Update app.py

app.py

@@ -32,6 +32,7 @@ def format_stars(score):
         score_int = int(score)
     except Exception:
         score_int = 0
+    # Render stars in black with a slightly larger font.
     return f'<span style="color: black; font-size:1.5em;">{"★" * score_int}</span>'
 
 def make_link(mname):
@@ -39,53 +40,94 @@ def make_link(mname):
     display_name = parts[1] if len(parts) > 1 else mname
     return f'<a href="https://huggingface.co/{mname}" target="_blank">{display_name}</a>'
 
+def extract_link_text(html_link):
+    """Extracts the inner text from an HTML link."""
+    start = html_link.find('>') + 1
+    end = html_link.rfind('</a>')
+    if start > 0 and end > start:
+        return html_link[start:end]
+    else:
+        return html_link
+
 def generate_html_table_from_df(df):
     """
-    Generates an HTML table with tooltips for column headers.
+    Given a dataframe with a numeric energy column (gpu_energy_numeric),
+    generate an HTML table with three columns:
+      - Model (the link, with a fixed width based on the longest model name)
+      - GPU Energy (Wh) plus a horizontal bar whose width is proportional
+        to the energy value relative to the maximum in the table.
+      - Score (displayed as stars)
     """
+    # Compute a static width (in pixels) for the Model column based on the longest model name.
+    if not df.empty:
+        max_length = max(len(extract_link_text(link)) for link in df['Model'])
+    else:
+        max_length = 10
+    # Multiply by an estimated average character width (10 pixels) and add some extra padding.
+    static_width = max_length * 10 + 16
+
     max_energy = df['gpu_energy_numeric'].max() if not df.empty else 1
     color_map = {"1": "black", "2": "black", "3": "black", "4": "black", "5": "black"}
-    
     html = '<table style="width:100%; border-collapse: collapse; font-family: Inter, sans-serif;">'
+    # Keep only one header (the one with hover text)
     html += '<thead><tr style="background-color: #f2f2f2;">'
     html += '<th style="text-align: left; padding: 8px;" title="Model name with link to Hugging Face">Model</th>'
     html += '<th style="text-align: left; padding: 8px;" title="GPU energy consumed in Watt-hours for 1,000 queries">GPU Energy (Wh)</th>'
-    html += '<th style="text-align: left; padding: 8px;" title="5 is most efficient, 1 is least. Relative energy efficiency score at launch">Score</th>'
+    html += '<th style="text-align: left; padding: 8px;" title="5 is most efficient, 1 is least. Relative energy efficiency score relative to other models in task/class at the time of leaderboard launch">Score</th>'
     html += '</tr></thead>'
     html += '<tbody>'
-    
     for _, row in df.iterrows():
         energy_numeric = row['gpu_energy_numeric']
-        energy_str = f"{energy_numeric:.2f}"  # Display GPU energy with 2 decimal places
+        energy_str = f"{energy_numeric:.2f}"
+        # Compute the relative width (as a percentage)
         bar_width = (energy_numeric / max_energy) * 100
         score_val = row['energy_score']
         bar_color = color_map.get(str(score_val), "gray")
-        
         html += '<tr>'
-        html += f'<td style="padding: 8px;">{row["Model"]}</td>'
-        html += f'<td style="padding: 8px;">{energy_str}<br>'
-        html += f'<div style="background-color: {bar_color}; width: {bar_width:.1f}%; height: 10px;"></div></td>'
+        html += f'<td style="padding: 8px; width: {static_width}px;">{row["Model"]}</td>'
+        html += (
+            f'<td style="padding: 8px;">{energy_str}<br>'
+            f'<div style="background-color: {bar_color}; width: {bar_width:.1f}%; height: 10px;"></div></td>'
+        )
         html += f'<td style="padding: 8px;">{row["Score"]}</td>'
         html += '</tr>'
-    
     html += '</tbody></table>'
     return html
 
+# --- Function to zip all CSV files ---
+def zip_csv_files():
+    data_dir = "data/energy"
+    zip_filename = "data.zip"
+    with zipfile.ZipFile(zip_filename, "w", zipfile.ZIP_DEFLATED) as zipf:
+        for filename in os.listdir(data_dir):
+            if filename.endswith(".csv"):
+                filepath = os.path.join(data_dir, filename)
+                zipf.write(filepath, arcname=filename)
+    return zip_filename
+
+def get_zip_data_link():
+    """Creates a data URI download link for the ZIP file."""
+    zip_filename = zip_csv_files()
+    with open(zip_filename, "rb") as f:
+        data = f.read()
+    b64 = base64.b64encode(data).decode()
+    href = f'<a href="data:application/zip;base64,{b64}" download="data.zip" style="margin: 0 15px; text-decoration: none; font-weight: bold; font-size: 1.1em;">Download Data</a>'
+    return href
+
+# --- Modified functions to include a sort_order parameter ---
 def get_model_names_html(task, sort_order="Low to High"):
     df = pd.read_csv('data/energy/' + task)
     if df.columns[0].startswith("Unnamed:"):
         df = df.iloc[:, 1:]
     df['energy_score'] = df['energy_score'].astype(int)
+    # Convert kWh to Wh:
     df['gpu_energy_numeric'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
     df['Model'] = df['model'].apply(make_link)
     df['Score'] = df['energy_score'].apply(format_stars)
-    ascending = True  # Always default to Low to High
+    ascending = True if sort_order == "Low to High" else False
     df = df.sort_values(by='gpu_energy_numeric', ascending=ascending)
     return generate_html_table_from_df(df)
 
-def update_all_tasks(sort_order):
-    return get_all_model_names_html(sort_order)
-
 def get_all_model_names_html(sort_order="Low to High"):
     all_df = pd.DataFrame()
     for task in tasks:
@@ -98,11 +140,69 @@ def get_all_model_names_html(sort_order="Low to High"):
         df['Score'] = df['energy_score'].apply(format_stars)
         all_df = pd.concat([all_df, df], ignore_index=True)
     all_df = all_df.drop_duplicates(subset=['model'])
-    ascending = True  # Default to Low to High
+    ascending = True if sort_order == "Low to High" else False
     all_df = all_df.sort_values(by='gpu_energy_numeric', ascending=ascending)
     return generate_html_table_from_df(all_df)
 
+def get_text_generation_model_names_html(model_class, sort_order="Low to High"):
+    df = pd.read_csv('data/energy/text_generation.csv')
+    if df.columns[0].startswith("Unnamed:"):
+        df = df.iloc[:, 1:]
+    if 'class' in df.columns:
+        df = df[df['class'] == model_class]
+    df['energy_score'] = df['energy_score'].astype(int)
+    df['gpu_energy_numeric'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
+    df['Model'] = df['model'].apply(make_link)
+    df['Score'] = df['energy_score'].apply(format_stars)
+    ascending = True if sort_order == "Low to High" else False
+    df = df.sort_values(by='gpu_energy_numeric', ascending=ascending)
+    return generate_html_table_from_df(df)
+
+# --- Update functions for dropdown changes ---
+
+# For Text Generation, two dropdowns: model class and sort order.
+def update_text_generation(selected_display, sort_order):
+    mapping = {
+        "A (Single Consumer GPU) <20B parameters": "A",
+        "B (Single Cloud GPU) 20-66B parameters": "B",
+        "C (Multiple Cloud GPUs) >66B parameters": "C"
+    }
+    model_class = mapping.get(selected_display, "A")
+    return get_text_generation_model_names_html(model_class, sort_order)
+
+# For the other tabs, each update function simply takes the sort_order.
+def update_image_generation(sort_order):
+    return get_model_names_html('image_generation.csv', sort_order)
+
+def update_text_classification(sort_order):
+    return get_model_names_html('text_classification.csv', sort_order)
+
+def update_image_classification(sort_order):
+    return get_model_names_html('image_classification.csv', sort_order)
+
+def update_image_captioning(sort_order):
+    return get_model_names_html('image_captioning.csv', sort_order)
+
+def update_summarization(sort_order):
+    return get_model_names_html('summarization.csv', sort_order)
+
+def update_asr(sort_order):
+    return get_model_names_html('asr.csv', sort_order)
+
+def update_object_detection(sort_order):
+    return get_model_names_html('object_detection.csv', sort_order)
+
+def update_sentence_similarity(sort_order):
+    return get_model_names_html('sentence_similarity.csv', sort_order)
+
+def update_extractive_qa(sort_order):
+    return get_model_names_html('question_answering.csv', sort_order)
+
+def update_all_tasks(sort_order):
+    return get_all_model_names_html(sort_order)
+
 # --- Build the Gradio Interface ---
+
 demo = gr.Blocks(css="""
 .gr-dataframe table {
     table-layout: fixed;
@@ -117,10 +217,134 @@ demo = gr.Blocks(css="""
 """)
 
 with demo:
-    gr.HTML('<div style="text-align: center;"><img src="logo.png" alt="Logo" style="width: 200px;"></div>')
-    gr.HTML('<div style="text-align: center; font-size: 1.2em;">Welcome to the AI Energy Score Leaderboard</div>')
+    # Replace title with a centered logo and a centered subtitle.
+    gr.HTML('<div style="text-align: center;"><img src="logo.png" alt="Logo"></div>')
+    gr.Markdown('<p style="text-align: center;">Welcome to the leaderboard for the <a href="https://huggingface.co/AIEnergyScore">AI Energy Score Project!</a> — Select different tasks to see scored models.</p>')
+    
+    # Header links (using a row of components, including a Download Data link)
+    with gr.Row():
+        submission_link = gr.HTML('<a href="https://huggingface.co/spaces/AIEnergyScore/submission_portal" style="margin: 0 10px; text-decoration: none; font-weight: bold; font-size: 1.1em;">Submission Portal</a>')
+        label_link = gr.HTML('<a href="https://huggingface.co/spaces/AIEnergyScore/Label" style="margin: 0 10px; text-decoration: none; font-weight: bold; font-size: 1.1em;">Label Generator</a>')
+        faq_link = gr.HTML('<a href="https://huggingface.github.io/AIEnergyScore/#faq" style="margin: 0 10px; text-decoration: none; font-weight: bold; font-size: 1.1em;">FAQ</a>')
+        documentation_link = gr.HTML('<a href="https://huggingface.github.io/AIEnergyScore/#documentation" style="margin: 0 10px; text-decoration: none; font-weight: bold; font-size: 1.1em;">Documentation</a>')
+        download_link = gr.HTML(get_zip_data_link())
+        community_link = gr.HTML('<a href="https://huggingface.co/spaces/AIEnergyScore/README/discussions" style="margin: 0 10px; text-decoration: none; font-weight: bold; font-size: 1.1em;">Community</a>')
     
     with gr.Tabs():
+        # --- Text Generation Tab ---
+        with gr.TabItem("Text Generation 💬"):
+            with gr.Row():
+                model_class_options = [
+                    "A (Single Consumer GPU) <20B parameters",
+                    "B (Single Cloud GPU) 20-66B parameters",
+                    "C (Multiple Cloud GPUs) >66B parameters"
+                ]
+                model_class_dropdown = gr.Dropdown(
+                    choices=model_class_options,
+                    label="Select Model Class",
+                    value=model_class_options[0]
+                )
+                sort_dropdown_tg = gr.Dropdown(
+                    choices=["Low to High", "High to Low"],
+                    label="Sort",
+                    value="Low to High"
+                )
+            tg_table = gr.HTML(get_text_generation_model_names_html("A", "Low to High"))
+            # When either dropdown changes, update the table.
+            model_class_dropdown.change(fn=update_text_generation, inputs=[model_class_dropdown, sort_dropdown_tg], outputs=tg_table)
+            sort_dropdown_tg.change(fn=update_text_generation, inputs=[model_class_dropdown, sort_dropdown_tg], outputs=tg_table)
+        
+        # --- Image Generation Tab ---
+        with gr.TabItem("Image Generation 📷"):
+            sort_dropdown_img = gr.Dropdown(
+                choices=["Low to High", "High to Low"],
+                label="Sort",
+                value="Low to High"
+            )
+            img_table = gr.HTML(get_model_names_html('image_generation.csv', "Low to High"))
+            sort_dropdown_img.change(fn=update_image_generation, inputs=sort_dropdown_img, outputs=img_table)
+        
+        # --- Text Classification Tab ---
+        with gr.TabItem("Text Classification 🎭"):
+            sort_dropdown_tc = gr.Dropdown(
+                choices=["Low to High", "High to Low"],
+                label="Sort",
+                value="Low to High"
+            )
+            tc_table = gr.HTML(get_model_names_html('text_classification.csv', "Low to High"))
+            sort_dropdown_tc.change(fn=update_text_classification, inputs=sort_dropdown_tc, outputs=tc_table)
+        
+        # --- Image Classification Tab ---
+        with gr.TabItem("Image Classification 🖼️"):
+            sort_dropdown_ic = gr.Dropdown(
+                choices=["Low to High", "High to Low"],
+                label="Sort",
+                value="Low to High"
+            )
+            ic_table = gr.HTML(get_model_names_html('image_classification.csv', "Low to High"))
+            sort_dropdown_ic.change(fn=update_image_classification, inputs=sort_dropdown_ic, outputs=ic_table)
+        
+        # --- Image Captioning Tab ---
+        with gr.TabItem("Image Captioning 📝"):
+            sort_dropdown_icap = gr.Dropdown(
+                choices=["Low to High", "High to Low"],
+                label="Sort",
+                value="Low to High"
+            )
+            icap_table = gr.HTML(get_model_names_html('image_captioning.csv', "Low to High"))
+            sort_dropdown_icap.change(fn=update_image_captioning, inputs=sort_dropdown_icap, outputs=icap_table)
+        
+        # --- Summarization Tab ---
+        with gr.TabItem("Summarization 📃"):
+            sort_dropdown_sum = gr.Dropdown(
+                choices=["Low to High", "High to Low"],
+                label="Sort",
+                value="Low to High"
+            )
+            sum_table = gr.HTML(get_model_names_html('summarization.csv', "Low to High"))
+            sort_dropdown_sum.change(fn=update_summarization, inputs=sort_dropdown_sum, outputs=sum_table)
+        
+        # --- Automatic Speech Recognition Tab ---
+        with gr.TabItem("Automatic Speech Recognition 💬"):
+            sort_dropdown_asr = gr.Dropdown(
+                choices=["Low to High", "High to Low"],
+                label="Sort",
+                value="Low to High"
+            )
+            asr_table = gr.HTML(get_model_names_html('asr.csv', "Low to High"))
+            sort_dropdown_asr.change(fn=update_asr, inputs=sort_dropdown_asr, outputs=asr_table)
+        
+        # --- Object Detection Tab ---
+        with gr.TabItem("Object Detection 🚘"):
+            sort_dropdown_od = gr.Dropdown(
+                choices=["Low to High", "High to Low"],
+                label="Sort",
+                value="Low to High"
+            )
+            od_table = gr.HTML(get_model_names_html('object_detection.csv', "Low to High"))
+            sort_dropdown_od.change(fn=update_object_detection, inputs=sort_dropdown_od, outputs=od_table)
+        
+        # --- Sentence Similarity Tab ---
+        with gr.TabItem("Sentence Similarity 📚"):
+            sort_dropdown_ss = gr.Dropdown(
+                choices=["Low to High", "High to Low"],
+                label="Sort",
+                value="Low to High"
+            )
+            ss_table = gr.HTML(get_model_names_html('sentence_similarity.csv', "Low to High"))
+            sort_dropdown_ss.change(fn=update_sentence_similarity, inputs=sort_dropdown_ss, outputs=ss_table)
+        
+        # --- Extractive QA Tab ---
+        with gr.TabItem("Extractive QA ❔"):
+            sort_dropdown_qa = gr.Dropdown(
+                choices=["Low to High", "High to Low"],
+                label="Sort",
+                value="Low to High"
+            )
+            qa_table = gr.HTML(get_model_names_html('question_answering.csv', "Low to High"))
+            sort_dropdown_qa.change(fn=update_extractive_qa, inputs=sort_dropdown_qa, outputs=qa_table)
+        
+        # --- All Tasks Tab ---
         with gr.TabItem("All Tasks 💡"):
             sort_dropdown_all = gr.Dropdown(
                 choices=["Low to High", "High to Low"],
@@ -140,4 +364,4 @@ with demo:
         )
     gr.Markdown("""Last updated: February 2025""")
 
-demo.launch()
+demo.launch()