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import os
import numpy as np
import gradio as gr
import requests
from genai_chat_ai import AI,create_chat_session
api_key = os.environ.get("Id_mode_vits")
headers = {"Authorization": f"Bearer {api_key}"}
from transformers import AutoTokenizer,VitsModel
import torch
models= {}
tokenizer = AutoTokenizer.from_pretrained("asg2024/vits-ar-sa-huba",token=api_key)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_model(name_model):
global models
if name_model in models:
return models[name_model]
models[name_model]=VitsModel.from_pretrained(name_model,token=api_key).to(device)
return models[name_model]
def genrate_speech(text,name_model):
inputs=tokenizer(text,return_tensors="pt")
model=get_model(name_model)
with torch.no_grad():
wav=model(
input_ids= input_ids.input_ids.to(device),
attention_mask=input_ids.attention_mask.to(device),
speaker_id=0
).waveform.cpu().numpy().reshape(-1)
return model.config.sampling_rate,wav
def remove_extra_spaces(text):
"""
Removes extra spaces between words in a string.
Args:
text: The string to process.
Returns:
The string with extra spaces removed.
"""
return ' '.join(text.split())
def query(text,API_URL):
payload={"inputs": text}
response = requests.post(API_URL, headers=headers, json=payload)
return response.content
def get_answer_ai(text):
global AI
try:
response = AI.send_message(text)
return response.text
except :
AI=create_chat_session()
response = AI.send_message(text)
return response.text
with gr.Blocks() as demo: # Use gr.Blocks to wrap the entire interface
with gr.Tab("محادثة صوتية بالذكاء الاصطناعي باللهجة السعودية"):
with gr.Row(): # Arrange input/output components side-by-side
with gr.Column():
text_input = gr.Textbox(label="أدخل أي نص")
user_audio = gr.Audio(label="صوتك")
with gr.Row():
btn = gr.Button("إرسال")
btn_ai_only = gr.Button("توليد رد الذكاء الاصطناعي فقط")
with gr.Column():
model_choices = gr.Dropdown(
choices=[
"asg2024/vits-ar-sa",
"asg2024/vits-ar-sa-huba",
"asg2024/vits-ar-sa-ms",
"asg2024/vits-ar-sa-magd",
"asg2024/vits-ar-sa-fahd",
],
label="اختر النموذج",
value="asg2024/vits-ar-sa",
)
ai_audio = gr.Audio(label="رد الذكاء الاصطناعي الصوتي")
ai_text = gr.Textbox(label="رد الذكاء الاصطناعي النصي")
# Use a single button to trigger both functionalities
def process_audio(text, model_choice, generate_user_audio=True):
API_URL = f"https://api-inference.huggingface.co/models/{model_choice}"
text_answer = get_answer_ai(text)
text_answer = remove_extra_spaces(text_answer)
data_ai = genrate_speech(text_answer,model_choice)#query(text_answer, API_URL)
if generate_user_audio: # Generate user audio if needed
data_user =genrate_speech(text_answer,model_choice)# query(text, API_URL)
return data_user, data_ai, text_answer
else:
return data_ai # Return None for user_audio
btn.click(
process_audio, # Call the combined function
inputs=[text_input, model_choices],
outputs=[user_audio, ai_audio, ai_text],
)
# Additional button to generate only AI audio
btn_ai_only.click(
lambda text, model_choice: process_audio(text, model_choice, False),
inputs=[text_input, model_choices],
outputs=[ai_audio],
)
if __name__ == "__main__":
demo.launch()
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