Update app.py

app.py

@@ -20,7 +20,7 @@ MAX_IMAGE_SIZE = 2048
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# ✅ Load model only once
+# ✅ DO NOT CHANGE: Working pipeline using taef1
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
 
 pipe = DiffusionPipeline.from_pretrained(
@@ -40,7 +40,6 @@ def sanitize_filename(name):
 @spaces.GPU(duration=75)
 def infer(user_token, prompt, seed=42, randomize_seed=False, width=1024, height=1024,
           guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
-    # Authenticate using user's token for this session
     login(token=user_token)
 
     if randomize_seed:
@@ -58,24 +57,27 @@ def infer(user_token, prompt, seed=42, randomize_seed=False, width=1024, height=
         generator=generator,
         output_type="pil",
     ):
-        # Save low-quality JPG
         safe_name = sanitize_filename(prompt)
         img_path = f"image_preview/{safe_name}_{seed}.jpg"
         img.convert("RGB").save(img_path, "JPEG", quality=60)
 
-        # Collect previews
         previews = [f"image_preview/{f}" for f in sorted(os.listdir("image_preview")) if f.endswith(".jpg")]
         return img, seed, previews
 
-
-examples = [
-    ["your_token_here", "a man walking in the forest"],
-    ["your_token_here", "a viking ship sailing down a river"],
-    ["your_token_here", "a woman resting by an open fire"],
-    ["your_token_here", "a sword fight in a medieval village"]
+# Wrapper to inject a fallback token if needed
+def infer_with_fallback_token(user_token, prompt, *args):
+    if not user_token.strip():
+        user_token = "your_token_here"  # Replace with a real test token for dev, not in production
+    return infer(user_token, prompt, *args)
+
+# Prompt-only examples; token will be filled in by wrapper
+prompt_examples = [
+    "a man walking in the forest",
+    "a viking ship sailing down a river",
+    "a woman resting by an open fire",
+    "a sword fight in a medieval village"
 ]
 
-
 with gr.Blocks(css="style.css") as natalie_diffusion:
     with gr.Row():
         with gr.Column(scale=1, elem_id="left-column"):
@@ -118,14 +120,13 @@ Generate images in the surreal style of artist [Natalie Kav](https://www.behance
             result_example = gr.Image(visible=False)
 
             gr.Examples(
-                examples=examples,
-                fn=infer,
-                inputs=[hf_token_input, prompt],
+                examples=[[prompt] for prompt in prompt_examples],
+                fn=lambda prompt: infer_with_fallback_token("", prompt),
+                inputs=[prompt],
                 outputs=[result_example, seed, gr.Gallery(visible=False)],
                 cache_examples=False,
             )
 
-
         with gr.Column(scale=1, elem_id="right-column"):
             result = gr.Image(label="", show_label=False, elem_id="generated-image")
 
@@ -135,7 +136,7 @@ Generate images in the surreal style of artist [Natalie Kav](https://www.behance
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn=infer,
+        fn=infer_with_fallback_token,
         inputs=[hf_token_input, prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs=[result, seed, gallery],
     )