Update app.py

app.py

@@ -6,36 +6,43 @@ import torch
 import os
 import logging
 
+# Setup logging
 logging.basicConfig(level=logging.INFO)
 
-
 # Retrieve Hugging Face access token from environment variables
 access_token = os.getenv("HF_ACCESS_TOKEN")
 
 # Set device
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-
 # Global variable for the pipeline
 pipe = None
 
 def load_model():
     global pipe
     if pipe is None:
-        logging.info("Loading the Stable Diffusion model...")
-        pipe = StableDiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-3-medium",
-            torch_dtype=torch.float16,
-            use_auth_token=access_token,
-            cache_dir="/path/to/cache"  # specify cache directory
-        )
-        pipe = pipe.to(device)
-        logging.info("Model loaded successfully.")
+        try:
+            logging.info("Loading the Stable Diffusion model...")
+            pipe = StableDiffusionPipeline.from_pretrained(
+                "stabilityai/stable-diffusion-3-medium",
+                torch_dtype=torch.float16,
+                use_auth_token=access_token,
+                cache_dir="/path/to/cache"  # specify cache directory if needed
+            )
+            pipe = pipe.to(device)
+            logging.info("Model loaded successfully.")
+        except Exception as e:
+            logging.error(f"Failed to load model: {e}")
+            pipe = None
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+    load_model()  # Ensure the model is loaded
+    if pipe is None:
+        raise RuntimeError("Model failed to load.")
+    
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
@@ -146,3 +153,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 demo.queue().launch()
+