_url):
+    """
+    Used for the "Live Scraping" text box in the Gradio UI.
+    """
     try:
         response = requests.get(model_url)
         if response.status_code != 200:
@@ -176,9 +200,18 @@ def scrape_model_page(model_url):
         return f"Error: {str(e)}"
 
 def display_scraped_model_data(model_url):
+    """
+    Helper for the "Live Scraping Features" section of the Gradio app.
+    """
     return scrape_model_page(model_url)
 
 def download_all_data():
+    """
+    Builds and returns a zip of:
+      - the CSV of your 'tiny' data,
+      - four plots (average performance, task performance, top models, heatmap),
+      - any YAML configurations for the 'tiny' table's models (if found).
+    """
     import io
     csv_buffer = io.StringIO()
     df_full.to_csv(csv_buffer, index=False)
@@ -200,28 +233,27 @@ def download_all_data():
     with zipfile.ZipFile(zip_buffer, 'w') as zf:
         zf.writestr("model_scores.csv", csv_data)
 
+        # Add the images
         for name, (pil_image, filename) in plot_dict.items():
             image_bytes = io.BytesIO()
             pil_image.save(image_bytes, format='PNG')
             image_bytes.seek(0)
             zf.writestr(filename, image_bytes.read())
 
-        # Also try scraping each model for a YAML config
+        # Also try scraping each model in the *tiny* dataset for a YAML config
         for model_name in df_full["Model Configuration"].to_list():
             yaml_content = scrape_mergekit_config(model_name)
             if ("No YAML configuration found" not in yaml_content) and ("Failed to fetch model page" not in yaml_content):
-               zf.writestr(f"{model_name.replace('/', '_')}_config.yaml", yaml_content.encode())
+                zf.writestr(f"{model_name.replace('/', '_')}_config.yaml", yaml_content.encode())
 
     zip_buffer.seek(0)
     return zip_buffer, "analysis_data.zip"
 
-
 # --------------------------------------------------------------------
-# PART 2: FULL "DATA START" SNIPPET (RANKS 44–105) + Parser
+# PART 2: THE "DATA START" SNIPPET (RANKS 44–105) + Parser
 # --------------------------------------------------------------------
+# This is your larger dataset, rank = 44..105
 benchmark_data = [
-    # The entire dataset from your "DATA START", rank 44..105
-    # (the code you posted with "knowledge of config" or scraping logic)
     {
         "rank": 44,
         "name": "sometimesanotion/Qwen2.5-14B-Vimarckoso-v3",
@@ -248,21 +280,533 @@ benchmark_data = [
             }
         }
     },
-    # ... rest of the snippet ...
-    # (Exactly copy/paste your big block from rank=44 to rank=105)
+    {
+        "rank": 45,
+        "name": "sthenno-com/miscii-14b-1225",
+        "scores": {
+            "average": 40.08,
+            "IFEval": 78.78,
+            "BBH": 50.91,
+            "MATH": 31.57,
+            "GPQA": 17.00,
+            "MUSR": 14.77,
+            "MMLU-PRO": 47.46
+        },
+        "hf_url": "https://huggingface.co/sthenno-com/miscii-14b-1225",
+        "known_config": None
+    },
+    {
+        "rank": 46,
+        "name": "djuna/Q2.5-Veltha-14B-0.5",
+        "scores": {
+            "average": 39.96,
+            "IFEval": 77.96,
+            "BBH": 50.32,
+            "MATH": 33.84,
+            "GPQA": 15.77,
+            "MUSR": 14.17,
+            "MMLU-PRO": 47.72
+        },
+        "hf_url": "https://huggingface.co/djuna/Q2.5-Veltha-14B-0.5",
+        "known_config": None
+    },
+    {
+        "rank": 48,
+        "name": "sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-model_stock",
+        "scores": {
+            "average": 39.81,
+            "IFEval": 71.62,
+            "BBH": 48.76,
+            "MATH": 33.99,
+            "GPQA": 17.34,
+            "MUSR": 19.23,
+            "MMLU-PRO": 47.95
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-model_stock",
+        "known_config": None
+    },
+    {
+        "rank": 50,
+        "name": "sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-Prose01",
+        "scores": {
+            "average": 39.46,
+            "IFEval": 68.72,
+            "BBH": 47.71,
+            "MATH": 35.05,
+            "GPQA": 18.23,
+            "MUSR": 19.56,
+            "MMLU-PRO": 47.50
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwen2.5-14B-Vimarckoso-v3-Prose01",
+        "known_config": None
+    },
+    {
+        "rank": 52,
+        "name": "arcee-ai/Virtuoso-Small",
+        "scores": {
+            "average": 39.43,
+            "IFEval": 79.35,
+            "BBH": 50.40,
+            "MATH": 34.29,
+            "GPQA": 11.52,
+            "MUSR": 14.44,
+            "MMLU-PRO": 46.57
+        },
+        "hf_url": "https://huggingface.co/arcee-ai/Virtuoso-Small",
+        "known_config": None
+    },
+    {
+        "rank": 54,
+        "name": "sometimesanotion/Qwentinuum-14B-v6",
+        "scores": {
+            "average": 39.23,
+            "IFEval": 63.04,
+            "BBH": 50.23,
+            "MATH": 33.84,
+            "GPQA": 18.23,
+            "MUSR": 21.18,
+            "MMLU-PRO": 48.89
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwentinuum-14B-v6",
+        "known_config": None
+    },
+    {
+        "rank": 55,
+        "name": "djuna/Q2.5-Veltha-14B",
+        "scores": {
+            "average": 39.21,
+            "IFEval": 82.92,
+            "BBH": 49.75,
+            "MATH": 28.02,
+            "GPQA": 14.54,
+            "MUSR": 12.26,
+            "MMLU-PRO": 47.76
+        },
+        "hf_url": "https://huggingface.co/djuna/Q2.5-Veltha-14B",
+        "known_config": None
+    },
+    {
+        "rank": 57,
+        "name": "allknowingroger/QwenSlerp6-14B",
+        "scores": {
+            "average": 39.02,
+            "IFEval": 68.67,
+            "BBH": 47.59,
+            "MATH": 34.14,
+            "GPQA": 16.44,
+            "MUSR": 18.32,
+            "MMLU-PRO": 48.95
+        },
+        "hf_url": "https://huggingface.co/allknowingroger/QwenSlerp6-14B",
+        "known_config": None
+    },
+    {
+        "rank": 58,
+        "name": "allknowingroger/QwenSlerp5-14B",
+        "scores": {
+            "average": 38.94,
+            "IFEval": 71.19,
+            "BBH": 47.39,
+            "MATH": 33.16,
+            "GPQA": 15.32,
+            "MUSR": 17.81,
+            "MMLU-PRO": 48.78
+        },
+        "hf_url": "https://huggingface.co/allknowingroger/QwenSlerp5-14B",
+        "known_config": None
+    },
+    {
+        "rank": 59,
+        "name": "sometimesanotion/Qwentinuum-14B-v5",
+        "scores": {
+            "average": 38.87,
+            "IFEval": 62.86,
+            "BBH": 50.28,
+            "MATH": 31.57,
+            "GPQA": 18.34,
+            "MUSR": 21.09,
+            "MMLU-PRO": 49.09
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwentinuum-14B-v5",
+        "known_config": None
+    },
+    {
+        "rank": 60,
+        "name": "sometimesanotion/Qwenvergence-14B-v6-Prose",
+        "scores": {
+            "average": 38.82,
+            "IFEval": 59.90,
+            "BBH": 50.12,
+            "MATH": 34.89,
+            "GPQA": 18.46,
+            "MUSR": 21.02,
+            "MMLU-PRO": 48.56
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwenvergence-14B-v6-Prose",
+        "known_config": None
+    },
+    {
+        "rank": 61,
+        "name": "CultriX/Qwen2.5-14B-Brocav3",
+        "scores": {
+            "average": 38.76,
+            "IFEval": 69.52,
+            "BBH": 49.05,
+            "MATH": 32.25,
+            "GPQA": 14.54,
+            "MUSR": 19.25,
+            "MMLU-PRO": 47.97
+        },
+        "hf_url": "https://huggingface.co/CultriX/Qwen2.5-14B-Brocav3",
+        "known_config": None
+    },
+    {
+        "rank": 62,
+        "name": "sometimesanotion/Qwentinuum-14B-v7",
+        "scores": {
+            "average": 38.76,
+            "IFEval": 61.09,
+            "BBH": 50.35,
+            "MATH": 33.38,
+            "GPQA": 18.79,
+            "MUSR": 19.95,
+            "MMLU-PRO": 49.00
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwentinuum-14B-v7",
+        "known_config": None
+    },
+    {
+        "rank": 64,
+        "name": "sometimesanotion/Qwentinuum-14B-v3",
+        "scores": {
+            "average": 38.74,
+            "IFEval": 61.58,
+            "BBH": 50.04,
+            "MATH": 32.85,
+            "GPQA": 18.34,
+            "MUSR": 20.62,
+            "MMLU-PRO": 49.03
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwentinuum-14B-v3",
+        "known_config": None
+    },
+    {
+        "rank": 65,
+        "name": "allura-org/TQ2.5-14B-Aletheia-v1",
+        "scores": {
+            "average": 38.74,
+            "IFEval": 75.30,
+            "BBH": 50.88,
+            "MATH": 29.53,
+            "GPQA": 14.99,
+            "MUSR": 14.61,
+            "MMLU-PRO": 47.12
+        },
+        "hf_url": "https://huggingface.co/allura-org/TQ2.5-14B-Aletheia-v1",
+        "known_config": None
+    },
+    {
+        "rank": 66,
+        "name": "qingy2024/Fusion4-14B-Instruct",
+        "scores": {
+            "average": 38.73,
+            "IFEval": 76.49,
+            "BBH": 50.70,
+            "MATH": 33.91,
+            "GPQA": 10.74,
+            "MUSR": 13.97,
+            "MMLU-PRO": 46.60
+        },
+        "hf_url": "https://huggingface.co/qingy2024/Fusion4-14B-Instruct",
+        "known_config": None
+    },
+    {
+        "rank": 68,
+        "name": "CultriX/Qwen2.5-14B-Brocav7",
+        "scores": {
+            "average": 38.52,
+            "IFEval": 67.24,
+            "BBH": 48.91,
+            "MATH": 31.87,
+            "GPQA": 15.66,
+            "MUSR": 20.15,
+            "MMLU-PRO": 47.31
+        },
+        "hf_url": "https://huggingface.co/CultriX/Qwen2.5-14B-Brocav7",
+        "known_config": None
+    },
+    {
+        "rank": 71,
+        "name": "sometimesanotion/Qwentinuum-14B-v6-Prose",
+        "scores": {
+            "average": 38.46,
+            "IFEval": 56.43,
+            "BBH": 50.14,
+            "MATH": 35.57,
+            "GPQA": 18.46,
+            "MUSR": 21.34,
+            "MMLU-PRO": 48.80
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwentinuum-14B-v6-Prose",
+        "known_config": None
+    },
+    {
+        "rank": 76,
+        "name": "CultriX/Qwen2.5-14B-Brocav6",
+        "scores": {
+            "average": 38.32,
+            "IFEval": 69.95,
+            "BBH": 47.82,
+            "MATH": 29.61,
+            "GPQA": 15.66,
+            "MUSR": 18.88,
+            "MMLU-PRO": 47.99
+        },
+        "hf_url": "https://huggingface.co/CultriX/Qwen2.5-14B-Brocav6",
+        "known_config": None
+    },
+    {
+        "rank": 80,
+        "name": "CultriX/SeQwence-14Bv1",
+        "scores": {
+            "average": 38.20,
+            "IFEval": 66.78,
+            "BBH": 47.19,
+            "MATH": 33.53,
+            "GPQA": 14.88,
+            "MUSR": 18.80,
+            "MMLU-PRO": 48.00
+        },
+        "hf_url": "https://huggingface.co/CultriX/SeQwence-14Bv1",
+        "known_config": None
+    },
+    {
+        "rank": 85,
+        "name": "sometimesanotion/Qwentinuum-14B-v013",
+        "scores": {
+            "average": 37.96,
+            "IFEval": 67.11,
+            "BBH": 43.97,
+            "MATH": 33.01,
+            "GPQA": 14.32,
+            "MUSR": 24.99,
+            "MMLU-PRO": 44.34
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwentinuum-14B-v013",
+        "known_config": None
+    },
+    {
+        "rank": 86,
+        "name": "CultriX/Qwen2.5-14B-Wernickev3",
+        "scores": {
+            "average": 37.94,
+            "IFEval": 70.48,
+            "BBH": 44.58,
+            "MATH": 32.78,
+            "GPQA": 14.99,
+            "MUSR": 18.69,
+            "MMLU-PRO": 46.13
+        },
+        "hf_url": "https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev3",
+        "known_config": None
+    },
+    {
+        "rank": 88,
+        "name": "allknowingroger/QwenSlerp4-14B",
+        "scores": {
+            "average": 37.80,
+            "IFEval": 63.28,
+            "BBH": 49.38,
+            "MATH": 30.97,
+            "GPQA": 16.33,
+            "MUSR": 17.59,
+            "MMLU-PRO": 49.28
+        },
+        "hf_url": "https://huggingface.co/allknowingroger/QwenSlerp4-14B",
+        "known_config": None
+    },
+    {
+        "rank": 89,
+        "name": "CultriX/Qwen2.5-14B-Broca",
+        "scores": {
+            "average": 37.72,
+            "IFEval": 56.04,
+            "BBH": 50.03,
+            "MATH": 34.59,
+            "GPQA": 18.23,
+            "MUSR": 18.95,
+            "MMLU-PRO": 48.49
+        },
+        "hf_url": "https://huggingface.co/CultriX/Qwen2.5-14B-Broca",
+        "known_config": None
+    },
+    {
+        "rank": 90,
+        "name": "CultriX/Qwen2.5-14B-Emerged",
+        "scores": {
+            "average": 37.66,
+            "IFEval": 70.00,
+            "BBH": 45.93,
+            "MATH": 30.74,
+            "GPQA": 14.32,
+            "MUSR": 18.47,
+            "MMLU-PRO": 46.51
+        },
+        "hf_url": "https://huggingface.co/CultriX/Qwen2.5-14B-Emerged",
+        "known_config": None
+    },
+    {
+        "rank": 91,
+        "name": "sometimesanotion/Qwentinuum-14B-v8",
+        "scores": {
+            "average": 37.65,
+            "IFEval": 54.12,
+            "BBH": 50.11,
+            "MATH": 34.14,
+            "GPQA": 17.79,
+            "MUSR": 20.75,
+            "MMLU-PRO": 49.02
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwentinuum-14B-v8",
+        "known_config": None
+    },
+    {
+        "rank": 92,
+        "name": "qingy2024/Fusion-14B-Instruct",
+        "scores": {
+            "average": 37.64,
+            "IFEval": 72.60,
+            "BBH": 48.58,
+            "MATH": 30.97,
+            "GPQA": 13.98,
+            "MUSR": 14.81,
+            "MMLU-PRO": 44.93
+        },
+        "hf_url": "https://huggingface.co/qingy2024/Fusion-14B-Instruct",
+        "known_config": None
+    },
+    {
+        "rank": 94,
+        "name": "CultriX/Qwestion-14B",
+        "scores": {
+            "average": 37.63,
+            "IFEval": 63.18,
+            "BBH": 48.76,
+            "MATH": 31.72,
+            "GPQA": 15.77,
+            "MUSR": 17.22,
+            "MMLU-PRO": 49.14
+        },
+        "hf_url": "https://huggingface.co/CultriX/Qwestion-14B",
+        "known_config": None
+    },
+    {
+        "rank": 99,
+        "name": "sometimesanotion/Qwenvergence-14B-v3-Prose",
+        "scores": {
+            "average": 37.37,
+            "IFEval": 49.18,
+            "BBH": 49.80,
+            "MATH": 35.57,
+            "GPQA": 19.35,
+            "MUSR": 21.77,
+            "MMLU-PRO": 48.55
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwenvergence-14B-v3-Prose",
+        "known_config": None
+    },
+    {
+        "rank": 102,
+        "name": "CultriX/SeQwence-14B-v5",
+        "scores": {
+            "average": 37.27,
+            "IFEval": 59.20,
+            "BBH": 50.00,
+            "MATH": 31.04,
+            "GPQA": 16.00,
+            "MUSR": 18.33,
+            "MMLU-PRO": 49.05
+        },
+        "hf_url": "https://huggingface.co/CultriX/SeQwence-14B-v5",
+        "known_config": None
+    },
+    {
+        "rank": 103,
+        "name": "sometimesanotion/Qwen-14B-ProseStock-v4",
+        "scores": {
+            "average": 37.23,
+            "IFEval": 49.42,
+            "BBH": 49.54,
+            "MATH": 35.50,
+            "GPQA": 18.46,
+            "MUSR": 21.70,
+            "MMLU-PRO": 48.74
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/Qwen-14B-ProseStock-v4",
+        "known_config": None
+    },
+    {
+        "rank": 104,
+        "name": "sometimesanotion/IF-reasoning-experiment-40",
+        "scores": {
+            "average": 37.21,
+            "IFEval": 63.30,
+            "BBH": 44.31,
+            "MATH": 27.72,
+            "GPQA": 17.34,
+            "MUSR": 25.86,
+            "MMLU-PRO": 44.72
+        },
+        "hf_url": "https://huggingface.co/sometimesanotion/IF-reasoning-experiment-40",
+        "known_config": None
+    },
+    {
+        "rank": 105,
+        "name": "CultriX/SeQwence-14B-EvolMerge",
+        "scores": {
+            "average": 37.20,
+            "IFEval": 53.82,
+            "BBH": 50.78,
+            "MATH": 31.80,
+            "GPQA": 17.45,
+            "MUSR": 20.26,
+            "MMLU-PRO": 49.10
+        },
+        "hf_url": "https://huggingface.co/CultriX/SeQwence-14B-EvolMerge",
+        "known_config": None
+    }
 ]
 
-
 def snippet_scrape_model_page(url):
     """
-    Same as scrape_model_page, but we keep it separate for clarity.
+    Equivalent scraping function for the larger dataset
+    to look for <pre> YAML and a .metadata section.
     """
-    return scrape_model_page(url)
+    try:
+        response = requests.get(url)
+        if response.status_code != 200:
+            return f"Error: Unable to fetch the page (Status Code: {response.status_code})"
+        
+        soup = BeautifulSoup(response.text, "html.parser")
+
+        yaml_config = soup.find("pre")
+        yaml_text = yaml_config.text.strip() if yaml_config else "No YAML configuration found."
+
+        metadata_section = soup.find("div", class_="metadata")
+        metadata_text = metadata_section.text.strip() if metadata_section else "No metadata found."
+
+        return {
+            "yaml_configuration": yaml_text,
+            "metadata": metadata_text
+        }
+
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 def snippet_print_benchmark_and_config_info(model_info):
     """
-    Prints an overview for each model (your "DATA START" logic), 
-    either known config or scraping snippet.
+    Prints an overview for each model in the rank=44..105 dataset.
+    If known_config is not None, prints it. Otherwise attempts to scrape.
     """
     print(f"---\nModel Rank: {model_info['rank']}")
     print(f"Model Name: {model_info['name']}")
@@ -274,7 +818,7 @@ def snippet_print_benchmark_and_config_info(model_info):
     print(f"Models average score in MUSR benchmarks in %: {model_info['scores']['MUSR']}")
     print(f"Models average score in MMLU-PRO benchmarks in %: {model_info['scores']['MMLU-PRO']}")
 
-    # If there's a known_config, print it as YAML
+    # If there's a known_config, print it in YAML form and stop.
     if model_info["known_config"] is not None:
         print("###")
         print("models:")
@@ -284,25 +828,22 @@ def snippet_print_benchmark_and_config_info(model_info):
         print(f"base_model: {model_info['known_config']['base_model']}")
         print(f"dtype: {model_info['known_config']['dtype']}")
         print("parameters:")
-        print(f"  t: {model_info['known_config']['parameters']['t']} # V shaped curve: Hermes for input & output, WizardMath in the middle layers")
+        t_vals = model_info["known_config"]["parameters"]["t"]
+        print(f"  t: {t_vals} # V shaped curve: Hermes for input & output, WizardMath in the middle layers")
         print("###")
         return
 
-    # Otherwise, scrape
+    # Otherwise, do scraping:
     scraped = snippet_scrape_model_page(model_info["hf_url"])
     if isinstance(scraped, str):
         # Means it's an error string or something
-        if "Error:" in scraped:
-            print("(No MergeKit configuration found or error occurred.)\n")
-            # optionally print snippet
-        else:
-            print(scraped)
+        print("(No MergeKit configuration found or scraping error.)")
+        print(scraped)
         return
     else:
-        # It's presumably a dict: { "yaml_configuration": "...", "metadata": "..." }
-        if ("No YAML configuration found." in scraped["yaml_configuration"]):
+        # It's presumably a dict
+        if "No YAML configuration found." in scraped["yaml_configuration"]:
             print("(No MergeKit configuration found.)\n")
-            # Print your snippet code
             print("You can try the following Python script to scrape the model page:\n")
             print("#" * 70)
             print(f'''import requests
@@ -336,14 +877,15 @@ if __name__ == "__main__":
     print(result)''')
             print("#" * 70)
         else:
+            # Found some YAML
             print("###")
             print(scraped["yaml_configuration"])
             print("###")
 
 def run_non_tiny_benchmarks():
     """
-    Captures the stdout from printing each model in benchmark_data 
-    (ranks 44 to 105), returning a single string for Gradio to display.
+    Captures the stdout from printing each model in benchmark_data (ranks 44..105),
+    returning the entire output as a single string for Gradio to display.
     """
     old_stdout = sys.stdout
     buffer = io.StringIO()
@@ -355,14 +897,13 @@ def run_non_tiny_benchmarks():
     sys.stdout = old_stdout
     return buffer.getvalue()
 
-
 # --------------------------------------------------------------------
-# PART 3: GRADIO APP (Your existing UI plus the "Parse Non-Tiny" button)
+# PART 3: The Gradio App
 # --------------------------------------------------------------------
 with gr.Blocks() as demo:
     gr.Markdown("# Comprehensive Model Performance Analysis with Hugging Face Links")
 
-    # The existing UI
+    # The existing UI for the “tiny” data
     with gr.Row():
         btn1 = gr.Button("Show Average Performance")
         img1 = gr.Image(type="pil", label="Average Performance Plot")
@@ -387,6 +928,7 @@ with gr.Blocks() as demo:
         heatmap_download = gr.File(label="Download Heatmap")
         btn4.click(plot_heatmap, outputs=[heatmap_img, heatmap_download])
 
+    # Scraping & YAML handling for the *tiny* table
     with gr.Row():
         model_selector = gr.Dropdown(choices=df_full["Model Configuration"].tolist(), label="Select a Model")
         with gr.Column():
@@ -398,12 +940,13 @@ with gr.Blocks() as demo:
             yaml_download = gr.File(label="Download MergeKit Configuration")
             save_yaml_btn.click(download_yaml, inputs=[yaml_output, model_selector], outputs=yaml_download)
 
+    # Download everything (CSV, plots, any found YAML)
     with gr.Row():
         download_all_btn = gr.Button("Download Everything")
         all_downloads = gr.File(label="Download All Data")
         download_all_btn.click(download_all_data, outputs=all_downloads)
         
-    # Live scraping feature
+    # Live Scraping
     gr.Markdown("## Live Scraping Features")
     with gr.Row():
         url_input = gr.Textbox(label="Enter Hugging Face Model URL", placeholder="https://huggingface.co/<model>")
@@ -411,11 +954,11 @@ with gr.Blocks() as demo:
         live_scrape_output = gr.Textbox(label="Scraped Data", lines=15)
         live_scrape_btn.click(display_scraped_model_data, inputs=url_input, outputs=live_scrape_output)
 
-    # NEW: Non-Tiny Benchmarks button
+    # Non-Tiny Benchmarks
     gr.Markdown("## Non-Tiny Benchmark Parser (Ranks 44–105)")
     with gr.Row():
         parse_non_tiny_btn = gr.Button("Parse Non-Tiny Benchmarks")
         parse_non_tiny_output = gr.Textbox(label="Non-Tiny Benchmark Output", lines=30)
         parse_non_tiny_btn.click(fn=run_non_tiny_benchmarks, outputs=parse_non_tiny_output)
 
-demo.launch()
+demo.launch()