Arabic-Chatbot / app.py
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Update app.py
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import os
import logging
import threading
import numpy as np
import torch
import librosa
import soundfile as sf
from pydub import AudioSegment
from telegram import Update
from telegram.ext import ApplicationBuilder, MessageHandler, filters, CommandHandler
from transformers import pipeline, AutoTokenizer, VitsModel
from huggingface_hub import login
import asyncio
from collections import defaultdict
# ===== تهيئة التوكن =====
login(token=os.getenv("HF_TOKEN"))
# ===== إعدادات النظام =====
logging.basicConfig(
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
level=logging.INFO
)
logger = logging.getLogger(__name__)
# ===== تحميل النماذج =====
try:
# 1. نموذج التعرف على الكلام
asr_pipeline = pipeline(
"automatic-speech-recognition",
model="jonatasgrosman/wav2vec2-large-xlsr-53-arabic",
token=os.getenv("HF_TOKEN")
)
# 2. نموذج توليف الصوت الأنثوي (الاسم الصحيح)
tts_tokenizer = AutoTokenizer.from_pretrained(
"facebook/mms-tts-ara", # تم تغيير النموذج إلى فيسبوك MMS
token=os.getenv("HF_TOKEN")
)
tts_model = VitsModel.from_pretrained(
"facebook/mms-tts-ara",
token=os.getenv("HF_TOKEN")
)
except Exception as e:
logger.error(f"فشل تحميل النماذج: {str(e)}")
raise
# ===== ذاكرة المحادثة =====
conversation_history = defaultdict(list)
# ===== دوال معالجة الصوت =====
def enhance_audio(input_path: str, output_path: str) -> bool:
try:
audio = AudioSegment.from_wav(input_path)
audio = audio.low_pass_filter(3000)
audio = audio.high_pass_filter(100)
audio = audio.normalize()
audio = audio.fade_in(150).fade_out(150)
audio.export(output_path, format="wav")
return True
except Exception as e:
logger.error(f"خطأ في تحسين الصوت: {str(e)}")
return False
async def speech_to_text(audio_path: str) -> str:
try:
audio, sr = librosa.load(audio_path, sr=16000)
sf.write("temp.wav", audio, sr)
result = asr_pipeline("temp.wav")
return result["text"]
except Exception as e:
logger.error(f"فشل التعرف على الصوت: {str(e)}")
return ""
async def generate_response(text: str, user_id: str) -> str:
try:
# تحديث ذاكرة المحادثة
conversation_history[user_id].append(text)
context = "\n".join(conversation_history[user_id][-3:])
chatbot = pipeline(
"text-generation",
model="aubmindlab/aragpt2-base",
token=os.getenv("HF_TOKEN"),
max_length=50,
temperature=0.7,
)
response = chatbot(
context,
num_return_sequences=1,
pad_token_id=50256
)
return response[0]['generated_text']
except Exception as e:
logger.error(f"فشل توليد الرد: {str(e)}")
return "حدث خطأ في توليد الرد."
async def text_to_speech(text: str) -> None:
try:
inputs = tts_tokenizer(text, return_tensors="pt")
with torch.no_grad():
output = tts_model(**inputs, speaker_id=1) # اختيار الصوت الأنثوي
waveform = output.waveform[0].numpy()
sf.write("bot_response.wav", waveform, tts_model.config.sampling_rate)
except Exception as e:
logger.error(f"فشل تحويل النص إلى صوت: {str(e)}")
# ===== دوال التفاعل مع المستخدم =====
async def start(update: Update, context):
await update.message.reply_text("مرحبًا! أنا بوت الدردشة الصوتية الأنثوي 🎤\nأرسل لي رسالة صوتية وسأرد عليك بصوت أنثوي واضح.")
async def process_voice(update: Update, context):
try:
user_id = update.message.from_user.id
voice_file = await update.message.voice.get_file()
await voice_file.download_to_drive("user_voice.ogg")
user_text = await speech_to_text("user_voice.ogg")
bot_response = await generate_response(user_text, str(user_id))
await text_to_speech(bot_response)
if enhance_audio("bot_response.wav", "bot_response_enhanced.wav"):
await update.message.reply_voice("bot_response_enhanced.wav")
else:
await update.message.reply_voice("bot_response.wav")
except Exception as e:
logger.error(f"خطأ غير متوقع: {str(e)}")
await update.message.reply_text("⚠️ عذرًا، حدث خطأ في المعالجة.")
# ===== التشغيل الرئيسي =====
def run_bot():
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
application = ApplicationBuilder().token(os.getenv("TELEGRAM_TOKEN")).build()
application.add_handler(CommandHandler("start", start))
application.add_handler(MessageHandler(filters.VOICE, process_voice))
application.run_polling(
close_loop=False,
stop_signals=[]
)
if __name__ == "__main__":
bot_thread = threading.Thread(target=run_bot, daemon=True)
bot_thread.start()
bot_thread.join()