Simplify to gr.Interface for HF Spaces compatibility

app.py

@@ -1,7 +1,5 @@
 """
-Architecture AI Enhancer - Gradio App for Hugging Face Spaces
-
-This file creates a Gradio interface for the enhancement pipeline.
+Architecture AI Enhancer - Simple Gradio Interface for HF Spaces
 """
 import gradio as gr
 import torch
@@ -9,7 +7,6 @@ from PIL import Image
 import logging
 from pathlib import Path
 import sys
-import os
 
 # Add backend to path
 sys.path.insert(0, str(Path(__file__).parent))
@@ -17,206 +14,90 @@ sys.path.insert(0, str(Path(__file__).parent))
 from backend.services.diffusion_pipeline import get_pipeline_manager, enhance_image as enhance_with_diffusion
 from backend.services.upscaler import upscale_image
 from backend.services.postprocess import postprocess_image
-from backend.config import settings
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Initialize pipeline manager (lazy loading)
+# Initialize pipeline manager
 pipeline_manager = None
 
 def initialize_models():
-    """Initialize all models on first use"""
+    """Initialize models on first use"""
     global pipeline_manager
-    
     if pipeline_manager is None:
         logger.info("Loading Stable Diffusion pipeline...")
         pipeline_manager = get_pipeline_manager()
 
-def enhance_image_gradio(
+def enhance_image_simple(
     input_image: Image.Image,
-    strength: float = 0.3,
-    guidance_scale: float = 5.5,
-    custom_prompt: str = "",
-    use_upscaler: bool = True,
-    use_postprocess: bool = True
+    strength: float,
+    guidance_scale: float,
+    use_upscaler: bool,
+    use_postprocess: bool
 ) -> Image.Image:
-    """
-    Enhance architectural image using AI pipeline
-    
-    Args:
-        input_image: Input PIL Image
-        strength: Denoising strength (0.1-0.8)
-        guidance_scale: CFG scale (1.0-15.0)
-        custom_prompt: Optional custom prompt
-        use_upscaler: Apply upscaling
-        use_postprocess: Apply post-processing
-        
-    Returns:
-        Enhanced PIL Image
-    """
+    """Enhance architectural image"""
     try:
+        logger.info("Starting enhancement process...")
+        
         # Initialize models
-        logger.info("Loading models...")
         initialize_models()
         
         # Step 1: AI Enhancement
-        logger.info("Preprocessing image...")
-        
-        prompt = custom_prompt if custom_prompt else settings.DEFAULT_PROMPT
-        
-        logger.info("Enhancing with AI (this may take a few minutes)...")
-        
-        enhanced = pipeline_manager.run_inference(
-            image=input_image,
-            prompt=prompt,
-            negative_prompt=settings.NEGATIVE_PROMPT,
+        logger.info("Applying AI enhancement...")
+        enhanced = enhance_with_diffusion(
+            pipeline_manager,
+            input_image,
+            prompt="professional architectural photography, highly detailed, 8k, photorealistic",
             strength=strength,
             guidance_scale=guidance_scale,
             num_inference_steps=30
         )
         
-        logger.info("AI enhancement complete!")
-        
-        # Step 2: Optional Upscaling
+        # Step 2: Upscaling
         if use_upscaler:
             logger.info("Upscaling image...")
             enhanced = upscale_image(enhanced, scale=2)
-            logger.info("Upscaling complete!")
         
-        # Step 3: Optional Post-processing
+        # Step 3: Post-processing
         if use_postprocess:
-            logger.info("Applying final touches...")
+            logger.info("Post-processing...")
             enhanced = postprocess_image(
                 enhanced,
                 clahe=True,
                 sharpen=1.0,
                 color_enhance=1.1
             )
-            logger.info("Post-processing complete!")
         
-        logger.info("Enhancement process complete!")
+        logger.info("Enhancement complete!")
         return enhanced
         
     except Exception as e:
         logger.error(f"Enhancement failed: {e}", exc_info=True)
-        raise gr.Error(f"Enhancement failed: {str(e)}")
+        raise
 
-# Create Gradio interface
-with gr.Blocks(title="Architecture AI Enhancer", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 🏗️ Architecture AI Enhancer
-    
-    Transform your architectural renders with AI-powered enhancement using Stable Diffusion 1.5.
-    
-    **Upload an image** and adjust the settings below to enhance your architectural visualization.
-    """)
-    
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(
-                label="📤 Input Image",
-                type="pil",
-                height=400
-            )
-            
-            with gr.Accordion("⚙️ Advanced Settings", open=False):
-                strength = gr.Slider(
-                    minimum=0.1,
-                    maximum=0.8,
-                    value=0.3,
-                    step=0.05,
-                    label="Denoising Strength",
-                    info="Lower = more faithful to input, Higher = more creative"
-                )
-                
-                guidance_scale = gr.Slider(
-                    minimum=1.0,
-                    maximum=15.0,
-                    value=5.5,
-                    step=0.5,
-                    label="Guidance Scale",
-                    info="How closely to follow the prompt"
-                )
-                
-                custom_prompt = gr.Textbox(
-                    label="Custom Prompt (optional)",
-                    placeholder="professional architectural photography, detailed, high quality...",
-                    lines=3
-                )
-                
-                use_upscaler = gr.Checkbox(
-                    label="Enable Upscaling (2x)",
-                    value=True
-                )
-                
-                use_postprocess = gr.Checkbox(
-                    label="Enable Post-Processing",
-                    value=True,
-                    info="Adds photographic enhancements"
-                )
-            
-            enhance_btn = gr.Button("✨ Enhance Image", variant="primary", size="lg")
-            
-        with gr.Column():
-            output_image = gr.Image(
-                label="✅ Enhanced Result",
-                type="pil",
-                height=400
-            )
-            
-            gr.Markdown("""
-            ### 📝 Tips for Best Results:
-            - Use high-quality architectural renders as input
-            - Start with default settings and adjust if needed
-            - Lower strength for subtle enhancements
-            - Higher strength for more dramatic changes
-            - Processing takes 2-5 minutes on CPU, ~30 seconds on GPU
-            """)
-    
-    gr.Markdown("""
-    ---
-    ### 🔧 Technical Details:
-    - **Model**: Stable Diffusion 1.5
-    - **Upscaler**: ESRGAN (optional)
-    - **Processing**: CPU/GPU automatic detection
-    - **Version**: 1.0.0
-    
-    ### 📚 Resources:
-    - [GitHub Repository](#)
-    - [Documentation](#)
-    - [Report Issues](#)
-    """)
-    
-    # Connect the enhance button
-    enhance_btn.click(
-        fn=enhance_image_gradio,
-        inputs=[
-            input_image,
-            strength,
-            guidance_scale,
-            custom_prompt,
-            use_upscaler,
-            use_postprocess
-        ],
-        outputs=output_image
-    )
+# Create simple interface
+iface = gr.Interface(
+    fn=enhance_image_simple,
+    inputs=[
+        gr.Image(label="Input Image", type="pil"),
+        gr.Slider(0.1, 0.8, value=0.3, step=0.05, label="Strength"),
+        gr.Slider(1.0, 15.0, value=5.5, step=0.5, label="Guidance Scale"),
+        gr.Checkbox(label="Enable Upscaling", value=True),
+        gr.Checkbox(label="Enable Post-Processing", value=True)
+    ],
+    outputs=gr.Image(label="Enhanced Image", type="pil"),
+    title="🏛️ Architecture AI Enhancer",
+    description="Transform architectural renders with AI-powered enhancement using Stable Diffusion 1.5",
+    article="""
+    ### Tips for Best Results:
+    - Use high-quality architectural renders
+    - Lower strength = more faithful to input
+    - Higher strength = more creative output
+    - Processing takes 2-5 minutes on CPU
+    """,
+    examples=None
+)
 
-# Launch configuration
 if __name__ == "__main__":
-    demo.queue(max_size=10)  # Enable queue for multiple users
-    
-    # Detect if running on HF Spaces
-    is_hf_space = os.getenv("SPACE_ID") is not None
-    
-    if is_hf_space:
-        # HF Spaces configuration
-        demo.launch(
-            server_name="0.0.0.0",
-            server_port=7860,
-            show_api=False
-        )
-    else:
-        # Local development
-        demo.launch(share=True)
+    iface.queue().launch()

app_blocks.py.bak

@@ -0,0 +1,222 @@
+"""
+Architecture AI Enhancer - Gradio App for Hugging Face Spaces
+
+This file creates a Gradio interface for the enhancement pipeline.
+"""
+import gradio as gr
+import torch
+from PIL import Image
+import logging
+from pathlib import Path
+import sys
+import os
+
+# Add backend to path
+sys.path.insert(0, str(Path(__file__).parent))
+
+from backend.services.diffusion_pipeline import get_pipeline_manager, enhance_image as enhance_with_diffusion
+from backend.services.upscaler import upscale_image
+from backend.services.postprocess import postprocess_image
+from backend.config import settings
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Initialize pipeline manager (lazy loading)
+pipeline_manager = None
+
+def initialize_models():
+    """Initialize all models on first use"""
+    global pipeline_manager
+    
+    if pipeline_manager is None:
+        logger.info("Loading Stable Diffusion pipeline...")
+        pipeline_manager = get_pipeline_manager()
+
+def enhance_image_gradio(
+    input_image: Image.Image,
+    strength: float = 0.3,
+    guidance_scale: float = 5.5,
+    custom_prompt: str = "",
+    use_upscaler: bool = True,
+    use_postprocess: bool = True
+) -> Image.Image:
+    """
+    Enhance architectural image using AI pipeline
+    
+    Args:
+        input_image: Input PIL Image
+        strength: Denoising strength (0.1-0.8)
+        guidance_scale: CFG scale (1.0-15.0)
+        custom_prompt: Optional custom prompt
+        use_upscaler: Apply upscaling
+        use_postprocess: Apply post-processing
+        
+    Returns:
+        Enhanced PIL Image
+    """
+    try:
+        # Initialize models
+        logger.info("Loading models...")
+        initialize_models()
+        
+        # Step 1: AI Enhancement
+        logger.info("Preprocessing image...")
+        
+        prompt = custom_prompt if custom_prompt else settings.DEFAULT_PROMPT
+        
+        logger.info("Enhancing with AI (this may take a few minutes)...")
+        
+        enhanced = pipeline_manager.run_inference(
+            image=input_image,
+            prompt=prompt,
+            negative_prompt=settings.NEGATIVE_PROMPT,
+            strength=strength,
+            guidance_scale=guidance_scale,
+            num_inference_steps=30
+        )
+        
+        logger.info("AI enhancement complete!")
+        
+        # Step 2: Optional Upscaling
+        if use_upscaler:
+            logger.info("Upscaling image...")
+            enhanced = upscale_image(enhanced, scale=2)
+            logger.info("Upscaling complete!")
+        
+        # Step 3: Optional Post-processing
+        if use_postprocess:
+            logger.info("Applying final touches...")
+            enhanced = postprocess_image(
+                enhanced,
+                clahe=True,
+                sharpen=1.0,
+                color_enhance=1.1
+            )
+            logger.info("Post-processing complete!")
+        
+        logger.info("Enhancement process complete!")
+        return enhanced
+        
+    except Exception as e:
+        logger.error(f"Enhancement failed: {e}", exc_info=True)
+        raise gr.Error(f"Enhancement failed: {str(e)}")
+
+# Create Gradio interface
+with gr.Blocks(title="Architecture AI Enhancer", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🏗️ Architecture AI Enhancer
+    
+    Transform your architectural renders with AI-powered enhancement using Stable Diffusion 1.5.
+    
+    **Upload an image** and adjust the settings below to enhance your architectural visualization.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                label="📤 Input Image",
+                type="pil",
+                height=400
+            )
+            
+            with gr.Accordion("⚙️ Advanced Settings", open=False):
+                strength = gr.Slider(
+                    minimum=0.1,
+                    maximum=0.8,
+                    value=0.3,
+                    step=0.05,
+                    label="Denoising Strength",
+                    info="Lower = more faithful to input, Higher = more creative"
+                )
+                
+                guidance_scale = gr.Slider(
+                    minimum=1.0,
+                    maximum=15.0,
+                    value=5.5,
+                    step=0.5,
+                    label="Guidance Scale",
+                    info="How closely to follow the prompt"
+                )
+                
+                custom_prompt = gr.Textbox(
+                    label="Custom Prompt (optional)",
+                    placeholder="professional architectural photography, detailed, high quality...",
+                    lines=3
+                )
+                
+                use_upscaler = gr.Checkbox(
+                    label="Enable Upscaling (2x)",
+                    value=True
+                )
+                
+                use_postprocess = gr.Checkbox(
+                    label="Enable Post-Processing",
+                    value=True,
+                    info="Adds photographic enhancements"
+                )
+            
+            enhance_btn = gr.Button("✨ Enhance Image", variant="primary", size="lg")
+            
+        with gr.Column():
+            output_image = gr.Image(
+                label="✅ Enhanced Result",
+                type="pil",
+                height=400
+            )
+            
+            gr.Markdown("""
+            ### 📝 Tips for Best Results:
+            - Use high-quality architectural renders as input
+            - Start with default settings and adjust if needed
+            - Lower strength for subtle enhancements
+            - Higher strength for more dramatic changes
+            - Processing takes 2-5 minutes on CPU, ~30 seconds on GPU
+            """)
+    
+    gr.Markdown("""
+    ---
+    ### 🔧 Technical Details:
+    - **Model**: Stable Diffusion 1.5
+    - **Upscaler**: ESRGAN (optional)
+    - **Processing**: CPU/GPU automatic detection
+    - **Version**: 1.0.0
+    
+    ### 📚 Resources:
+    - [GitHub Repository](#)
+    - [Documentation](#)
+    - [Report Issues](#)
+    """)
+    
+    # Connect the enhance button
+    enhance_btn.click(
+        fn=enhance_image_gradio,
+        inputs=[
+            input_image,
+            strength,
+            guidance_scale,
+            custom_prompt,
+            use_upscaler,
+            use_postprocess
+        ],
+        outputs=output_image
+    )
+
+# Launch configuration
+if __name__ == "__main__":
+    demo.queue(max_size=10)  # Enable queue for multiple users
+    
+    # Detect if running on HF Spaces
+    is_hf_space = os.getenv("SPACE_ID") is not None
+    
+    if is_hf_space:
+        # HF Spaces configuration
+        demo.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            show_api=False
+        )
+    else:
+        # Local development
+        demo.launch(share=True)