Update app.py

app.py

@@ -21,7 +21,7 @@ from mutagen.mp3 import MP3
 from gtts import gTTS
 from pydub import AudioSegment
 import textwrap
-
+nltk.download('punkt_tab')
 # Log GPU Memory (optional, for debugging)
 def log_gpu_memory():
     """Log GPU memory usage."""
@@ -84,49 +84,6 @@ def load_text_summarization_model():
 tokenizer, model = load_text_summarization_model()
 
 
-#@spaces.GPU()
-def generate_image_with_flux_old(
-    text: str,
-    seed: int = 42,
-    width: int = 1024,
-    height: int = 1024,
-    num_inference_steps: int = 4,
-    randomize_seed: bool = True):
-    """
-    Generates an image from text using FLUX.
-    Args:
-        text: The text prompt to generate the image from.
-        seed: The random seed for image generation. -1 for random.
-        width: Width of the generated image.
-        height: Height of the generated image.
-        num_inference_steps: Number of inference steps.
-        randomize_seed: Whether to randomize the seed.
-    Returns:
-        A PIL Image object.
-    """
-    print(f"DEBUG: Generating image with FLUX for text: '{text}'")
-    
-    # Initialize FLUX pipeline here
-    dtype = torch.bfloat16
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    torch.cuda.empty_cache()  # Clear cache
-    gc.collect()  # Run garbage collection
-    flux_pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)  # Specify device for generator
-    image = flux_pipe(
-        prompt=text,
-        width=width,
-        height=height,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        guidance_scale=0.0
-    ).images[0]
-    print("DEBUG: Image generated successfully.")
-    return image
-
 @spaces.GPU()
 def generate_image_with_flux(
     text: str,