Update app.py

app.py

@@ -3,6 +3,7 @@ import torch
 import random
 import time
 from diffusers import DiffusionPipeline, LCMScheduler
+from PIL import Image, ImageFilter
 
 # -------------------------------
 # MODEL SETUP (CPU SAFE)
@@ -30,12 +31,10 @@ def refine_prompt_fast(user_prompt: str):
     """
     Instant deterministic prompt refinement (<1ms)
     """
-    # Known objects
     known_objects = {"apple","banana","snake","cat","dog","fox","rabbit","dragon","bird","frog","hamster"}
     object_found = next((w for w in known_objects if w in user_prompt.lower()), None)
     subject = object_found if object_found else user_prompt.lower().strip()
 
-    # Style detection
     style = ""
     if any(w in user_prompt.lower() for w in ["cute","adorable","kawaii"]):
         style = ", cute, friendly, rounded body, big eyes, pastel colors, cartoon style"
@@ -44,12 +43,8 @@ def refine_prompt_fast(user_prompt: str):
     else:
         style = ", high quality, clean background"
 
-    # Construct prompt
     prompt = f"a single {subject}, centered, isolated{style}"
-
-    # Negative prompt (always same)
     negative = "multiple objects, duplicate, blurry, low quality, cropped, out of frame, horror, grotesque, aggressive, weird colors, artifacts"
-
     return prompt, negative
 
 # -------------------------------
@@ -57,14 +52,14 @@ def refine_prompt_fast(user_prompt: str):
 # -------------------------------
 def estimate_time(steps, resolution):
     per_step = {256:6, 512:12, 768:25, 1024:45}[int(resolution)]
-    overhead = 2  # thinking stage is <1s, plus small overhead
+    overhead = 2
     est = overhead + steps * per_step
     minutes = est // 60
     seconds = est % 60
     return f"⏱️ Estimated time: ~{int(minutes)}m {int(seconds)}s"
 
 # -------------------------------
-# IMAGE GENERATION WITH LIVE STATUS
+# IMAGE GENERATION WITH PROGRESSIVE BLUR REVEAL
 # -------------------------------
 def generate(prompt, resolution, steps):
     # --- THINKING PHASE ---
@@ -77,7 +72,7 @@ def generate(prompt, resolution, steps):
     seed = random.randint(0, 10**9)
     gen = torch.Generator("cpu").manual_seed(seed)
     
-    pipe.scheduler.set_timesteps(int(steps))  # fast path
+    pipe.scheduler.set_timesteps(int(steps))
     img = pipe(
         prompt=refined_prompt,
         negative_prompt=neg_prompt,
@@ -88,6 +83,21 @@ def generate(prompt, resolution, steps):
         generator=gen
     ).images[0]
 
+    # --- PROGRESSIVE BLUR REVEAL ---
+    # First show white placeholder
+    width, height = img.size
+    white_img = Image.new("RGB", (width, height), (255, 255, 255))
+    yield [white_img], "🖼 Image generated! Revealing..."
+
+    # Apply progressive blur
+    max_blur = 20  # max blur radius
+    steps_blur = 10  # number of steps
+    for i in range(steps_blur):
+        blur_percent = 100 - i*10
+        blurred_img = img.filter(ImageFilter.GaussianBlur(radius=max_blur * blur_percent / 100))
+        yield [blurred_img], f"🖼 Revealing image... {i*10}%"
+        time.sleep(1)  # 1 second per step
+
     duration = int(time.time() - start_time)
     yield [img], f"✅ Finished in {duration}s | Seed: {seed}"
 
@@ -96,7 +106,7 @@ def generate(prompt, resolution, steps):
 # -------------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 👾 CREEPER AI — CPU SMART IMAGE GENERATION")
-    
+
     with gr.Row():
         with gr.Column():
             prompt_in = gr.Textbox(label="Prompt", placeholder="cute snake", lines=2)
@@ -112,7 +122,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             status = gr.Markdown("🟢 Ready")
             gallery = gr.Gallery(columns=1)
     
-    # Update ETA dynamically
     for ctrl in [steps, resolution]:
         ctrl.change(estimate_time, [steps, resolution], eta)