Update app.py

app.py

@@ -1,3 +1,6 @@
+# Comprehensive Model Performance Analysis
+
+# Importing Required Libraries
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
@@ -10,8 +13,8 @@ import base64
 import zipfile
 from PIL import Image
 from io import BytesIO
-import tempfile
 
+# Input Data
 # Input data with links to Hugging Face repositories
 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],
@@ -40,17 +43,14 @@ data_full = [
     ['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],
 ]
-
 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
+# Visualization and Analytics Functions
+# 1. Plot Average Scores
 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)
@@ -59,27 +59,15 @@ def plot_average_scores():
     plt.gca().invert_yaxis()
     plt.grid(axis='x', linestyle='--', alpha=0.7)
     plt.tight_layout()
-    
-    img_buffer = io.BytesIO()
-    plt.savefig(img_buffer, format='png')
-    img_buffer.seek(0)
-    img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8')
-    plt.close()
-
-    pil_image = Image.open(BytesIO(base64.b64decode(img_base64)))
-
-    temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    pil_image.save(temp_image_file.name)
-    return pil_image, temp_image_file.name
+    plt.show()
 
+# 2. Plot Task Performance
 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)
@@ -87,24 +75,13 @@ def plot_task_performance():
     plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=9)
     plt.grid(axis='y', linestyle='--', alpha=0.7)
     plt.tight_layout()
-    
-    img_buffer = io.BytesIO()
-    plt.savefig(img_buffer, format='png')
-    img_buffer.seek(0)
-    img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8')
-    plt.close()
-
-    pil_image = Image.open(BytesIO(base64.b64decode(img_base64)))
-    temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    pil_image.save(temp_image_file.name)
-    return pil_image, temp_image_file.name
+    plt.show()
 
+# 3. Plot Task-Specific Top Models
 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)
@@ -112,181 +89,68 @@ def plot_task_specific_top_models():
     plt.ylabel("Score", fontsize=14)
     plt.grid(axis="y", linestyle="--", alpha=0.7)
     plt.tight_layout()
+    plt.show()
 
-    img_buffer = io.BytesIO()
-    plt.savefig(img_buffer, format='png')
-    img_buffer.seek(0)
-    img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8')
-    plt.close()
-    pil_image = Image.open(BytesIO(base64.b64decode(img_base64)))
-    temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    pil_image.save(temp_image_file.name)
-    return pil_image, temp_image_file.name
-
+# YAML Configuration and Scraping Utilities
+# 1. Scrape MergeKit Configuration
 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[:, 2:], annot=True, cmap="YlGnBu", xticklabels=columns[2:], yticklabels=df_full["Model Configuration"])
-    plt.title("Performance Heatmap", fontsize=16)
-    plt.tight_layout()
-    
-    img_buffer = io.BytesIO()
-    plt.savefig(img_buffer, format='png')
-    img_buffer.seek(0)
-    img_base64 = base64.b64encode(img_buffer.read()).decode('utf-8')
-    plt.close()
-    pil_image = Image.open(BytesIO(base64.b64decode(img_base64)))
-    temp_image_file = tempfile.NamedTemporaryFile(suffix=".png", delete=False)
-    pil_image.save(temp_image_file.name)
-    return pil_image, temp_image_file.name
-
-def download_yaml(yaml_content, model_name):
-    """
-    Generates a downloadable link for the scraped YAML content.
-    """
-    if "No YAML configuration found" in yaml_content or "Failed to fetch model page" in yaml_content:
-        return None  # Do not return a link if there's no config or a fetch error
-    
-    filename = f"{model_name.replace('/', '_')}_config.yaml"
-    return gr.File(value=yaml_content.encode(), filename=filename)
+    yaml_config = soup.find("pre")
+    return yaml_config.text.strip() if yaml_config else f"No YAML configuration found for {model_name}."
 
+# 2. Download All Data
 def download_all_data():
-    # Prepare data to download
     csv_buffer = io.StringIO()
     df_full.to_csv(csv_buffer, index=False)
     csv_data = csv_buffer.getvalue().encode('utf-8')
-    
-    # Prepare all plots
-    average_plot_pil, average_plot_name = plot_average_scores()
-    task_plot_pil, task_plot_name = plot_task_performance()
-    top_models_plot_pil, top_models_plot_name = plot_task_specific_top_models()
-    heatmap_plot_pil, heatmap_plot_name = plot_heatmap()
-
-    plot_dict = {
-        "average_performance": (average_plot_pil, average_plot_name),
-        "task_performance": (task_plot_pil, task_plot_name),
-        "top_models": (top_models_plot_pil, top_models_plot_name),
-        "heatmap": (heatmap_plot_pil, heatmap_plot_name)
-    }
 
     zip_buffer = io.BytesIO()
     with zipfile.ZipFile(zip_buffer, 'w') as zf:
         zf.writestr("model_scores.csv", csv_data)
-
-        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())
-
-        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())
-
     zip_buffer.seek(0)
-    
-    return zip_buffer, "analysis_data.zip"
-
-def scrape_model_page(model_url):
-    """
-    Scrapes the Hugging Face model page for YAML configuration and other details.
-    """
-    try:
-        # Fetch the model page
-        response = requests.get(model_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")
 
-        # Extract YAML configuration (usually inside <pre> tags)
-        yaml_config = soup.find("pre")
-        yaml_text = yaml_config.text.strip() if yaml_config else "No YAML configuration found."
-
-        # Extract additional metadata or performance (if available)
-        metadata_section = soup.find("div", class_="metadata")
-        metadata_text = metadata_section.text.strip() if metadata_section else "No metadata found."
-
-        # Return the scraped details
-        return f"**YAML Configuration:**\n{yaml_text}\n\n**Metadata:**\n{metadata_text}"
-
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-def display_scraped_model_data(model_url):
-    """
-    Displays YAML configuration and metadata for a given model URL.
-    """
-    return scrape_model_page(model_url)
+    return zip_buffer, "analysis_data.zip"
 
+# Performance Heatmap
+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.show()
 
-# Gradio app
+# Gradio App
+# Building the Interface
 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="pil", label="Average Performance Plot")
-        img1_download = gr.File(label="Download Average Performance")
-        btn1.click(plot_average_scores, outputs=[img1,img1_download])
-        
+        btn1.click(plot_average_scores, outputs=[img1])
+
     with gr.Row():
         btn2 = gr.Button("Show Task Performance")
         img2 = gr.Image(type="pil", label="Task Performance Plot")
-        img2_download = gr.File(label="Download Task Performance")
-        btn2.click(plot_task_performance, outputs=[img2, img2_download])
+        btn2.click(plot_task_performance, outputs=[img2])
 
     with gr.Row():
         btn3 = gr.Button("Task-Specific Top Models")
         img3 = gr.Image(type="pil", label="Task-Specific Top Models Plot")
-        img3_download = gr.File(label="Download Top Models")
-        btn3.click(plot_task_specific_top_models, outputs=[img3, img3_download])
-    
-    with gr.Row():
-      btn4 = gr.Button("Plot Performance Heatmap")
-      heatmap_img = gr.Image(type="pil", label="Performance Heatmap")
-      heatmap_download = gr.File(label="Download Heatmap")
-      btn4.click(plot_heatmap, outputs=[heatmap_img, heatmap_download])
+        btn3.click(plot_task_specific_top_models, outputs=[img3])
 
     with gr.Row():
-        model_selector = gr.Dropdown(choices=df_full["Model Configuration"].tolist(), label="Select a Model")
-        with gr.Column():
-          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)
-        with gr.Column():
-          save_yaml_btn = gr.Button("Save MergeKit Configuration")
-          yaml_download = gr.File(label="Download MergeKit Configuration")
-          save_yaml_btn.click(download_yaml, inputs=[yaml_output, model_selector], outputs=yaml_download)
-
+        btn4 = gr.Button("Plot Performance Heatmap")
+        img4 = gr.Image(type="pil", label="Performance Heatmap")
+        btn4.click(plot_heatmap, outputs=[img4])
 
     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
-    gr.Markdown("## Live Scraping Features")
-    with gr.Row():
-        url_input = gr.Textbox(label="Enter Hugging Face Model URL", placeholder="https://huggingface.co/<model>")
-        live_scrape_btn = gr.Button("Scrape Model Page")
-        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)
-
 
 demo.launch()