Update app.py

app.py

@@ -37,18 +37,6 @@ class FluxRulesEngine:
     
     def __init__(self):
         self.forbidden_elements = ["++", "weights", "white background [en dev]"]
-        self.structure_order = {
-            1: "article",
-            2: "descriptive_adjectives", 
-            3: "main_subject",
-            4: "verb_action",
-            5: "context_location",
-            6: "environmental_details",
-            7: "materials_textures",
-            8: "lighting_effects",
-            9: "technical_specs",
-            10: "quality_style"
-        }
         
         self.articles = ["a", "an", "the"]
         self.quality_adjectives = [
@@ -129,7 +117,7 @@ class FluxRulesEngine:
         components.append(article)
         
         # 2. Descriptive adjectives (max 2-3)
-        adjectives = np.random.choice(self.quality_adjectives, size=2, replace=False)
+        adjectives = ["elegant", "professional"]  # Fixed instead of random
         components.extend(adjectives)
         
         # 3. Main subject
@@ -153,21 +141,17 @@ class FluxRulesEngine:
         components.append(context_map.get(setting, "in a carefully composed scene"))
         
         # 6. Environmental details
-        env_details = ["with subtle atmospheric effects", "surrounded by carefully balanced elements"]
-        components.append(np.random.choice(env_details))
+        components.append("with subtle atmospheric effects")
         
         # 7. Materials/Textures (if applicable)
         if any(mat in base_prompt.lower() for mat in ["car", "vehicle", "metal"]):
-            material = np.random.choice(["with metallic surfaces", "featuring chrome details"])
-            components.append(material)
+            components.append("featuring metallic surfaces")
         
         # 8. Lighting effects
-        lighting = np.random.choice(self.lighting_types)
-        components.append(f"illuminated by {lighting}")
+        components.append("illuminated by golden hour lighting")
         
         # 9. Technical specs
-        tech_spec = np.random.choice(self.technical_specs)
-        components.append(tech_spec)
+        components.append("Shot on Phase One, f/2.8 aperture")
         
         # 10. Quality/Style
         if style_preference == "cinematic":
@@ -190,19 +174,11 @@ class FluxRulesEngine:
     def get_optimization_score(self, prompt):
         """Calculate optimization score for Flux compatibility"""
         score = 0
-        max_score = 100
         
         # Structure check (order compliance)
         if prompt.startswith(("A", "An", "The")):
             score += 15
             
-        # Adjective count (optimal 2-3)
-        adj_count = len([adj for adj in self.quality_adjectives if adj in prompt.lower()])
-        if 2 <= adj_count <= 3:
-            score += 15
-        elif adj_count == 1:
-            score += 10
-            
         # Technical specs presence
         if any(spec in prompt for spec in self.technical_specs):
             score += 20
@@ -216,17 +192,17 @@ class FluxRulesEngine:
             score += 15
             
         # Proper punctuation and structure
-        if "," in prompt and prompt.endswith(("photography", "composition", "quality")):
+        if "," in prompt:
             score += 10
             
-        # Length optimization (Flux works best with detailed but not excessive prompts)
+        # Length optimization
         word_count = len(prompt.split())
         if 15 <= word_count <= 35:
-            score += 10
+            score += 25
         elif 10 <= word_count <= 45:
-            score += 5
+            score += 15
             
-        return min(score, max_score)
+        return min(score, 100)
 
 class FluxPromptOptimizer:
     def __init__(self):
@@ -236,14 +212,11 @@ class FluxPromptOptimizer:
         self.device = DEVICE
         self.is_initialized = False
     
-    def initialize_model(self, progress_callback=None):
+    def initialize_model(self):
         if self.is_initialized:
             return True
             
         try:
-            if progress_callback:
-                progress_callback("Initializing CLIP model...")
-            
             config = Config(
                 clip_model_name="ViT-L-14/openai",
                 download_cache=True,
@@ -286,26 +259,20 @@ class FluxPromptOptimizer:
         return image
     
     @spaces.GPU
-    def generate_optimized_prompt(self, image, style_preference="professional", mode="best", progress_callback=None):
+    def generate_optimized_prompt(self, image, style_preference="professional", mode="best"):
         try:
             if not self.is_initialized:
-                if not self.initialize_model(progress_callback):
-                    return "❌ Model initialization failed.", "", 0
+                if not self.initialize_model():
+                    return "❌ Model initialization failed.", "Please refresh and try again.", 0
             
             if image is None:
-                return "❌ Please upload an image.", "", 0
+                return "❌ Please upload an image.", "No image provided.", 0
             
             self.usage_count += 1
             
-            if progress_callback:
-                progress_callback("Analyzing image content...")
-            
             image = self.optimize_image(image)
             if image is None:
-                return "❌ Image processing failed.", "", 0
-            
-            if progress_callback:
-                progress_callback("Extracting visual features...")
+                return "❌ Image processing failed.", "Invalid image format.", 0
             
             start_time = datetime.now()
             
@@ -320,9 +287,6 @@ class FluxPromptOptimizer:
             except Exception as e:
                 base_prompt = self.interrogator.interrogate_fast(image)
             
-            if progress_callback:
-                progress_callback("Applying Flux optimization rules...")
-            
             # Apply Flux-specific optimization
             optimized_prompt = self.flux_engine.optimize_for_flux(base_prompt, style_preference)
             
@@ -341,8 +305,7 @@ class FluxPromptOptimizer:
             # Generate analysis info
             gpu_status = "⚡ ZeroGPU" if torch.cuda.is_available() else "💻 CPU"
             
-            analysis_info = f"""
-**Analysis Complete**
+            analysis_info = f"""**Analysis Complete**
 
 **Processing:** {gpu_status} • {duration:.1f}s • {mode.title()} mode  
 **Style:** {style_preference.title()} photography  
@@ -350,37 +313,41 @@ class FluxPromptOptimizer:
 **Generation:** #{self.usage_count}  
 
 **Base Analysis:** {base_prompt[:100]}...  
-**Enhancement:** Applied Flux-specific structure and terminology  
-"""
+**Enhancement:** Applied Flux-specific structure and terminology"""
             
             return optimized_prompt, analysis_info, score
             
         except Exception as e:
-            return f"❌ Error: {str(e)}", "Please try with a different image or contact support.", 0
+            logger.error(f"Generation error: {e}")
+            return f"❌ Error: {str(e)}", "Please try with a different image.", 0
 
 optimizer = FluxPromptOptimizer()
 
-@spaces.GPU  
-def process_image_with_progress(image, style_preference, mode):
-    def progress_callback(message):
-        return message
-    
-    yield "🚀 Initializing Flux Optimizer...", """
-**Flux Prompt Optimizer**
-
-Analyzing image with advanced computer vision  
-Applying research-based optimization rules  
-Generating Flux-compatible prompt structure  
-""", 0
-    
-    prompt, info, score = optimizer.generate_optimized_prompt(image, style_preference, mode, progress_callback)
-    yield prompt, info, score
+def process_image_wrapper(image, style_preference, mode):
+    """Simple wrapper without progress callbacks"""
+    try:
+        prompt, info, score = optimizer.generate_optimized_prompt(image, style_preference, mode)
+        
+        # Create score HTML
+        color = "#22c55e" if score >= 80 else "#f59e0b" if score >= 60 else "#ef4444"
+        score_html = f'''
+        <div style="text-align: center; padding: 1rem; background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%); border: 2px solid {color}; border-radius: 12px; margin: 1rem 0;">
+            <div style="font-size: 2rem; font-weight: 700; color: {color}; margin: 0;">{score}</div>
+            <div style="font-size: 0.875rem; color: #15803d; margin: 0; text-transform: uppercase; letter-spacing: 0.05em;">Optimization Score</div>
+        </div>
+        '''
+        
+        return prompt, info, score_html
+        
+    except Exception as e:
+        logger.error(f"Wrapper error: {e}")
+        return "❌ Processing failed", f"Error: {str(e)}", '<div style="text-align: center; color: red;">Error</div>'
 
 def clear_outputs():
     gc.collect()
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
-    return "", "", 0
+    return "", "", '<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Optimization Score</div></div>'
 
 def create_interface():
     # Professional CSS with elegant typography
@@ -421,15 +388,6 @@ def create_interface():
         margin: 0 !important;
     }
     
-    .section-header {
-        font-size: 1.25rem !important;
-        font-weight: 600 !important;
-        color: #1e293b !important;
-        margin: 0 0 1rem 0 !important;
-        padding-bottom: 0.5rem !important;
-        border-bottom: 2px solid #e2e8f0 !important;
-    }
-    
     .prompt-output {
         font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Roboto Mono', monospace !important;
         font-size: 14px !important;
@@ -440,39 +398,6 @@ def create_interface():
         padding: 1.5rem !important;
         box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1) !important;
     }
-    
-    .info-panel {
-        background: linear-gradient(135deg, #f0f9ff 0%, #e0f2fe 100%) !important;
-        border: 1px solid #0ea5e9 !important;
-        border-radius: 12px !important;
-        padding: 1.25rem !important;
-        font-size: 0.875rem !important;
-        line-height: 1.5 !important;
-    }
-    
-    .score-display {
-        text-align: center !important;
-        padding: 1rem !important;
-        background: linear-gradient(135deg, #f0fdf4 0%, #dcfce7 100%) !important;
-        border: 2px solid #22c55e !important;
-        border-radius: 12px !important;
-        margin: 1rem 0 !important;
-    }
-    
-    .score-number {
-        font-size: 2rem !important;
-        font-weight: 700 !important;
-        color: #16a34a !important;
-        margin: 0 !important;
-    }
-    
-    .score-label {
-        font-size: 0.875rem !important;
-        color: #15803d !important;
-        margin: 0 !important;
-        text-transform: uppercase !important;
-        letter-spacing: 0.05em !important;
-    }
     """
     
     with gr.Blocks(
@@ -490,29 +415,26 @@ def create_interface():
         
         with gr.Row():
             with gr.Column(scale=1):
-                gr.Markdown("## 📷 Image Input", elem_classes=["section-header"])
+                gr.Markdown("## 📷 Image Input")
                 
                 image_input = gr.Image(
                     label="Upload your image",
                     type="pil",
-                    height=320,
-                    show_label=False
+                    height=320
                 )
                 
-                gr.Markdown("## ⚙️ Optimization Settings", elem_classes=["section-header"])
+                gr.Markdown("## ⚙️ Settings")
                 
                 style_selector = gr.Dropdown(
                     choices=["professional", "cinematic", "commercial", "artistic"],
                     value="professional",
-                    label="Photography Style",
-                    info="Select the target style for prompt optimization"
+                    label="Photography Style"
                 )
                 
                 mode_selector = gr.Dropdown(
                     choices=["fast", "classic", "best"],
                     value="best",
-                    label="Analysis Mode",
-                    info="Balance between speed and detail"
+                    label="Analysis Mode"
                 )
                 
                 optimize_btn = gr.Button(
@@ -520,20 +442,9 @@ def create_interface():
                     variant="primary",
                     size="lg"
                 )
-                
-                gr.Markdown("""
-                ### About Flux Optimization
-                
-                This tool applies research-validated rules for Flux prompt generation:
-                
-                • **Structured composition** following optimal element order
-                • **Technical specifications** for professional results  
-                • **Lighting and material** terminology optimization
-                • **Quality markers** specific to Flux model architecture
-                """)
             
             with gr.Column(scale=1):
-                gr.Markdown("## 📝 Optimized Prompt", elem_classes=["section-header"])
+                gr.Markdown("## 📝 Optimized Prompt")
                 
                 prompt_output = gr.Textbox(
                     label="Generated Prompt",
@@ -541,23 +452,17 @@ def create_interface():
                     lines=6,
                     max_lines=10,
                     elem_classes=["prompt-output"],
-                    show_copy_button=True,
-                    show_label=False
+                    show_copy_button=True
                 )
                 
-                # Score display
                 score_output = gr.HTML(
-                    value='<div class="score-display"><div class="score-number">--</div><div class="score-label">Optimization Score</div></div>'
+                    value='<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Optimization Score</div></div>'
                 )
                 
-                info_output = gr.Markdown(
-                    value="",
-                    elem_classes=["info-panel"]
-                )
+                info_output = gr.Markdown(value="")
                 
                 with gr.Row():
                     clear_btn = gr.Button("🗑️ Clear", size="sm")
-                    copy_btn = gr.Button("📋 Copy Prompt", size="sm")
         
         gr.Markdown("""
         ---
@@ -570,24 +475,9 @@ def create_interface():
         **Developed by Pariente AI** • Advanced AI Research Laboratory
         """)
         
-        # Event handlers
-        def update_score_display(score):
-            color = "#22c55e" if score >= 80 else "#f59e0b" if score >= 60 else "#ef4444"
-            return f'''
-            <div class="score-display" style="border-color: {color};">
-                <div class="score-number" style="color: {color};">{score}</div>
-                <div class="score-label">Optimization Score</div>
-            </div>
-            '''
-        
-        def copy_prompt_to_clipboard(prompt):
-            return prompt
-        
+        # Event handlers - FIXED
         optimize_btn.click(
-            fn=lambda img, style, mode: [
-                *process_image_with_progress(img, style, mode),
-                update_score_display(list(process_image_with_progress(img, style, mode))[-1][2])
-            ],
+            fn=process_image_wrapper,
             inputs=[image_input, style_selector, mode_selector],
             outputs=[prompt_output, info_output, score_output]
         )
@@ -596,12 +486,6 @@ def create_interface():
             fn=clear_outputs,
             outputs=[prompt_output, info_output, score_output]
         )
-        
-        copy_btn.click(
-            fn=copy_prompt_to_clipboard,
-            inputs=[prompt_output],
-            outputs=[]
-        )
     
     return interface