Update app.py

app.py

@@ -7,32 +7,32 @@ from diffusers import DiffusionPipeline, LCMScheduler
 from PIL import Image, ImageFilter
 
 # -------------------------------
-# SMALL PROMPT ENHANCER (CPU)
+# PASSWORD
+# -------------------------------
+PASSWORD = "CREEPERIMG"
+
+# -------------------------------
+# PROMPT ENHANCER SETUP
 # -------------------------------
 ENHANCER_MODEL = "HuggingFaceTB/SmolLM-135M-Instruct"
 tokenizer_enhancer = AutoTokenizer.from_pretrained(ENHANCER_MODEL)
 model_enhancer = AutoModelForCausalLM.from_pretrained(ENHANCER_MODEL)
 
-def enhance_text(user_prompt, prefix):
-    """
-    Uses a tiny LLM to rewrite user input into a more detailed instruction.
-    """
-    instruction = f"Rewrite this for an image generator with detail: {user_prompt}"
-    if prefix:
-        instruction = f"{prefix} {user_prompt}"
-
-    inputs = tokenizer_enhancer(instruction, return_tensors="pt")
+def enhance_text(user_prompt):
+    """Enhance user prompt using small LLM"""
+    if not user_prompt.strip():
+        return ""
+    inputs = tokenizer_enhancer(user_prompt, return_tensors="pt")
     outputs = model_enhancer.generate(
         **inputs,
         max_new_tokens=50,
         temperature=0.7,
         do_sample=True
     )
-    text = tokenizer_enhancer.decode(outputs[0], skip_special_tokens=True)
-    return text.strip()
+    return tokenizer_enhancer.decode(outputs[0], skip_special_tokens=True).strip()
 
 # -------------------------------
-# IMAGE MODEL SETUP (CPU SAFE)
+# IMAGE MODEL SETUP (CPU)
 # -------------------------------
 IMG_MODEL_ID = "runwayml/stable-diffusion-v1-5"
 IMG_ADAPTER_ID = "latent-consistency/lcm-lora-sdv1-5"
@@ -63,18 +63,20 @@ def estimate_time(steps, resolution):
     return f"⏱️ Estimated: ~{mins}m {secs}s"
 
 # -------------------------------
-# GENERATE WITH PROGRESSIVE BLUR
+# GENERATE IMAGE WITH BLUR REVEAL
 # -------------------------------
-def generate(prompt, neg_prompt, resolution, steps):
-    # 1️⃣ AI PROMPT ENHANCEMENT
-    enhanced_prompt = enhance_text(prompt, "")
-    enhanced_negative = enhance_text(neg_prompt, "Rewrite negative prompt:")
-
-    # 2️⃣ White placeholder
-    placeholder = Image.new("RGB", (int(resolution), int(resolution)), (255,255,255))
-    yield [placeholder], "🟡 Generating..."
-
-    # 3️⃣ CPU IMAGE GENERATION
+def generate(password, prompt, neg_prompt, resolution, steps):
+    if password != PASSWORD:
+        return [Image.new("RGB", (int(resolution), int(resolution)), (255,255,255))], "❌ Wrong password", ""
+    
+    # 1️⃣ Enhance prompts
+    enhanced_prompt = enhance_text(prompt)
+    enhanced_negative = enhance_text(neg_prompt)
+    
+    # Show the enhanced prompt for debugging
+    yield [Image.new("RGB", (int(resolution), int(resolution)), (255,255,255))], "🟡 Generating...", enhanced_prompt
+
+    # 2️⃣ Generate the image
     seed = random.randint(0, 10**9)
     gen = torch.Generator("cpu").manual_seed(seed)
     pipe.scheduler.set_timesteps(int(steps))
@@ -89,26 +91,30 @@ def generate(prompt, neg_prompt, resolution, steps):
         generator=gen
     ).images[0]
 
-    # 4️⃣ BLUR REVEAL
+    # 3️⃣ Progressive blur reveal
     max_blur = 20
     for i in range(10):
         blur_pct = 100 - i*10
         blurred = img.filter(ImageFilter.GaussianBlur(radius=max_blur * blur_pct/100))
-        yield [blurred], "🟢 Revealing..."
+        yield [blurred], "🟢 Revealing...", enhanced_prompt
         time.sleep(1)
 
-    # 5️⃣ FINAL
-    yield [img], f"✅ Done | Seed: {seed}"
+    # 4️⃣ Final image
+    yield [img], f"✅ Done | Seed: {seed}", enhanced_prompt
 
 # -------------------------------
 # GRADIO UI
 # -------------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 👾 CREEPER AI — SMART IMAGE GENERATOR")
-    gr.Markdown("1: The higher the resolution & steps, the longer the image takes to make.\n2: The more detailed the prompt and negative prompt, the better the result.")
+    gr.Markdown(
+        "1: The higher the resolution & steps, the longer the image takes to make.\n"
+        "2: The more detailed the prompt and negative prompt, the better the result."
+    )
 
     with gr.Row():
         with gr.Column():
+            password_in = gr.Textbox(label="Password", placeholder="Enter password to enable generation")
             prompt_in = gr.Textbox(label="Prompt")
             neg_in = gr.Textbox(label="Negative Prompt")
 
@@ -119,6 +125,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             gen_btn = gr.Button("Generate")
 
             status = gr.Markdown("🟢 Ready")
+            enhanced_box = gr.Textbox(label="Enhanced Prompt (sent to image model)", interactive=False)
 
         with gr.Column():
             gallery = gr.Gallery(columns=1)
@@ -128,8 +135,8 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     gen_btn.click(
         generate,
-        inputs=[prompt_in, neg_in, resolution, steps],
-        outputs=[gallery, status]
+        inputs=[password_in, prompt_in, neg_in, resolution, steps],
+        outputs=[gallery, status, enhanced_box]
     )
 
 demo.launch()