Major UI overhaul: dark mode, accessibility, responsive layout, improved UX

app.py

@@ -1,4 +1,4 @@
-"""Z-Image-Turbo v1.1 - AI Vision Assistant (GLM-4.6V)"""
+"""Z-Image-Turbo v1.2 - Fixed GLM Reasoning Filter"""
 
 import os
 import re
@@ -45,11 +45,11 @@ def polish_prompt(original_prompt: str, mode: str = "generate") -> str:
         if mode == "transform":
             return "high quality, enhanced details, professional finish"
         return "Ultra HD, 4K, cinematic composition, highly detailed"
-    
+
     client = get_deepseek_client()
     if not client:
         return original_prompt
-    
+
     if mode == "transform":
         system_prompt = """ROLE: Expert prompt engineer for AI image-to-image transformation.
 
@@ -75,7 +75,7 @@ STRICT RULES:
 - Include artistic style references when appropriate
 
 OUTPUT FORMAT: Only the final prompt text. No thinking, no explanation, no preamble, no word count."""
-    
+
     try:
         response = client.chat.completions.create(
             model="deepseek-reasoner",
@@ -85,28 +85,23 @@ OUTPUT FORMAT: Only the final prompt text. No thinking, no explanation, no pream
                 {"role": "user", "content": original_prompt}
             ],
         )
-        
+
         msg = response.choices[0].message
         content = msg.content if msg.content else ""
-        
+
         # If content is empty, try to extract final answer from reasoning_content
         if not content and hasattr(msg, 'reasoning_content') and msg.reasoning_content:
             text = msg.reasoning_content.strip()
-            # Get the last paragraph as the final answer
             paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()]
             if paragraphs:
                 content = paragraphs[-1]
-        
+
         if content:
-            # Clean up and limit length
             content = content.strip().replace("\n", " ")
-            # Remove any thinking markers if present
             if "<think>" in content:
                 content = content.split("</think>")[-1].strip()
-            # Remove quotes if wrapped
             if content.startswith('"') and content.endswith('"'):
                 content = content[1:-1]
-            # Truncate based on mode
             max_words = 80 if mode == "transform" else 100
             words = content.split()
             if len(words) > max_words:
@@ -141,18 +136,118 @@ def encode_image_base64(image: Optional[Image.Image]) -> Optional[str]:
     buf.seek(0)
     return base64.b64encode(buf.getvalue()).decode('utf-8')
 
+def clean_glm_response(text: str) -> str:
+    """Remove GLM special tokens and clean up text."""
+    if not text:
+        return ""
+    text = text.replace('<|begin_of_box|>', '').replace('<|end_of_box|>', '')
+    text = text.strip()
+    return text
+
+def is_thinking_text(text: str) -> bool:
+    """Check if text looks like GLM thinking/reasoning rather than actual content."""
+    if not text:
+        return True
+    
+    text_lower = text.lower().strip()
+    
+    # Patterns that indicate thinking/meta content
+    thinking_starts = (
+        'let me', 'i need', 'i should', 'first,', 'first ', 'got it', 'okay,', 'okay ', 
+        'alright,', 'alright ', 'the user', 'the request', 'the original', 'based on', 
+        'following the', "i'll", 'i will', 'now i', "here's", 'here is', 'my prompt',
+        'the prompt', 'this prompt', 'for this', 'to create', 'considering',
+        'compositionally', 'the image shows', 'this image shows', 'in this image',
+        'we see', 'i can see', 'looking at', 'analyzing', 'i notice', 'it appears',
+        'so,', 'so ', 'well,', 'well ', 'hmm', 'let\'s', 'now,', 'now ', 'sure,',
+        'certainly', 'of course', 'understood', 'i understand', 'right,', 'right ',
+        '**', '##',  # Markdown headers often indicate meta content
+    )
+    
+    # Check if starts with thinking pattern
+    if any(text_lower.startswith(pat) for pat in thinking_starts):
+        return True
+    
+    # Check for thinking patterns anywhere in short text
+    if len(text) < 200:
+        thinking_contains = ('let me', 'i need to', 'i should', 'i\'ll ', 'i will ')
+        if any(pat in text_lower for pat in thinking_contains):
+            return True
+    
+    return False
+
+def extract_clean_prompt(text: str) -> str:
+    """Extract clean prompt from GLM response, aggressively filtering thinking content."""
+    if not text:
+        return ""
+    
+    text = clean_glm_response(text)
+    
+    # Try to find content after common delimiters
+    delimiters = [
+        '\n\n---\n\n', '\n---\n', '---',
+        '\n\nPrompt:', '\nPrompt:', 'Prompt:',
+        '\n\nFinal:', '\nFinal:', 'Final:',
+        '\n\nResult:', '\nResult:', 'Result:',
+        '\n\nDescription:', '\nDescription:',
+    ]
+    
+    for delim in delimiters:
+        if delim in text:
+            parts = text.split(delim)
+            if len(parts) > 1:
+                candidate = parts[-1].strip()
+                if len(candidate) >= 50 and not is_thinking_text(candidate):
+                    return candidate.strip('"\'""')
+    
+    # Split into paragraphs
+    paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()]
+    
+    # Also split by single newlines if we didn't get good paragraphs
+    if len(paragraphs) <= 1:
+        paragraphs = [p.strip() for p in text.split('\n') if p.strip() and len(p.strip()) > 30]
+    
+    # Filter out thinking paragraphs
+    good_paragraphs = []
+    for p in paragraphs:
+        if len(p) < 40:
+            continue
+        if is_thinking_text(p):
+            continue
+        # Remove quotes
+        p = p.strip('"\'""')
+        good_paragraphs.append(p)
+    
+    # Return the longest good paragraph
+    if good_paragraphs:
+        result = max(good_paragraphs, key=len)
+        return result.strip('"\'""')
+    
+    # Fallback: try to get ANY paragraph that's long enough
+    valid = [p.strip('"\'""') for p in paragraphs if len(p) >= 50]
+    if valid:
+        # Prefer later paragraphs (more likely to be final answer)
+        return valid[-1]
+    
+    # Last resort: return cleaned original if it's long enough
+    cleaned = text.strip('"\'""')
+    if len(cleaned) >= 50 and not is_thinking_text(cleaned[:100]):
+        return cleaned
+    
+    return ""
+
 def analyze_image_with_glm(image: Optional[Image.Image]) -> str:
     """Analyze image using GLM-4V and return description."""
     if image is None:
         return "Please upload an image first."
-    
+
     client = get_glm_client()
     if not client:
         return "GLM API key not configured. Please add GLM_API_KEY to space secrets."
-    
+
     try:
         base64_image = encode_image_base64(image)
-        
+
         response = client.chat.completions.create(
             model="glm-4.6v-flash",
             messages=[
@@ -165,195 +260,126 @@ def analyze_image_with_glm(image: Optional[Image.Image]) -> str:
                         },
                         {
                             "type": "text",
-                            "text": """Write a detailed image description in 300-400 tokens.
+                            "text": """Describe this image in ONE paragraph, 80-120 words.
 
-FORMAT: One single paragraph. Start with the main subject. No analysis, no commentary, no "Compositionally", no meta-language.
+START DIRECTLY with the main subject. NO meta-language.
 
-DESCRIBE IN ORDER:
-- Main subject (person/object): appearance, clothing, pose, expression
-- Setting: environment, architecture, objects
-- Colors: specific hues, palette
-- Lighting: source, quality, shadows
-- Textures: materials like silk, marble, wood
-- Mood: atmosphere, emotion
-- Small details: background elements, decorations
+WRONG: "This image shows...", "I can see...", "The image depicts..."
+RIGHT: "A woman in red dress...", "Golden sunset over mountains...", "Vintage car parked..."
 
-EXAMPLE START: "A woman in a deep burgundy Victorian gown stands elegantly in a grand ballroom, her auburn hair pinned up with pearl accessories, delicate lace gloves adorning her hands..."
-
-DO NOT START WITH: "This image", "The image shows", "Compositionally", "In this", "We see"
-
-Write the description now:"""
+Include: subject, setting, colors, lighting, mood, key details.
+Output ONLY the description paragraph."""
                         }
                     ]
                 }
             ],
-            max_tokens=1200,
+            max_tokens=600,
         )
-        
+
         msg = response.choices[0].message
-        content = msg.content if msg.content else ""
-        
-        # GLM-4.6v returns thinking in reasoning_content and final answer in content
-        # If content is empty or too short, use reasoning_content
-        if len(content) < 200 and hasattr(msg, 'reasoning_content') and msg.reasoning_content:
-            # Use the full reasoning_content if content is insufficient
-            reasoning = msg.reasoning_content.strip()
-            # Try to find the actual description (longest coherent paragraph)
-            paragraphs = [p.strip() for p in reasoning.split('\n\n') if p.strip() and len(p) > 100]
-            if paragraphs:
-                # Find the paragraph that looks like a description (starts with subject, not analysis)
-                for p in paragraphs:
-                    if not p.lower().startswith(('compositionally', 'the image', 'this image', 'in this', 'we see', 'i can see')):
-                        content = p
-                        break
-                if not content or len(content) < 200:
-                    content = max(paragraphs, key=len)  # Use longest paragraph as fallback
+        content = clean_glm_response(msg.content) if msg.content else ""
         
+        # Try to extract clean content
         if content:
-            # Remove GLM special tokens
-            content = content.replace('<|begin_of_box|>', '').replace('<|end_of_box|>', '')
-            # Clean up any remaining artifacts
-            content = content.strip()
-            return content
-        return "Could not extract description from response."
-    except Exception:
-        return "Error analyzing image. Please try again."
-
-def extract_prompt_from_glm_response(text: str) -> str:
-    """Extract the actual prompt from GLM response, filtering out thinking/meta content."""
-    if not text:
-        return ""
-    
-    text = text.strip()
-    
-    # Clean GLM special tokens first
-    text = text.replace('<|begin_of_box|>', '').replace('<|end_of_box|>', '')
-    
-    # Split into paragraphs and filter
-    paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()]
-    
-    # Filter out thinking/meta paragraphs - these patterns indicate GLM is "thinking out loud"
-    thinking_patterns = (
-        'let me', 'i need', 'i should', 'first,', 'got it', 'okay,', 'alright,',
-        'the user', 'the request', 'the original', 'based on', 'following the',
-        'i\'ll', 'i will', 'now i', 'here\'s', 'here is', 'my prompt',
-        'the prompt', 'this prompt', 'for this', 'to create', 'considering',
-        'compositionally', 'the image', 'this image', 'in this', 'we see', 'i can see'
-    )
-    
-    # Find paragraphs that look like actual prompts (descriptive, starts with A/An or descriptive word)
-    good_paragraphs = []
-    for p in paragraphs:
-        p_lower = p.lower()
-        # Skip if it's thinking/meta content
-        if any(p_lower.startswith(pat) for pat in thinking_patterns):
-            continue
-        # Skip very short paragraphs
-        if len(p) < 50:
-            continue
-        good_paragraphs.append(p)
-    
-    # Return the longest good paragraph (most likely to be the actual prompt)
-    if good_paragraphs:
-        return max(good_paragraphs, key=len)
-    
-    # Fallback: if all paragraphs were filtered, take the longest one that's at least 50 chars
-    valid_paragraphs = [p for p in paragraphs if len(p) >= 50]
-    if valid_paragraphs:
-        return max(valid_paragraphs, key=len)
-    
-    return ""
+            extracted = extract_clean_prompt(content)
+            if extracted and len(extracted) >= 50:
+                content = extracted
+        
+        # If content is bad, try reasoning_content
+        if not content or len(content) < 50 or is_thinking_text(content):
+            if hasattr(msg, 'reasoning_content') and msg.reasoning_content:
+                extracted = extract_clean_prompt(msg.reasoning_content)
+                if extracted and len(extracted) >= 50:
+                    content = extracted
 
+        if content and len(content) >= 50:
+            return content
+        return "Could not extract description. Please try again."
+    except Exception as e:
+        return f"Error analyzing image: {str(e)}"
 
 def generate_prompt_with_glm(image_description: str, user_request: str, style: str) -> str:
     """Generate transformation prompt using GLM based on image description and user request."""
-    if not image_description or image_description.startswith("Please upload") or image_description.startswith("Error") or image_description.startswith("GLM API"):
+    if not image_description or image_description.startswith("Please") or image_description.startswith("Error") or image_description.startswith("GLM API") or image_description.startswith("Could not"):
         return "Please analyze the image first."
-    
+
     if not user_request or not user_request.strip():
         return "Please describe what changes you want."
-    
+
     client = get_glm_client()
     if not client:
         return "GLM API key not configured. Please add GLM_API_KEY to space secrets."
-    
-    style_hint = f" Apply {style} style." if style and style != "None" else ""
-    
-    # Truncate description if too long to leave room for prompt generation
-    desc = image_description[:1500] if len(image_description) > 1500 else image_description
-    
+
+    style_hint = f" Style: {style}." if style and style != "None" else ""
+    desc = image_description[:1200] if len(image_description) > 1200 else image_description
+
     try:
         response = client.chat.completions.create(
             model="glm-4.6v-flash",
             messages=[
                 {
                     "role": "user",
-                    "content": f"""Write an image prompt (100-150 words) describing this transformed scene.
+                    "content": f"""Write an image generation prompt (80-120 words) for this transformed scene.
 
-ORIGINAL: {desc}
+ORIGINAL IMAGE: {desc}
 
-CHANGE: {user_request}{style_hint}
+REQUESTED CHANGE: {user_request}{style_hint}
 
-Write the NEW scene description now. Start with the main subject. Describe lighting, colors, atmosphere, details. One paragraph only:"""
+RULES:
+- Describe the FINAL result, not the transformation process
+- Start with main subject, then setting, colors, lighting, mood
+- ONE paragraph only, no thinking, no explanation
+- Output ONLY the prompt text
+
+Write the prompt now:"""
                 }
             ],
-            max_tokens=600,
+            max_tokens=500,
         )
-        
+
         msg = response.choices[0].message
-        content = msg.content if msg.content else ""
         
-        # GLM-4.6v returns thinking in reasoning_content, final answer in content
-        # First try content, then extract from reasoning_content if needed
+        # Get both content and reasoning_content
+        content = clean_glm_response(msg.content) if msg.content else ""
+        reasoning = ""
+        if hasattr(msg, 'reasoning_content') and msg.reasoning_content:
+            reasoning = msg.reasoning_content
+        
+        # Try to extract from content first
+        result = ""
         if content:
-            content = content.strip()
-            # Clean GLM tokens
-            content = content.replace('<|begin_of_box|>', '').replace('<|end_of_box|>', '')
-            content = content.strip().strip('"\'""')
+            extracted = extract_clean_prompt(content)
+            if extracted and len(extracted) >= 50 and not is_thinking_text(extracted):
+                result = extracted
         
-        # If content is empty or looks like thinking, try reasoning_content
-        if not content or len(content) < 50 or content.lower().startswith(('let me', 'i need', 'first', 'got it', 'okay')):
-            if hasattr(msg, 'reasoning_content') and msg.reasoning_content:
-                extracted = extract_prompt_from_glm_response(msg.reasoning_content)
-                if extracted and len(extracted) >= 50:
-                    content = extracted
+        # If content extraction failed, try reasoning_content
+        if not result and reasoning:
+            extracted = extract_clean_prompt(reasoning)
+            if extracted and len(extracted) >= 50 and not is_thinking_text(extracted):
+                result = extracted
         
-        # Also check if we got a better result from reasoning even if content exists
-        if content and len(content) < 100 and hasattr(msg, 'reasoning_content') and msg.reasoning_content:
-            extracted = extract_prompt_from_glm_response(msg.reasoning_content)
-            if extracted and len(extracted) > len(content):
-                content = extracted
+        # Compare both and pick the better one
+        if result and reasoning and not is_thinking_text(result):
+            reasoning_extracted = extract_clean_prompt(reasoning)
+            # Prefer the one that's cleaner (doesn't start with thinking)
+            if reasoning_extracted and len(reasoning_extracted) > len(result):
+                if not is_thinking_text(reasoning_extracted):
+                    result = reasoning_extracted
         
-        if content:
-            # Final cleanup
-            content = content.strip().strip('"\'""')
-            # Remove any remaining thinking markers
-            if content.lower().startswith(('let me', 'i need', 'first,', 'got it', 'okay,')):
-                # Try to find the actual prompt after the thinking
-                lines = content.split('\n')
-                for i, line in enumerate(lines):
-                    line = line.strip()
-                    if line and len(line) > 50 and not line.lower().startswith(('let me', 'i need', 'first,', 'got it', 'okay,')):
-                        content = '\n'.join(lines[i:]).strip()
-                        break
-            
-            # Validate: at least 50 chars and doesn't look like meta content
-            if len(content) >= 50:
-                return content
+        if result and len(result) >= 50:
+            return result.strip('"\'""')
         
-        return "Could not generate a valid prompt. Please try again with a different request."
-    except Exception:
-        return "Error generating prompt. Please try again."
+        return "Could not generate prompt. Please try again with different input."
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 print("Loading Z-Image-Turbo pipeline...")
 
-# Load text-to-image pipeline
 pipe_t2i = DiffusionPipeline.from_pretrained(
     "Tongyi-MAI/Z-Image-Turbo",
 )
 pipe_t2i.to("cuda", torch.bfloat16)
 
-# Create img2img pipeline sharing components (no duplicate loading)
 pipe_i2i = ZImageImg2ImgPipeline(
     transformer=pipe_t2i.transformer,
     vae=pipe_t2i.vae,
@@ -412,7 +438,6 @@ EXAMPLES_GENERATE = [
     ["Cozy witch cottage interior on a stormy autumn night, iron cauldrons bubbling with green smoke, wooden shelves packed with glowing potion bottles and ancient spell books, a sleepy black cat curled by the stone fireplace, bundles of dried herbs and garlic hanging from dark oak ceiling beams, warm amber candlelight flickering throughout the room", "Digital Art", "1:1 MAX (2048x2048)", 9, 42, True],
 ]
 
-# Transform examples: [prompt, style, strength, steps, seed, randomize]
 EXAMPLES_TRANSFORM = [
     ["Transform into ultra realistic photograph with sharp details and natural lighting", "Photorealistic", 0.7, 9, 42, True],
     ["Dramatic movie scene with cinematic lighting and film grain texture", "Cinematic", 0.65, 9, 42, True],
@@ -444,25 +469,22 @@ def upload_to_hf_cdn(image: Optional[Image.Image]) -> str:
         return f"Upload failed: {response.status_code}"
     except requests.Timeout:
         return "Upload timed out. Please try again."
-    except Exception as e:
-        return f"Upload error. Please try again."
+    except Exception:
+        return "Upload error. Please try again."
 
 def do_polish_prompt(prompt: str, style: str, do_polish: bool, mode: str = "generate") -> Tuple[str, str]:
     """Polish prompt before generation (runs on CPU, before GPU allocation)."""
     if not prompt or not prompt.strip():
         return "", ""
-    
+
     base_prompt = prompt.strip()
-    
-    # Polish if enabled
+
     if do_polish:
         polished = polish_prompt(base_prompt, mode=mode)
     else:
         polished = base_prompt
-    
-    # Add style suffix for final prompt
+
     final_prompt = polished + STYLE_SUFFIXES.get(style, "")
-    
     return final_prompt, polished
 
 def do_polish_transform_prompt(prompt: str, style: str, do_polish: bool) -> Tuple[str, str]:
@@ -471,7 +493,7 @@ def do_polish_transform_prompt(prompt: str, style: str, do_polish: bool) -> Tupl
         base = prompt.strip() if prompt else "high quality image"
         final = base + STYLE_SUFFIXES.get(style, "")
         return final, ""
-    
+
     return do_polish_prompt(prompt, style, True, mode="transform")
 
 @spaces.GPU
@@ -479,12 +501,12 @@ def generate(full_prompt: str, polished_display: str, ratio: str, steps: int, se
     if randomize:
         seed = torch.randint(0, 2**32 - 1, (1,)).item()
     seed = int(seed)
-    
+
     if not full_prompt or not full_prompt.strip():
         return None, seed
-    
+
     w, h = RATIO_DIMS.get(ratio, (1024, 1024))
-    
+
     generator = torch.Generator("cuda").manual_seed(seed)
     image = pipe_t2i(
         prompt=full_prompt,
@@ -494,36 +516,32 @@ def generate(full_prompt: str, polished_display: str, ratio: str, steps: int, se
         guidance_scale=0.0,
         generator=generator,
     ).images[0]
-    
+
     return image, seed
 
 @spaces.GPU
 def transform(input_image: Optional[Image.Image], full_prompt: str, polished_display: str, strength: float, steps: int, seed: int, randomize: bool, progress=gr.Progress(track_tqdm=True)) -> Tuple[Optional[Image.Image], int]:
     if input_image is None:
         return None, 0
-    
+
     if randomize:
         seed = torch.randint(0, 2**32 - 1, (1,)).item()
     seed = int(seed)
-    
+
     if not full_prompt or not full_prompt.strip():
         full_prompt = "high quality image, enhanced details"
-    
-    # Resize to supported dimensions
+
     input_image = input_image.convert("RGB")
     w, h = input_image.size
-    # Round to nearest multiple of 16
     w = (w // 16) * 16
     h = (h // 16) * 16
     w = max(512, min(2048, w))
     h = max(512, min(2048, h))
     input_image = input_image.resize((w, h), Image.LANCZOS)
-    
-    # Adjust steps to compensate for strength (actual_steps = internal_steps * strength)
-    # So we need internal_steps = desired_steps / strength
+
     strength = float(strength)
     effective_steps = max(4, int(steps / strength)) if strength > 0 else int(steps)
-    
+
     generator = torch.Generator("cuda").manual_seed(seed)
     image = pipe_i2i(
         prompt=full_prompt,
@@ -533,66 +551,47 @@ def transform(input_image: Optional[Image.Image], full_prompt: str, polished_dis
         guidance_scale=0.0,
         generator=generator,
     ).images[0]
-    
+
     return image, seed
 
 css = r"""
 :root {
-    /* Dark theme - Modern AI aesthetic */
     --bg-primary: #0c0c0e;
     --bg-secondary: #141416;
     --bg-tertiary: #1c1c20;
     --surface: #232328;
     --surface-hover: #2a2a30;
-    
-    /* Accent colors - Purple/Indigo gradient */
     --accent-primary: #818cf8;
     --accent-secondary: #a78bfa;
     --accent-hover: #6366f1;
     --accent-gradient: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%);
     --accent-glow: rgba(99, 102, 241, 0.4);
-    
-    /* Text hierarchy */
     --text-primary: #f4f4f5;
     --text-secondary: #a1a1aa;
     --text-muted: #71717a;
-    
-    /* Borders */
     --border-subtle: rgba(255, 255, 255, 0.08);
     --border-default: rgba(255, 255, 255, 0.12);
-    
-    /* Status colors */
     --success: #10b981;
     --warning: #f59e0b;
     --error: #ef4444;
-    
-    /* Shadows */
     --shadow-sm: 0 1px 2px rgba(0,0,0,0.3);
     --shadow-md: 0 4px 6px -1px rgba(0,0,0,0.4);
     --shadow-lg: 0 10px 15px -3px rgba(0,0,0,0.5);
     --shadow-glow: 0 0 20px var(--accent-glow);
-    
-    /* Spacing & Radius */
     --radius-sm: 8px;
     --radius-md: 12px;
     --radius-lg: 16px;
     --transition: 0.2s ease;
 }
 
-/* Base container */
 .gradio-container {
     background: var(--bg-primary) !important;
     min-height: 100vh;
     color: var(--text-primary);
 }
 
-/* Tab container */
-.tabs {
-    background: transparent !important;
-    padding: 8px 0;
-}
+.tabs { background: transparent !important; padding: 8px 0; }
 
-/* Tab navigation - Pill style */
 .tab-nav {
     background: var(--bg-secondary) !important;
     border: 1px solid var(--border-subtle) !important;
@@ -622,11 +621,6 @@ css = r"""
     color: var(--text-primary) !important;
 }
 
-.tab-nav > button:focus-visible {
-    outline: 2px solid var(--accent-primary);
-    outline-offset: 2px;
-}
-
 .tab-nav > button.selected,
 .tab-nav > button[aria-selected="true"],
 [role="tab"][aria-selected="true"] {
@@ -636,7 +630,6 @@ css = r"""
     box-shadow: var(--shadow-glow);
 }
 
-/* Primary button */
 button.primary, .primary {
     background: var(--accent-gradient) !important;
     border: none !important;
@@ -654,16 +647,6 @@ button.primary:hover, .primary:hover {
     filter: brightness(1.1);
 }
 
-button.primary:focus-visible, .primary:focus-visible {
-    outline: 2px solid var(--accent-secondary);
-    outline-offset: 2px;
-}
-
-button.primary:active, .primary:active {
-    transform: scale(0.98);
-}
-
-/* Secondary button */
 button.secondary, .secondary {
     background: var(--surface) !important;
     color: var(--text-primary) !important;
@@ -678,12 +661,6 @@ button.secondary:hover, .secondary:hover {
     border-color: var(--accent-primary) !important;
 }
 
-button.secondary:focus-visible, .secondary:focus-visible {
-    outline: 2px solid var(--accent-primary);
-    outline-offset: 2px;
-}
-
-/* Content blocks */
 .block {
     background: var(--bg-secondary) !important;
     border: 1px solid var(--border-subtle) !important;
@@ -694,17 +671,8 @@ button.secondary:focus-visible, .secondary:focus-visible {
     transition: all var(--transition);
 }
 
-.block:hover {
-    border-color: var(--border-default) !important;
-}
-
-/* Tab content area */
-.tabitem {
-    background: transparent !important;
-    padding: 16px 0;
-}
+.tabitem { background: transparent !important; padding: 16px 0; }
 
-/* Input fields */
 input, textarea, .gr-input, .gr-textbox textarea {
     background: var(--bg-tertiary) !important;
     border: 1px solid var(--border-default) !important;
@@ -713,13 +681,12 @@ input, textarea, .gr-input, .gr-textbox textarea {
     transition: all var(--transition);
 }
 
-input:focus, textarea:focus, .gr-input:focus, .gr-textbox textarea:focus {
+input:focus, textarea:focus {
     border-color: var(--accent-primary) !important;
     box-shadow: 0 0 0 3px rgba(99, 102, 241, 0.2) !important;
     outline: none !important;
 }
 
-/* Dropdown */
 .gr-dropdown, select {
     background: var(--bg-tertiary) !important;
     border: 1px solid var(--border-default) !important;
@@ -727,23 +694,11 @@ input:focus, textarea:focus, .gr-input:focus, .gr-textbox textarea:focus {
     color: var(--text-primary) !important;
 }
 
-/* Slider */
-.gr-slider input[type="range"] {
-    accent-color: var(--accent-primary);
-}
+.gr-slider input[type="range"] { accent-color: var(--accent-primary); }
+.gr-checkbox input[type="checkbox"] { accent-color: var(--accent-primary); }
 
-/* Checkbox */
-.gr-checkbox input[type="checkbox"] {
-    accent-color: var(--accent-primary);
-}
-
-/* Labels */
-label, .gr-label {
-    color: var(--text-secondary) !important;
-    font-weight: 500;
-}
+label, .gr-label { color: var(--text-secondary) !important; font-weight: 500; }
 
-/* Image container */
 .gr-image, .image-container {
     background: var(--bg-tertiary) !important;
     border: 2px dashed var(--border-default) !important;
@@ -751,77 +706,26 @@ label, .gr-label {
     transition: all var(--transition);
 }
 
-.gr-image:hover, .image-container:hover {
-    border-color: var(--accent-primary) !important;
-}
-
-/* Generated image output */
-.gr-image img {
-    border-radius: var(--radius-md);
-}
-
-/* Examples table */
-[class*="examples"] td:first-child {
-    text-align: left;
-}
-
-[class*="examples"] table {
-    background: var(--bg-secondary) !important;
-    border-radius: var(--radius-md);
-}
-
-[class*="examples"] tr {
-    background: transparent !important;
-    border-bottom: 1px solid var(--border-subtle) !important;
-}
-
-[class*="examples"] tr:hover {
-    background: var(--bg-tertiary) !important;
-}
-
-[class*="examples"] td {
-    color: var(--text-secondary) !important;
-    padding: 12px !important;
-}
+.gr-image:hover { border-color: var(--accent-primary) !important; }
+.gr-image img { border-radius: var(--radius-md); }
 
-/* Examples header row */
-[class*="examples"] thead,
-[class*="examples"] thead tr,
-[class*="examples"] thead th {
+[class*="examples"] td:first-child { text-align: left; }
+[class*="examples"] table { background: var(--bg-secondary) !important; border-radius: var(--radius-md); }
+[class*="examples"] tr { background: transparent !important; border-bottom: 1px solid var(--border-subtle) !important; }
+[class*="examples"] tr:hover { background: var(--bg-tertiary) !important; }
+[class*="examples"] td { color: var(--text-secondary) !important; padding: 12px !important; }
+[class*="examples"] thead, [class*="examples"] thead tr, [class*="examples"] thead th {
     background: var(--surface) !important;
     color: var(--text-primary) !important;
     border-bottom: 1px solid var(--border-default) !important;
 }
 
-[class*="examples"] thead th {
-    padding: 12px !important;
-    font-weight: 600;
-}
-
-/* Headers */
-h1, h2, h3, h4 {
-    color: var(--text-primary) !important;
-}
+h1, h2, h3, h4 { color: var(--text-primary) !important; }
+h1 { font-size: clamp(1.5rem, 4vw, 2.2rem); font-weight: 700; }
 
-h1 {
-    font-size: clamp(1.5rem, 4vw, 2.2rem);
-    font-weight: 700;
-}
+.markdown-text, .gr-markdown { color: var(--text-secondary) !important; }
+.gr-markdown a { color: var(--accent-primary) !important; }
 
-/* Markdown */
-.markdown-text, .gr-markdown {
-    color: var(--text-secondary) !important;
-}
-
-.gr-markdown a {
-    color: var(--accent-primary) !important;
-}
-
-.gr-markdown a:hover {
-    color: var(--accent-secondary) !important;
-}
-
-/* Group styling */
 .gr-group {
     background: var(--surface) !important;
     border: 1px solid var(--border-subtle) !important;
@@ -829,20 +733,13 @@ h1 {
     padding: 16px !important;
 }
 
-/* Accordion */
 .gr-accordion {
     background: var(--bg-secondary) !important;
     border: 1px solid var(--border-subtle) !important;
     border-radius: var(--radius-md) !important;
 }
 
-/* Footer */
-.footer-no-box {
-    background: transparent !important;
-    border: none !important;
-    box-shadow: none !important;
-    padding: 0;
-}
+.footer-no-box { background: transparent !important; border: none !important; box-shadow: none !important; padding: 0; }
 
 .gradio-container > footer {
     background: var(--bg-secondary) !important;
@@ -850,447 +747,84 @@ h1 {
     padding: 12px 20px;
 }
 
-.gradio-container > footer span,
-.gradio-container > footer p {
-    color: var(--text-muted) !important;
-}
-
-.gradio-container > footer a {
-    color: var(--accent-primary) !important;
-    text-decoration: none;
-    transition: color var(--transition);
-}
-
-.gradio-container > footer a:hover {
-    color: var(--accent-secondary) !important;
-}
-
-/* Progress bar */
-.progress-bar {
-    background: var(--bg-tertiary) !important;
-    border-radius: 4px;
-}
+.gradio-container > footer span, .gradio-container > footer p { color: var(--text-muted) !important; }
+.gradio-container > footer a { color: var(--accent-primary) !important; }
 
-.progress-bar > div {
-    background: var(--accent-gradient) !important;
-    border-radius: 4px;
-}
+.progress-bar { background: var(--bg-tertiary) !important; border-radius: 4px; }
+.progress-bar > div { background: var(--accent-gradient) !important; border-radius: 4px; }
 
-/* Accessibility: Reduced motion */
 @media (prefers-reduced-motion: reduce) {
-    *, *::before, *::after {
-        animation-duration: 0.01ms !important;
-        animation-iteration-count: 1 !important;
-        transition-duration: 0.01ms !important;
-    }
+    *, *::before, *::after { animation-duration: 0.01ms !important; transition-duration: 0.01ms !important; }
 }
 
-/* Mobile responsiveness */
 @media (max-width: 768px) {
-    .tab-nav {
-        padding: 4px;
-        gap: 4px;
-    }
-    
-    .tab-nav > button {
-        padding: 10px 16px;
-        font-size: 0.85rem;
-    }
-    
-    .block {
-        padding: 12px;
-        margin: 6px 0;
-    }
-    
-    button.primary, .primary {
-        padding: 10px 16px;
-        width: 100%;
-    }
-    
-    h1 {
-        font-size: 1.4rem !important;
-    }
-}
-
-@media (max-width: 480px) {
-    .tab-nav > button {
-        padding: 8px 12px;
-        font-size: 0.8rem;
-    }
-    
-    .block {
-        padding: 10px;
-        border-radius: var(--radius-md) !important;
-    }
-}
-
-/* Custom scrollbar */
-::-webkit-scrollbar {
-    width: 8px;
-    height: 8px;
-}
-
-::-webkit-scrollbar-track {
-    background: var(--bg-secondary);
-}
-
-::-webkit-scrollbar-thumb {
-    background: var(--bg-tertiary);
-    border-radius: 4px;
-}
-
-::-webkit-scrollbar-thumb:hover {
-    background: var(--surface);
-}
-
-/* ===== TEXT VISIBILITY FIXES ===== */
-/* Ensure all text elements are readable on dark background */
-
-/* Tab navigation buttons - CRITICAL */
-.tab-nav button,
-.tab-nav > button,
-.tabs .tab-nav button,
-div[role="tablist"] button,
-button[role="tab"] {
-    color: #e5e5e5 !important;
-    font-weight: 500 !important;
-}
-
-.tab-nav button:hover,
-.tab-nav > button:hover,
-button[role="tab"]:hover {
-    color: #ffffff !important;
-}
-
-.tab-nav button.selected,
-.tab-nav > button.selected,
-.tab-nav button[aria-selected="true"],
-button[role="tab"][aria-selected="true"] {
-    color: #ffffff !important;
-}
-
-/* All labels - make visible */
-label,
-.gr-label,
-.label-wrap,
-.label-wrap span,
-span.svelte-1gfkn6j,
-.gr-input-label,
-.gr-box label,
-.gr-form label,
-.wrap label,
-.container label {
-    color: #d4d4d8 !important;
-}
-
-/* Specific Gradio label classes */
-label span,
-.gr-label span,
-.label-wrap > span,
-span[data-testid="block-label"],
-.block-label span {
-    color: #d4d4d8 !important;
-}
-
-/* Span elements that might be labels */
-span.block-label,
-span.svelte-1gfkn6j,
-.gr-block-label,
-.svelte-1gfkn6j {
-    color: #d4d4d8 !important;
-}
-
-/* Input and textbox labels */
-.gr-textbox label,
-.gr-dropdown label,
-.gr-slider label,
-.gr-checkbox label,
-.gr-number label,
-.gr-image label {
-    color: #d4d4d8 !important;
-}
-
-/* Accordion headers */
-.gr-accordion summary,
-.gr-accordion .label-wrap,
-.gr-accordion button,
-.accordion summary,
-summary span,
-details summary,
-details summary span,
-.gr-accordion > div:first-child,
-button[aria-expanded] span {
-    color: #e5e5e5 !important;
-}
-
-/* Examples table - headers and cells */
-.gr-examples th,
-.gr-examples thead th,
-.examples th,
-.examples thead th,
-table th,
-[class*="examples"] th,
-[class*="examples"] thead th,
-.gr-samples-table th,
-.gr-samples th {
-    color: #f4f4f5 !important;
-    background: var(--surface) !important;
-    font-weight: 600 !important;
-}
-
-/* Examples table cells */
-.gr-examples td,
-.examples td,
-table td,
-[class*="examples"] td,
-.gr-samples-table td,
-.gr-samples td {
-    color: #d4d4d8 !important;
-}
-
-/* Examples table row text */
-.gr-examples tr,
-.examples tr,
-[class*="examples"] tr {
-    color: #d4d4d8 !important;
-}
-
-/* Markdown text */
-.gr-markdown,
-.gr-markdown p,
-.gr-markdown span,
-.markdown-text,
-.prose,
-.prose p {
-    color: #a1a1aa !important;
-}
-
-/* Checkbox labels */
-.gr-checkbox span,
-.gr-checkbox label span,
-input[type="checkbox"] + span,
-.checkbox-wrap span,
-.gr-check-radio label span {
-    color: #d4d4d8 !important;
-}
-
-/* Dropdown text */
-.gr-dropdown select,
-.gr-dropdown option,
-select option,
-.dropdown-container span {
-    color: #e5e5e5 !important;
-}
-
-/* Number input labels */
-.gr-number span,
-.gr-number label span {
-    color: #d4d4d8 !important;
-}
-
-/* Slider labels */
-.gr-slider span,
-.gr-slider label span,
-.slider-wrap span {
-    color: #d4d4d8 !important;
-}
-
-/* Image upload labels */
-.gr-image span,
-.gr-image label span,
-.image-container span {
-    color: #d4d4d8 !important;
-}
-
-/* File upload text */
-.gr-file span,
-.gr-file label,
-.upload-text {
-    color: #d4d4d8 !important;
-}
-
-/* Button text (ensure readable) */
-button span,
-.gr-button span {
-    color: inherit !important;
-}
-
-/* Secondary button text */
-button.secondary span,
-.secondary span {
-    color: #e5e5e5 !important;
-}
-
-/* Form group labels */
-.gr-group label,
-.gr-group span,
-.gr-box label,
-.gr-box span.label {
-    color: #d4d4d8 !important;
-}
-
-/* Info text and hints */
-.info-text,
-.gr-info,
-.hint,
-small {
-    color: #a1a1aa !important;
-}
-
-/* Error and warning text */
-.error-text,
-.gr-error {
-    color: #ef4444 !important;
-}
-
-.warning-text,
-.gr-warning {
-    color: #f59e0b !important;
-}
-
-/* Textbox placeholder */
-input::placeholder,
-textarea::placeholder {
-    color: #71717a !important;
-}
-
-/* Selected/active states */
-.selected span,
-.active span,
-[aria-selected="true"] span {
-    color: #ffffff !important;
-}
-
-/* Modal and popup text */
-.modal span,
-.popup span,
-.dialog span {
-    color: #e5e5e5 !important;
-}
-
-/* Generic fix for any remaining dark text on dark background */
-.dark span:not([style*="color"]),
-.dark label:not([style*="color"]) {
-    color: #d4d4d8 !important;
-}
-
-/* ========== TEXT VISIBILITY MASTER FIX ========== */
-/* Force all text to be readable on dark background */
-
-/* Global text color reset */
-.gradio-container, .gradio-container * {
-    color: #e5e5e5;
-}
-
-/* Tab buttons - MUST be visible */
-.tab-nav button,
-.tab-nav > button,
-button[role="tab"],
-.tabs button {
-    color: #e5e5e5 !important;
-}
-
-/* All labels */
-label, .gr-label, .label-wrap, .label-wrap span,
-.gr-box label, .gr-form label, .gr-group label,
-[class*="label"], [class*="Label"] {
-    color: #d4d4d8 !important;
-}
-
-/* All spans in form context */
-.gr-block span, .gr-box span, .gr-form span,
-.gr-group span, .block span {
-    color: #d4d4d8 !important;
-}
-
-/* Input labels specifically */
-.gr-textbox label, .gr-dropdown label, .gr-slider label,
-.gr-checkbox label, .gr-number label, .gr-image label {
-    color: #d4d4d8 !important;
-}
-
-/* Examples table */
-table thead, table thead tr, table thead th,
-[class*="examples"] thead th,
-.examples-table th, .example-table th {
-    background: #2a2a30 !important;
-    color: #f4f4f5 !important;
-}
-
-table tbody td, [class*="examples"] td {
-    color: #d4d4d8 !important;
-}
-
-/* Accordion headers */
-.gr-accordion summary, .gr-accordion button,
-details summary, summary span,
-[class*="accordion"] summary {
-    color: #e5e5e5 !important;
-}
-
-/* Markdown text */
-.gr-markdown, .gr-markdown p, .gr-markdown li,
-.markdown-text, .prose {
-    color: #d4d4d8 !important;
-}
-
-/* Placeholder text */
-input::placeholder, textarea::placeholder {
-    color: #71717a !important;
-}
-
-/* Secondary button text */
-button.secondary, .secondary {
-    color: #e5e5e5 !important;
-}
+    .tab-nav { padding: 4px; gap: 4px; }
+    .tab-nav > button { padding: 10px 16px; font-size: 0.85rem; }
+    .block { padding: 12px; margin: 6px 0; }
+    button.primary { padding: 10px 16px; width: 100%; }
+    h1 { font-size: 1.4rem !important; }
+}
+
+::-webkit-scrollbar { width: 8px; height: 8px; }
+::-webkit-scrollbar-track { background: var(--bg-secondary); }
+::-webkit-scrollbar-thumb { background: var(--bg-tertiary); border-radius: 4px; }
+::-webkit-scrollbar-thumb:hover { background: var(--surface); }
+
+.gradio-container, .gradio-container * { color: #e5e5e5; }
+.tab-nav button, .tab-nav > button, button[role="tab"], .tabs button { color: #e5e5e5 !important; }
+label, .gr-label, .label-wrap, .label-wrap span, .gr-box label, .gr-form label, .gr-group label { color: #d4d4d8 !important; }
+.gr-block span, .gr-box span, .gr-form span, .gr-group span, .block span { color: #d4d4d8 !important; }
+table thead, table thead tr, table thead th, [class*="examples"] thead th { background: #2a2a30 !important; color: #f4f4f5 !important; }
+table tbody td, [class*="examples"] td { color: #d4d4d8 !important; }
+.gr-accordion summary, .gr-accordion button, details summary, summary span { color: #e5e5e5 !important; }
+.gr-markdown, .gr-markdown p, .gr-markdown li, .markdown-text, .prose { color: #d4d4d8 !important; }
+input::placeholder, textarea::placeholder { color: #71717a !important; }
+button.secondary, .secondary { color: #e5e5e5 !important; }
 """
 
 with gr.Blocks(title="Z Image Turbo", css=css, theme=gr.themes.Base()) as demo:
     gr.HTML("""
     <div style="text-align: center; padding: 24px 16px 16px 16px;">
         <h1 style="background: linear-gradient(135deg, #818cf8 0%, #a78bfa 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; font-size: clamp(1.5rem, 4vw, 2.2rem); margin-bottom: 8px; font-weight: 700;">
-            ⚡ Z Image Turbo + GLM-4.6V
+            Z Image Turbo + GLM-4.6V
         </h1>
         <p style="color: #a1a1aa; font-size: 1rem; margin: 0;">
             AI Image Generation & Transformation powered by DeepSeek Reasoning
         </p>
         <p style="color: #f472b6; font-size: 0.9rem; margin-top: 12px;">
-            If you liked it, please ❤️ it. Thank you!
+            If you liked it, please like it. Thank you!
         </p>
     </div>
     """)
     
     with gr.Tabs():
         # TAB 1: Generate Image
-        with gr.Tab("🎨 Generate"):
+        with gr.Tab("Generate"):
             with gr.Row():
                 with gr.Column(scale=2):
-                    gen_prompt = gr.Textbox(label="✏️ Prompt", placeholder="Describe your image in detail...", lines=4)
-                    gen_polish = gr.Checkbox(label="✨ Prompt+ by deepseek-reasoner", value=False)
+                    gen_prompt = gr.Textbox(label="Prompt", placeholder="Describe your image in detail...", lines=4)
+                    gen_polish = gr.Checkbox(label="Prompt+ by deepseek-reasoner", value=False)
                     with gr.Row():
-                        gen_style = gr.Dropdown(choices=STYLES, value="None", label="🎨 Style")
-                        gen_ratio = gr.Dropdown(choices=RATIOS, value="1:1 Square (1024x1024)", label="📐 Aspect Ratio")
-                    with gr.Accordion("⚙️ Advanced Settings", open=False):
+                        gen_style = gr.Dropdown(choices=STYLES, value="None", label="Style")
+                        gen_ratio = gr.Dropdown(choices=RATIOS, value="1:1 Square (1024x1024)", label="Aspect Ratio")
+                    with gr.Accordion("Advanced Settings", open=False):
                         gen_steps = gr.Slider(minimum=4, maximum=16, value=9, step=1, label="Steps")
                         with gr.Row():
                             gen_seed = gr.Number(label="Seed", value=42, precision=0)
                             gen_randomize = gr.Checkbox(label="Random Seed", value=True)
-                    gen_btn = gr.Button("🚀 Generate", variant="primary", size="lg")
+                    gen_btn = gr.Button("Generate", variant="primary", size="lg")
                 
                 with gr.Column(scale=3):
                     gen_output = gr.Image(label="Generated Image", type="pil", format="png", interactive=False, height=512)
-                    gen_polished_prompt = gr.Textbox(label="✨ Enhanced Prompt", interactive=False, visible=True, lines=4)
+                    gen_polished_prompt = gr.Textbox(label="Enhanced Prompt", interactive=False, visible=True, lines=4)
                     gen_seed_out = gr.Number(label="Seed Used", interactive=False)
                     with gr.Row():
-                        gen_share_btn = gr.Button("📤 Share", variant="secondary")
+                        gen_share_btn = gr.Button("Share", variant="secondary")
                         gen_share_link = gr.Textbox(label="", interactive=False, show_copy_button=True, show_label=False)
             
-            # Hidden state to pass polished prompt to generate
             gen_full_prompt = gr.State("")
-            
             gr.Examples(examples=EXAMPLES_GENERATE, inputs=[gen_prompt, gen_style, gen_ratio, gen_steps, gen_seed, gen_randomize])
             
-            # Chain: First polish prompt (CPU), then generate (GPU)
             gen_btn.click(
                 fn=do_polish_prompt,
                 inputs=[gen_prompt, gen_style, gen_polish],
@@ -1312,23 +846,23 @@ with gr.Blocks(title="Z Image Turbo", css=css, theme=gr.themes.Base()) as demo:
             gen_share_btn.click(fn=upload_to_hf_cdn, inputs=[gen_output], outputs=[gen_share_link])
         
         # TAB 2: AI Vision Assistant
-        with gr.Tab("🤖 AI Assistant"):
+        with gr.Tab("AI Assistant"):
             gr.Markdown("**AI-Powered Prompt Generator** - Upload an image, analyze it with GLM-4.6V, then generate optimized prompts.")
             
             with gr.Row():
                 with gr.Column(scale=1):
-                    ai_image = gr.Image(label="📷 Upload Image", type="pil", height=300)
-                    ai_analyze_btn = gr.Button("🔍 Analyze Image", variant="primary")
-                    ai_description = gr.Textbox(label="📝 Image Description", lines=5, interactive=False)
+                    ai_image = gr.Image(label="Upload Image", type="pil", height=300)
+                    ai_analyze_btn = gr.Button("Analyze Image", variant="primary")
+                    ai_description = gr.Textbox(label="Image Description", lines=5, interactive=False)
                 
                 with gr.Column(scale=1):
-                    ai_request = gr.Textbox(label="✏️ What changes do you want?", placeholder="e.g., 'watercolor style' or 'dramatic sunset lighting'", lines=2)
-                    ai_style = gr.Dropdown(choices=STYLES, value="None", label="🎨 Target Style")
-                    ai_generate_btn = gr.Button("✨ Generate Prompt", variant="primary")
-                    ai_generated_prompt = gr.Textbox(label="🎯 Generated Prompt", lines=6, interactive=False)
-                    ai_send_btn = gr.Button("🚀 Send to Transform Tab", variant="primary")
+                    ai_request = gr.Textbox(label="What changes do you want?", placeholder="e.g., 'watercolor style' or 'dramatic sunset lighting'", lines=2)
+                    ai_style = gr.Dropdown(choices=STYLES, value="None", label="Target Style")
+                    ai_generate_btn = gr.Button("Generate Prompt", variant="primary")
+                    ai_generated_prompt = gr.Textbox(label="Generated Prompt", lines=6, interactive=False)
+                    ai_send_btn = gr.Button("Send to Transform Tab", variant="primary")
             
-            with gr.Accordion("💡 How to Use", open=False):
+            with gr.Accordion("How to Use", open=False):
                 gr.Markdown("""
 1. **Upload** an image and click "Analyze Image"
 2. **Describe** the changes you want
@@ -1336,7 +870,6 @@ with gr.Blocks(title="Z Image Turbo", css=css, theme=gr.themes.Base()) as demo:
 4. **Send** to Transform tab to apply changes
                 """)
             
-            # Event handlers for AI Assistant
             ai_analyze_btn.click(
                 fn=analyze_image_with_glm,
                 inputs=[ai_image],
@@ -1349,39 +882,37 @@ with gr.Blocks(title="Z Image Turbo", css=css, theme=gr.themes.Base()) as demo:
             )
         
         # TAB 3: Transform Image
-        with gr.Tab("✨ Transform"):
+        with gr.Tab("Transform"):
             gr.Markdown("**Transform your image** - Upload and describe the transformation. Lower strength = subtle, higher = dramatic.")
             
             with gr.Row():
                 with gr.Column(scale=2):
-                    trans_input = gr.Image(label="📷 Upload Image", type="pil", height=300)
-                    trans_prompt = gr.Textbox(label="✏️ Transformation Prompt", placeholder="e.g., 'oil painting style, vibrant colors'", lines=3)
-                    trans_polish = gr.Checkbox(label="✨ Prompt+ by deepseek-reasoner", value=False)
+                    trans_input = gr.Image(label="Upload Image", type="pil", height=300)
+                    trans_prompt = gr.Textbox(label="Transformation Prompt", placeholder="e.g., 'oil painting style, vibrant colors'", lines=3)
+                    trans_polish = gr.Checkbox(label="Prompt+ by deepseek-reasoner", value=False)
                     with gr.Row():
-                        trans_style = gr.Dropdown(choices=STYLES, value="None", label="🎨 Style")
-                        trans_strength = gr.Slider(minimum=0.1, maximum=1.0, value=0.6, step=0.05, label="💪 Strength")
-                    with gr.Accordion("⚙️ Advanced Settings", open=False):
+                        trans_style = gr.Dropdown(choices=STYLES, value="None", label="Style")
+                        trans_strength = gr.Slider(minimum=0.1, maximum=1.0, value=0.6, step=0.05, label="Strength")
+                    with gr.Accordion("Advanced Settings", open=False):
                         trans_steps = gr.Slider(minimum=4, maximum=16, value=9, step=1, label="Steps")
                         with gr.Row():
                             trans_seed = gr.Number(label="Seed", value=42, precision=0)
                             trans_randomize = gr.Checkbox(label="Random Seed", value=True)
-                    trans_btn = gr.Button("🚀 Transform", variant="primary", size="lg")
+                    trans_btn = gr.Button("Transform", variant="primary", size="lg")
                 
                 with gr.Column(scale=3):
                     trans_output = gr.Image(label="Transformed Image", type="pil", format="png", interactive=False, height=512)
-                    trans_polished_prompt = gr.Textbox(label="✨ Enhanced Prompt", interactive=False, visible=True, lines=4)
+                    trans_polished_prompt = gr.Textbox(label="Enhanced Prompt", interactive=False, visible=True, lines=4)
                     trans_seed_out = gr.Number(label="Seed Used", interactive=False)
                     with gr.Row():
-                        trans_share_btn = gr.Button("📤 Share", variant="secondary")
+                        trans_share_btn = gr.Button("Share", variant="secondary")
                         trans_share_link = gr.Textbox(label="", interactive=False, show_copy_button=True, show_label=False)
             
-            # Hidden state to pass polished prompt to transform
             trans_full_prompt = gr.State("")
             
-            with gr.Accordion("💡 Example Prompts", open=False):
+            with gr.Accordion("Example Prompts", open=False):
                 gr.Examples(examples=EXAMPLES_TRANSFORM, inputs=[trans_prompt, trans_style, trans_strength, trans_steps, trans_seed, trans_randomize])
             
-            # Chain: First polish prompt (CPU), then transform (GPU)
             trans_btn.click(
                 fn=do_polish_transform_prompt,
                 inputs=[trans_prompt, trans_style, trans_polish],
@@ -1402,20 +933,19 @@ with gr.Blocks(title="Z Image Turbo", css=css, theme=gr.themes.Base()) as demo:
             )
             trans_share_btn.click(fn=upload_to_hf_cdn, inputs=[trans_output], outputs=[trans_share_link])
         
-        # Cross-tab handler: Send from AI Assistant to Transform
+        # Cross-tab handler
         ai_send_btn.click(
             fn=lambda prompt, img: (prompt, img),
             inputs=[ai_generated_prompt, ai_image],
             outputs=[trans_prompt, trans_input]
         )
     
-    # Footer
     gr.HTML(
         """
         <div style="text-align: center; width: 100%; font-size: 0.9rem; padding: 1rem; margin-top: 1.5rem; background: #141416; border: 1px solid rgba(255,255,255,0.08); border-radius: 12px; color: #71717a;">
             <strong style="color: #a1a1aa;">Models:</strong> 
-            <a href="https://huggingface.co/Tongyi-MAI/Z-Image-Turbo" target="_blank" style="color: #818cf8; font-weight: 500;">Z-Image-Turbo</a> • 
-            <a href="https://huggingface.co/zai-org/GLM-4.6V" target="_blank" style="color: #818cf8; font-weight: 500;">GLM-4.6V</a> • 
+            <a href="https://huggingface.co/Tongyi-MAI/Z-Image-Turbo" target="_blank" style="color: #818cf8; font-weight: 500;">Z-Image-Turbo</a> | 
+            <a href="https://huggingface.co/zai-org/GLM-4.6V" target="_blank" style="color: #818cf8; font-weight: 500;">GLM-4.6V</a> | 
             <strong style="color: #a1a1aa;">by</strong> 
             <a href="https://huggingface.co/lulavc" target="_blank" style="color: #a78bfa; font-weight: 600;">@lulavc</a>
         </div>