Update app.py

app.py

@@ -1,32 +1,9 @@
 import gradio as gr
-import pandas as pd
-from langdetect import detect
-from datasets import load_dataset
-import threading, time, uuid, sqlite3, shutil, os, random, asyncio, threading
-from pathlib import Path
-from huggingface_hub import CommitScheduler, delete_file, hf_hub_download
-from gradio_client import Client
-import pyloudnorm as pyln
-import soundfile as sf
-import librosa
-from detoxify import Detoxify
 import os
-import tempfile
-from pydub import AudioSegment
-
-def match_target_amplitude(sound, target_dBFS):
-    change_in_dBFS = target_dBFS - sound.dBFS
-    return sound.apply_gain(change_in_dBFS)
-
-
+from pathlib import Path
 
-toxicity = Detoxify('original')
-with open('bsc.txt') as f:
-    sents = f.read().strip().splitlines()
-    
- 
 ####################################
-# Constants
+# Constants (static data version)
 ####################################
 AVAILABLE_MODELS = {
     'XTTSv2': 'xtts',
@@ -43,84 +20,46 @@ AVAILABLE_MODELS = {
 SPACE_ID = os.getenv('SPACE_ID')
 MAX_SAMPLE_TXT_LENGTH = 300
 MIN_SAMPLE_TXT_LENGTH = 10
-DB_DATASET_ID = os.getenv('DATASET_ID')
-DB_NAME = "database.db"
-
-# If /data available => means local storage is enabled => let's use it!
-DB_PATH = f"/data/{DB_NAME}" if os.path.isdir("/data") else DB_NAME
-print(f"Using {DB_PATH}")
-
 
 ####################################
-# Functions
+# Datos estáticos del leaderboard
 ####################################
-
-def create_db_if_missing():
-    conn = get_db()
-    cursor = conn.cursor()
-    cursor.execute('''
-        CREATE TABLE IF NOT EXISTS model (
-            name TEXT UNIQUE,
-            upvote INTEGER,
-            downvote INTEGER
-        );
-    ''')
-    cursor.execute('''
-        CREATE TABLE IF NOT EXISTS vote (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            username TEXT,
-            model TEXT,
-            vote INTEGER,
-            timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        );
-    ''')
-    cursor.execute('''
-        CREATE TABLE IF NOT EXISTS votelog (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            username TEXT,
-            chosen TEXT,
-            rejected TEXT,
-            timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        );
-    ''')
-    cursor.execute('''
-        CREATE TABLE IF NOT EXISTS spokentext (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            spokentext TEXT,
-            timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        );
-    ''')
-def get_db():
-    return sqlite3.connect(DB_PATH)
-
-
+leaderboard_data = [
+    {'name': 'XTTSv2', 'upvote': 150, 'downvote': 30},
+    {'name': 'ElevenLabs', 'upvote': 200, 'downvote': 50},
+    {'name': 'OpenVoice V2', 'upvote': 180, 'downvote': 40},
+    {'name': 'Play.HT 2.0', 'upvote': 100, 'downvote': 20},
+    {'name': 'MeloTTS', 'upvote': 90, 'downvote': 15},
+    {'name': 'StyleTTS 2', 'upvote': 170, 'downvote': 25},
+    {'name': 'GPT-SoVITS', 'upvote': 160, 'downvote': 35},
+    {'name': 'VoiceCraft 2.0', 'upvote': 140, 'downvote': 25},
+    {'name': 'Parler TTS', 'upvote': 110, 'downvote': 22}
+]
 
 ####################################
-# Space initialization
+# Functions (static version)
 ####################################
 
-# Download existing DB
-if not os.path.isfile(DB_PATH):
-    print("Downloading DB...")
-    try:
-        cache_path = hf_hub_download(repo_id=DB_DATASET_ID, repo_type='dataset', filename=DB_NAME)
-        shutil.copyfile(cache_path, DB_PATH)
-        print("Downloaded DB")
-    except Exception as e:
-        print("Error while downloading DB:", e)
+def get_leaderboard():
+    """
+    Retorna el leaderboard en orden descendente por votos positivos.
+    """
+    return sorted(leaderboard_data, key=lambda x: x['upvote'], reverse=True)
+
+def filter_preliminary(leaderboard, reveal=False):
+    """
+    Si reveal es True, muestra todos los resultados.
+    Si reveal es False, filtra los modelos con menos de 50 votos combinados.
+    """
+    if reveal:
+        return leaderboard
+    return [model for model in leaderboard if model['upvote'] + model['downvote'] > 50]
 
-# Create DB table (if doesn't exist)
-create_db_if_missing()
-    
-# Sync local DB with remote repo every 5 minute (only if a change is detected)
-scheduler = CommitScheduler(
-    repo_id=DB_DATASET_ID,
-    repo_type="dataset",
-    folder_path=Path(DB_PATH).parent,
-    every=5,
-    allow_patterns=DB_NAME,
-)
+####################################
+# Space initialization (static version)
+####################################
 
+# No DB download or sync needed
 
 """
 ## 🏆 Leaderboard
@@ -129,25 +68,29 @@ The leaderboard displays models in descending order of how natural they sound (b
 Important: In order to help keep results fair, the leaderboard hides results by default until the number of votes passes a threshold. Tick the `Reveal preliminary results` to show models without sufficient votes. Please note that preliminary results may be inaccurate.
 """.strip()
 
+####################################
+# Interfaz con Gradio
+####################################
 
-
-def del_db(txt):
-    if not txt.lower() == 'delete db':
-        raise gr.Error('You did not enter "delete db"')
-
-    # Delete local + remote
-    os.remove(DB_PATH)
-    delete_file(path_in_repo=DB_NAME, repo_id=DB_DATASET_ID, repo_type='dataset')
-
-    # Recreate
-    create_db_if_missing()
-    return 'Delete DB'
+def update_leaderboard(reveal):
+    """
+    Actualiza la tabla del leaderboard con base en si se deben mostrar
+    resultados preliminares o no.
+    """
+    filtered_leaderboard = filter_preliminary(get_leaderboard(), reveal)
+    return [[model['name'], model['upvote'], model['downvote']] for model in filtered_leaderboard]
 
 theme = gr.themes.Base(
     font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
 )
 
+with gr.Blocks(theme=theme) as demo:
+    gr.Markdown("# 🏆 Leaderboard\nVote to help the community determine the best TTS models.\n")
+    
+    reveal_checkbox = gr.Checkbox(label="Reveal preliminary results", value=False)
+    leaderboard_table = gr.DataFrame(headers=["Model", "Upvotes", "Downvotes"], datatype=["str", "int", "int"], value=update_leaderboard(reveal=False))
+    
+    reveal_checkbox.change(fn=update_leaderboard, inputs=[reveal_checkbox], outputs=[leaderboard_table])
 
-
-
-demo.queue(api_open=False, default_concurrency_limit=40).launch(show_api=False)
+# Lanzar la aplicación
+demo.queue(api_open=False, default_concurrency_limit=40).launch(show_api=False)