Update app.py

app.py

@@ -1,13 +1,19 @@
 import gradio as gr
-from transformers import pipeline
+import torch
+from diffusers import DiffusionPipeline
 
-# Load the BigGAN model using the transformers pipeline
-generator = pipeline("text-to-image", model="osanseviero/BigGAN-deep-128")
+# Load the BigGAN model using the diffusers library
+# The model type is "biggan", which is the correct pipeline to use.
+# Move model to GPU if available for faster generation
+pipeline = DiffusionPipeline.from_pretrained(
+    "osanseviero/BigGAN-deep-128", 
+    use_auth_token=True
+).to("cuda" if torch.cuda.is_available() else "cpu")
 
 def generate_image(text_input):
-    """Generates an image from text using the BigGAN pipeline."""
+    """Generates an image from text using the BigGAN diffusers pipeline."""
     # The pipeline's output is an image.
-    image = generator(text_input)["images"][0]
+    image = pipeline(text_input).images[0]
     return image
 
 # Create the Gradio interface directly