Create app.py

app.py

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+import gradio as gr
+# from dotenv import load_dotenv
+import os
+from huggingface_hub import hf_hub_download
+import pandas as pd
+import sqlite3
+
+# load_dotenv()
+
+DB_DATASET_ID = os.getenv("DB_DATASET_ID")
+DB_NAME = os.getenv("DB_NAME")
+
+cache_path = hf_hub_download(repo_id=DB_DATASET_ID, repo_type='dataset', filename=DB_NAME, token=os.getenv("HF_TOKEN"))
+
+# Model name mappings and metadata
+
+closed_source = [
+    'ElevenLabs',
+    'Play.HT 2.0',
+    'Play.HT 3.0 Mini',
+    'PlayDialog',
+    'Papla P1',
+    'Hume Octave'
+]
+
+# Model name mapping, can include models that users cannot vote on
+model_names = {
+    'styletts2': 'StyleTTS 2',
+    'tacotron': 'Tacotron',
+    'tacotronph': 'Tacotron Phoneme',
+    'tacotrondca': 'Tacotron DCA',
+    'speedyspeech': 'Speedy Speech',
+    'overflow': 'Overflow TTS',
+    'anonymoussparkle': 'Anonymous Sparkle',
+    'vits': 'VITS',
+    'vitsneon': 'VITS Neon',
+    'neuralhmm': 'Neural HMM',
+    'glow': 'Glow TTS',
+    'fastpitch': 'FastPitch',
+    'jenny': 'Jenny',
+    'tortoise': 'Tortoise TTS',
+    'xtts2': 'Coqui XTTSv2',
+    'xtts': 'Coqui XTTS',
+    'openvoice': 'MyShell OpenVoice',
+    'elevenlabs': 'ElevenLabs',
+    'openai': 'OpenAI',
+    'hierspeech': 'HierSpeech++',
+    'pheme': 'PolyAI Pheme',
+    'speecht5': 'SpeechT5',
+    'metavoice': 'MetaVoice-1B',
+}
+model_links = {
+    'ElevenLabs': 'https://elevenlabs.io/',
+    'Play.HT 2.0': 'https://play.ht/',
+    'Play.HT 3.0 Mini': 'https://play.ht/',
+    'XTTSv2': 'https://huggingface.co/coqui/XTTS-v2',
+    'MeloTTS': 'https://github.com/myshell-ai/MeloTTS',
+    'StyleTTS 2': 'https://github.com/yl4579/StyleTTS2',
+    'Parler TTS Large': 'https://github.com/huggingface/parler-tts',
+    'Parler TTS': 'https://github.com/huggingface/parler-tts',
+    'Fish Speech v1.5': 'https://github.com/fishaudio/fish-speech',
+    'Fish Speech v1.4': 'https://github.com/fishaudio/fish-speech',
+    'GPT-SoVITS': 'https://github.com/RVC-Boss/GPT-SoVITS',
+    'WhisperSpeech': 'https://github.com/WhisperSpeech/WhisperSpeech',
+    'VoiceCraft 2.0': 'https://github.com/jasonppy/VoiceCraft',
+    'PlayDialog': 'https://play.ht/',
+    'Kokoro v0.19': 'https://huggingface.co/hexgrad/Kokoro-82M',
+    'Kokoro v1.0': 'https://huggingface.co/hexgrad/Kokoro-82M',
+    'CosyVoice 2.0': 'https://github.com/FunAudioLLM/CosyVoice',
+    'MetaVoice': 'https://github.com/metavoiceio/metavoice-src',
+    'OpenVoice': 'https://github.com/myshell-ai/OpenVoice',
+    'OpenVoice V2': 'https://github.com/myshell-ai/OpenVoice',
+    'Pheme': 'https://github.com/PolyAI-LDN/pheme',
+    'Vokan TTS': 'https://huggingface.co/ShoukanLabs/Vokan',
+    'Papla P1': 'https://papla.media',
+    'Hume Octave': 'https://www.hume.ai'
+}
+
+
+def get_db():
+    conn = sqlite3.connect(cache_path)
+    return conn
+
+def get_leaderboard(reveal_prelim=False, hide_battle_votes=False, sort_by_elo=True, hide_proprietary=False):
+    conn = get_db()
+    cursor = conn.cursor()
+    
+    if hide_battle_votes:
+        sql = '''
+        SELECT m.name, 
+               SUM(CASE WHEN v.username NOT LIKE '%_battle' AND v.vote = 1 THEN 1 ELSE 0 END) as upvote, 
+               SUM(CASE WHEN v.username NOT LIKE '%_battle' AND v.vote = -1 THEN 1 ELSE 0 END) as downvote
+        FROM model m
+        LEFT JOIN vote v ON m.name = v.model
+        GROUP BY m.name
+        '''
+    else:
+        sql = '''
+        SELECT name, 
+               SUM(CASE WHEN vote = 1 THEN 1 ELSE 0 END) as upvote, 
+               SUM(CASE WHEN vote = -1 THEN 1 ELSE 0 END) as downvote
+        FROM model
+        LEFT JOIN vote ON model.name = vote.model
+        GROUP BY name
+        '''
+    
+    cursor.execute(sql)
+    data = cursor.fetchall()
+    df = pd.DataFrame(data, columns=['name', 'upvote', 'downvote'])
+    df['name'] = df['name'].replace(model_names).replace('Anonymous Sparkle', 'Fish Speech v1.5')
+    
+    # Calculate total votes and win rate
+    df['votes'] = df['upvote'] + df['downvote']
+    df['win_rate'] = (df['upvote'] / df['votes'] * 100).round(1)
+    
+    # Remove models with no votes
+    df = df[df['votes'] > 0]
+
+    # Filter out rows with insufficient votes if not revealing preliminary results
+    if not reveal_prelim:
+        df = df[df['votes'] > 500]
+
+    ## Calculate ELO SCORE (kept as secondary metric)
+    df['elo'] = 1200
+    for i in range(len(df)):
+        for j in range(len(df)):
+            if i != j:
+                try:
+                    expected_a = 1 / (1 + 10 ** ((df['elo'].iloc[j] - df['elo'].iloc[i]) / 400))
+                    expected_b = 1 / (1 + 10 ** ((df['elo'].iloc[i] - df['elo'].iloc[j]) / 400))
+                    actual_a = df['upvote'].iloc[i] / df['votes'].iloc[i] if df['votes'].iloc[i] > 0 else 0.5
+                    actual_b = df['upvote'].iloc[j] / df['votes'].iloc[j] if df['votes'].iloc[j] > 0 else 0.5
+                    df.iloc[i, df.columns.get_loc('elo')] += 32 * (actual_a - expected_a)
+                    df.iloc[j, df.columns.get_loc('elo')] += 32 * (actual_b - expected_b)
+                except Exception as e:
+                    print(f"Error in ELO calculation for rows {i} and {j}: {str(e)}")
+                    continue
+    df['elo'] = round(df['elo'])
+
+    # Sort based on user preference
+    sort_column = 'elo' if sort_by_elo else 'win_rate'
+    df = df.sort_values(by=sort_column, ascending=False)
+    df['order'] = ['#' + str(i + 1) for i in range(len(df))]
+    
+    # Select and order columns for display
+    df = df[['order', 'name', 'win_rate', 'votes', 'elo']]
+    
+    # Remove proprietary models if filter is enabled
+    if hide_proprietary:
+        df = df[~df['name'].isin(closed_source)]
+    
+    # Convert DataFrame to markdown table with CSS styling
+    markdown_table = """
+<style>
+/* Reset any Gradio table styles */
+.leaderboard-table, 
+.leaderboard-table th, 
+.leaderboard-table td {
+    border: none !important;
+    border-collapse: separate !important;
+    border-spacing: 0 !important;
+}
+
+.leaderboard-container {
+    background: var(--background-fill-primary);
+    border: 1px solid var(--border-color-primary);
+    border-radius: 12px;
+    padding: 4px;
+    margin: 10px 0;
+    width: 100%;
+    overflow-x: auto;  /* Enable horizontal scroll */
+}
+
+.leaderboard-scroll {
+    max-height: 600px;
+    overflow-y: auto;
+    border-radius: 8px;
+}
+
+.leaderboard-table {
+    width: 100%;
+    border-spacing: 0;
+    border-collapse: separate;
+    font-size: 15px;
+    line-height: 1.5;
+    table-layout: auto;  /* Allow flexible column widths */
+}
+
+.leaderboard-table th {
+    background: var(--background-fill-secondary);
+    color: var(--body-text-color);
+    font-weight: 600;
+    text-align: left;
+    padding: 12px 16px;
+    position: sticky;
+    top: 0;
+    z-index: 1;
+}
+
+.leaderboard-table th:after {
+    content: '';
+    position: absolute;
+    left: 0;
+    bottom: 0;
+    width: 100%;
+    border-bottom: 1px solid var(--border-color-primary);
+}
+
+.leaderboard-table td {
+    padding: 12px 16px;
+    color: var(--body-text-color);
+}
+
+.leaderboard-table tr td {
+    border-bottom: 1px solid var(--border-color-primary);
+}
+
+.leaderboard-table tr:last-child td {
+    border-bottom: none;
+}
+
+.leaderboard-table tr:hover td {
+    background: var(--background-fill-secondary);
+}
+
+/* Column-specific styles */
+.leaderboard-table .col-rank {
+    width: 70px;
+    min-width: 70px;  /* Prevent rank from shrinking */
+}
+
+.leaderboard-table .col-model {
+    min-width: 200px;  /* Minimum width before scrolling */
+}
+
+.leaderboard-table .col-winrate {
+    width: 100px;
+    min-width: 100px;  /* Prevent win rate from shrinking */
+}
+
+.leaderboard-table .col-votes {
+    width: 100px;
+    min-width: 100px;  /* Prevent votes from shrinking */
+}
+
+.leaderboard-table .col-arena {
+    width: 100px;
+    min-width: 100px;  /* Prevent arena score from shrinking */
+}
+
+.win-rate {
+    display: inline-block;
+    font-weight: 600;
+    padding: 4px 8px;
+    border-radius: 6px;
+    min-width: 65px;
+    text-align: center;
+}
+
+.win-rate-excellent {
+    background-color: var(--color-accent);
+    color: var(--color-accent-foreground);
+}
+
+.win-rate-good {
+    background-color: var(--color-accent-soft);
+    color: var(--body-text-color);
+}
+
+.win-rate-average {
+    background-color: var(--background-fill-secondary);
+    color: var(--body-text-color);
+    border: 1px solid var(--border-color-primary);
+}
+
+.win-rate-below {
+    background-color: var(--error-background-fill);
+    color: var(--body-text-color);
+}
+
+.model-link {
+    color: var(--body-text-color) !important;
+    text-decoration: none !important;
+    border-bottom: 2px dashed rgba(128, 128, 128, 0.3);
+}
+
+.model-link:hover {
+    color: var(--color-accent) !important;
+    border-bottom-color: var(--color-accent) !important;
+}
+
+.proprietary-badge {
+    display: inline-block;
+    font-size: 12px;
+    padding: 2px 6px;
+    border-radius: 4px;
+    background-color: var(--background-fill-secondary);
+    color: var(--body-text-color);
+    margin-left: 6px;
+    border: 1px solid var(--border-color-primary);
+}
+
+/* New Arena V2 Pointer */
+.arena-v2-pointer {
+    display: block;
+    margin: 20px auto;
+    padding: 20px;
+    text-align: center;
+    border-radius: 12px;
+    font-size: 20px;
+    font-weight: bold;
+    cursor: pointer;
+    transition: all 0.3s ease;
+    position: relative;
+    overflow: hidden;
+    text-decoration: none !important;
+    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
+    max-width: 800px;
+    background: linear-gradient(135deg, #FF7B00, #FF5500);
+    color: white !important;
+    border: none;
+}
+
+/* Dark mode adjustments */
+@media (prefers-color-scheme: dark) {
+    .arena-v2-pointer {
+        box-shadow: 0 4px 20px rgba(255, 123, 0, 0.3);
+    }
+}
+
+.arena-v2-pointer:hover {
+    transform: translateY(-5px);
+    box-shadow: 0 7px 25px rgba(255, 123, 0, 0.4);
+    filter: brightness(1.05);
+    color: white !important;
+    text-decoration: none !important;
+}
+
+.arena-v2-pointer::after {
+    content: "→";
+    font-size: 24px;
+    margin-left: 10px;
+    display: inline-block;
+    transition: transform 0.3s ease;
+}
+
+.arena-v2-pointer:hover::after {
+    transform: translateX(5px);
+}
+</style>
+
+<a href="https://huggingface.co/spaces/TTS-AGI/TTS-Arena-V2" class="arena-v2-pointer" target="_blank">
+    Visit the new TTS Arena V2 to vote on the latest models!
+</a>
+
+<div class="leaderboard-container">
+<div class="leaderboard-scroll">
+<table class="leaderboard-table">
+<thead>
+<tr>
+<th class="col-rank">Rank</th>
+<th class="col-model">Model</th>
+<th class="col-winrate">Win Rate</th>
+<th class="col-votes">Votes</th>
+""" + ("""<th class="col-arena">Arena Score</th>""" if sort_by_elo else "") + """
+</tr>
+</thead>
+<tbody>
+"""
+    
+    def get_win_rate_class(win_rate):
+        if win_rate >= 60:
+            return "win-rate-excellent"
+        elif win_rate >= 55:
+            return "win-rate-good"
+        elif win_rate >= 45:
+            return "win-rate-average"
+        else:
+            return "win-rate-below"
+    
+    for _, row in df.iterrows():
+        win_rate_class = get_win_rate_class(row['win_rate'])
+        win_rate_html = f'<span class="win-rate {win_rate_class}">{row["win_rate"]}%</span>'
+        
+        # Add link to model name if available and proprietary badge if closed source
+        model_name = row['name']
+        original_model_name = model_name
+        if model_name in model_links:
+            model_name = f'<a href="{model_links[model_name]}" target="_blank" class="model-link">{model_name}</a>'
+        
+        if original_model_name in closed_source:
+            model_name += '<span class="proprietary-badge">Proprietary</span>'
+        
+        markdown_table += f'''<tr>
+            <td class="col-rank">{row['order']}</td>
+            <td class="col-model">{model_name}</td>
+            <td class="col-winrate">{win_rate_html}</td>
+            <td class="col-votes">{row['votes']:,}</td>''' + (
+            f'''<td class="col-arena">{int(row['elo'])}</td>''' if sort_by_elo else ""
+        ) + "</tr>\n"
+    
+    markdown_table += "</tbody></table></div></div>"
+    return markdown_table
+
+ABOUT = """
+# TTS Arena (Legacy)
+
+This is the legacy read-only leaderboard for TTS Arena V1. No new votes are being accepted.
+
+**Please visit the new [TTS Arena](https://huggingface.co/spaces/TTS-AGI/TTS-Arena-V2) to vote!**
+"""
+
+CITATION_TEXT = """@misc{tts-arena,
+	title        = {Text to Speech Arena},
+	author       = {mrfakename and Srivastav, Vaibhav and Fourrier, Clémentine and Pouget, Lucain and Lacombe, Yoach and main and Gandhi, Sanchit},
+	year         = 2024,
+	publisher    = {Hugging Face},
+	howpublished = "\\url{https://huggingface.co/spaces/TTS-AGI/TTS-Arena}"
+}"""
+FOOTER = f"""
+If you reference the Arena in your work, please cite it as follows:
+
+```bibtex
+{CITATION_TEXT}
+```
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown(ABOUT)
+    
+    with gr.Row():
+        with gr.Column():
+            reveal_prelim = gr.Checkbox(label="Show preliminary results (< 500 votes)", value=False)
+            hide_battle_votes = gr.Checkbox(label="Exclude battle votes", value=False)
+        with gr.Column():
+            sort_by_elo = gr.Checkbox(label="Sort by Arena Score instead of Win Rate", value=True)
+            hide_proprietary = gr.Checkbox(label="Hide proprietary models", value=False)
+    
+    leaderboard_html = gr.HTML(get_leaderboard())
+    
+    # Update leaderboard when filters change
+    for control in [reveal_prelim, hide_battle_votes, sort_by_elo, hide_proprietary]:
+        control.change(
+            fn=get_leaderboard,
+            inputs=[reveal_prelim, hide_battle_votes, sort_by_elo, hide_proprietary],
+            outputs=leaderboard_html
+        )
+    
+    gr.Markdown(FOOTER)
+
+demo.launch()