changes spaces

app.py

@@ -30,7 +30,6 @@ def safe_copy(src: str, dst: str) -> str:
 # ──────────────────────────────────────────────────────────────────────────────
 # Core processing pipeline
 # ──────────────────────────────────────────────────────────────────────────────
-@spaces.GPU()
 def process_video(
     context: str,
     instruction: str,

scripts/generate_scripts.py

@@ -5,6 +5,7 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
 from dotenv import load_dotenv
+import spaces
 
 
 # Chargement du modèle et du tokenizer
@@ -17,7 +18,7 @@ model_id = "Qwen/Qwen2.5-0.5B"
 tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, trust_remote_code=True).to(device)
 
-
+@spaces.GPU()
 def generate_local(prompt: str, max_new_tokens: int = 350, temperature: float = 0.7) -> str:
     device = model.device  # get the device the model is on
     inputs = tokenizer(prompt, return_tensors="pt").to(device)
@@ -32,7 +33,7 @@ def generate_local(prompt: str, max_new_tokens: int = 350, temperature: float =
     return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
 
-
+@spaces.GPU()
 def generate_script(prompt: str, word_count: int = 60) -> str:
     system_prompt = (
         "You are a professional video scriptwriter. "
@@ -41,7 +42,7 @@ def generate_script(prompt: str, word_count: int = 60) -> str:
     )
     return generate_local(system_prompt)
 
-
+@spaces.GPU()
 def one_word(query: str) -> str:
     prompt_final = (
         "Extract only the unique central theme of the following text in English in JSON format like this: "
@@ -56,7 +57,7 @@ def one_word(query: str) -> str:
         keyword = matches[0] if matches else ""
     return keyword.lower()
 
-
+@spaces.GPU()
 def generate_title(text: str) -> str:
     prompt_final = (
         "Generate a unique title for a YouTube Short video that is engaging and informative, "
@@ -64,7 +65,7 @@ def generate_title(text: str) -> str:
     )
     return generate_local(prompt_final, max_new_tokens=50, temperature=0.9).strip()
 
-
+@spaces.GPU()
 def generate_description(text: str) -> str:
     prompt_final = (
         "Write only the YouTube video description in English:\n"
@@ -75,7 +76,7 @@ def generate_description(text: str) -> str:
     )
     return generate_local(prompt_final, max_new_tokens=300, temperature=0.7).strip()
 
-
+@spaces.GPU()
 def generate_tags(text: str) -> list:
     prompt_final = (
         "List only the important keywords for this YouTube video, separated by commas, "

scripts/generate_subtitles.py

@@ -12,6 +12,8 @@ from moviepy import (
     vfx
 )
 from moviepy.video.fx import FadeIn, Resize
+import spaces
+
 
 
 FONT_PATH = "Arial-Bold"
@@ -81,6 +83,8 @@ def save_subtitles_to_srt(subtitles, output_path):
             f.write(f"{format_timestamp(sub['start'])} --> {format_timestamp(sub['end'])}\n")
             f.write(f"{sub['text'].strip()}\n\n")
 
+
+@spaces.GPU()
 def transcribe_audio_to_subs(audio_path):
     """
     Transcrit le fichier audio en texte (via Whisper), retourne la liste

scripts/generate_voice.py

@@ -2,6 +2,8 @@ import os
 import soundfile as sf
 from kokoro import KPipeline
 import random
+import spaces
+
 
 pipeline = KPipeline(lang_code='a')  # 'a' for English
 
@@ -12,7 +14,7 @@ ENGLISH_VOICES = [
     "am_fenrir"     
 ]
 
-
+@spaces.GPU()
 def generate_voice(text: str, path: str):
     for voice in random.sample(ENGLISH_VOICES, len(ENGLISH_VOICES)):
         try: