Update app.py

app.py

@@ -8,7 +8,6 @@ from diffusers import DiffusionPipeline
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Initialize the model
 pipe = DiffusionPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-schnell",
     torch_dtype=dtype
@@ -17,7 +16,6 @@ pipe = DiffusionPipeline.from_pretrained(
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-# Pattern-specific prompt engineering
 def enhance_prompt_for_pattern(prompt):
     """Add specific terms to ensure seamless, tileable patterns."""
     pattern_terms = [
@@ -36,9 +34,7 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024,
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     
-    # Enhance the prompt for pattern generation
     enhanced_prompt = enhance_prompt_for_pattern(prompt)
-    
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
         prompt=enhanced_prompt,
@@ -51,7 +47,6 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024,
     
     return image, seed
 
-# Example prompts specifically for pattern generation
 examples = [
     "geometric Art Deco shapes in gold and navy",
     "delicate floral motifs with small roses and leaves",
@@ -60,77 +55,185 @@ examples = [
     "modern minimalist lines and circles",
 ]
 
+# Enhanced CSS for better visual design and mobile responsiveness
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 520px;
+    max-width: 800px !important;
+    padding: 20px;
+}
+
+.main-title {
+    text-align: center;
+    color: #2d3748;
+    margin-bottom: 1rem;
+    font-family: 'Poppins', sans-serif;
+}
+
+.subtitle {
+    text-align: center;
+    color: #4a5568;
+    margin-bottom: 2rem;
+    font-family: 'Inter', sans-serif;
+    font-size: 0.95rem;
+    line-height: 1.5;
+}
+
+.pattern-input {
+    border: 2px solid #e2e8f0;
+    border-radius: 10px;
+    padding: 12px !important;
+    margin-bottom: 1rem !important;
+    font-size: 1rem;
+    transition: all 0.3s ease;
+}
+
+.pattern-input:focus {
+    border-color: #4299e1;
+    box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.1);
+}
+
+.generate-button {
+    background-color: #4299e1 !important;
+    color: white !important;
+    padding: 12px 24px !important;
+    border-radius: 8px !important;
+    font-weight: 600 !important;
+    transition: all 0.3s ease !important;
+}
+
+.generate-button:hover {
+    background-color: #3182ce !important;
+    transform: translateY(-1px);
+}
+
+.result-image {
+    border-radius: 12px;
+    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
+    margin-top: 1rem;
+}
+
+.advanced-settings {
+    margin-top: 1.5rem;
+    border: 1px solid #e2e8f0;
+    border-radius: 10px;
+    padding: 1rem;
+}
+
+/* Mobile Responsiveness */
+@media (max-width: 768px) {
+    #col-container {
+        padding: 12px;
+    }
+    
+    .main-title {
+        font-size: 1.5rem;
+    }
+    
+    .subtitle {
+        font-size: 0.9rem;
+    }
+    
+    .pattern-input {
+        font-size: 0.9rem;
+    }
+}
+
+/* Custom styling for examples section */
+.examples-section {
+    margin-top: 2rem;
+    padding: 1rem;
+    background: #f7fafc;
+    border-radius: 10px;
 }
 """
 
-with gr.Blocks(css=css) as demo:
+with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("""
-        # Deradh's AI Pattern Master
-        ### Create seamless, tileable patterns for high-quality textile designs
+        gr.Markdown(
+            """
+            # 🎨 Deradh's AI Pattern Master
+            """,
+            elem_classes=["main-title"]
+        )
         
-        This tool specializes in generating patterns that can be used for fabric printing and textile design.
-        Each pattern is optimized to be seamless and repeatable.
-        """)
+        gr.Markdown(
+            """
+            Create beautiful, seamless patterns for your textile designs using AI. 
+            Simply describe your desired pattern, and watch as AI brings your vision to life with 
+            professional-quality, repeatable patterns perfect for fabrics and materials.
+            """,
+            elem_classes=["subtitle"]
+        )
         
         with gr.Row():
             prompt = gr.Text(
                 label="Pattern Description",
                 show_label=False,
                 max_lines=1,
-                placeholder="Describe your desired pattern (e.g., 'geometric Art Deco shapes in gold and navy')",
+                placeholder="Describe your dream pattern (e.g., 'geometric Art Deco shapes in gold and navy')",
                 container=False,
+                elem_classes=["pattern-input"]
+            )
+            run_button = gr.Button(
+                "✨ Generate",
+                scale=0,
+                elem_classes=["generate-button"]
             )
-            run_button = gr.Button("Generate Pattern", scale=0)
         
-        result = gr.Image(label="Generated Pattern", show_label=True)
+        result = gr.Image(
+            label="Your Generated Pattern",
+            show_label=True,
+            elem_classes=["result-image"]
+        )
         
-        with gr.Accordion("Advanced Settings", open=False):
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
+        with gr.Accordion("🔧 Advanced Settings", open=False):
+            with gr.Group(elem_classes=["advanced-settings"]):
+                seed = gr.Slider(
+                    label="Pattern Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
                 )
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
+                randomize_seed = gr.Checkbox(
+                    label="Randomize Pattern",
+                    value=True
                 )
-            
-            with gr.Row():
+                
+                with gr.Row():
+                    width = gr.Slider(
+                        label="Width",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,
+                    )
+                    height = gr.Slider(
+                        label="Height",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,
+                    )
+                
                 num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
+                    label="Generation Quality (Steps)",
                     minimum=1,
                     maximum=50,
                     step=1,
                     value=4,
                 )
         
-        gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result, seed],
-            cache_examples="lazy"
-        )
+        with gr.Group(elem_classes=["examples-section"]):
+            gr.Markdown("### 💫 Try These Examples")
+            gr.Examples(
+                examples=examples,
+                fn=infer,
+                inputs=[prompt],
+                outputs=[result, seed],
+                cache_examples="lazy"
+            )
         
         gr.on(
             triggers=[run_button.click, prompt.submit],