Update app.py

app.py

@@ -46,7 +46,6 @@ class UltraSupremeAnalyzer:
             "senior": ["senior", "older", "elderly", "aged", "vintage", "seasoned"],
             "elderly": ["elderly", "old", "ancient", "weathered", "aged", "gray", "grey", "white hair", "silver", "wrinkled", "lined", "creased", "time-worn", "distinguished by age"]
         }
-        
         self.ultra_facial_analysis = {
             "eye_features": {
                 "shape": ["round eyes", "almond eyes", "narrow eyes", "wide eyes", "deep-set eyes", "prominent eyes"],
@@ -91,7 +90,6 @@ class UltraSupremeAnalyzer:
             "headwear": ["hat", "cap", "beret", "headband", "turban", "hood", "helmet", "crown", "headpiece"],
             "eyewear": ["glasses", "spectacles", "sunglasses", "reading glasses", "wire-frame glasses", "thick-rimmed glasses", "designer glasses", "vintage glasses"],
             "clothing_types": ["suit", "jacket", "shirt", "dress", "robe", "uniform", "casual wear", "formal wear", "business attire"],
-            "clothing_colors": ["black", "white", "gray", "blue", "red", "green", "brown", "navy", "dark", "light"],
             "clothing_styles": ["formal", "casual", "business", "traditional", "modern", "vintage", "classic", "contemporary"],
             "accessories": ["jewelry", "watch", "necklace", "ring", "bracelet", "earrings", "pin", "brooch"]
         }
@@ -125,7 +123,6 @@ class UltraSupremeAnalyzer:
             "eye_contact": ["looking at camera", "looking away", "direct gaze", "averted gaze", "looking down", "looking up"],
             "overall_demeanor": ["confident", "reserved", "approachable", "authoritative", "gentle", "stern", "relaxed", "tense"]
         }
-        
         self.composition_photography_ultra = {
             "shot_types": ["close-up", "medium shot", "wide shot", "extreme close-up", "portrait shot", "headshot", "bust shot", "full body"],
             "angles": ["eye level", "high angle", "low angle", "bird's eye", "worm's eye", "Dutch angle"],
@@ -245,9 +242,13 @@ class UltraSupremeAnalyzer:
             if isinstance(items, list):
                 for item in items:
                     if item in combined_analysis["combined"]:
-                        ultra_result["clothing_accessories"][category].append(item)
-        
-        # ENVIRONMENTAL ULTRA ANALYSIS
+                        if category == "clothing_types":
+                            ultra_result["clothing_accessories"]["clothing"].append(item)
+                        elif category == "clothing_styles":
+                            ultra_result["clothing_accessories"]["clothing"].append(item)
+                        elif category in ["headwear", "eyewear", "accessories"]:
+                            ultra_result["clothing_accessories"][category].append(item)
+                            # ENVIRONMENTAL ULTRA ANALYSIS
         setting_scores = {}
         for main_setting, sub_settings in self.environmental_ultra_analysis.items():
             if isinstance(sub_settings, dict):
@@ -269,7 +270,8 @@ class UltraSupremeAnalyzer:
         for pose_category, indicators in self.pose_body_language_ultra.items():
             for indicator in indicators:
                 if indicator in combined_analysis["combined"]:
-                    ultra_result["pose_composition"][pose_category].append(indicator)
+                    if pose_category in ultra_result["pose_composition"]:
+                        ultra_result["pose_composition"][pose_category].append(indicator)
         
         # TECHNICAL PHOTOGRAPHY ANALYSIS
         for shot_type in self.composition_photography_ultra["shot_types"]:
@@ -285,165 +287,163 @@ class UltraSupremeAnalyzer:
         
         return ultra_result
     
-        def build_ultra_supreme_prompt(self, ultra_analysis, clip_results):
-            """BUILD ULTRA SUPREME FLUX PROMPT - ABSOLUTE MAXIMUM QUALITY"""
-            
-            components = []
-            
-            # 1. ULTRA INTELLIGENT ARTICLE SELECTION
-            subject_desc = []
+    def build_ultra_supreme_prompt(self, ultra_analysis, clip_results):
+        """BUILD ULTRA SUPREME FLUX PROMPT - ABSOLUTE MAXIMUM QUALITY"""
+        
+        components = []
+        
+        # 1. ULTRA INTELLIGENT ARTICLE SELECTION
+        subject_desc = []
+        if ultra_analysis["demographic"]["cultural_religious"]:
+            subject_desc.extend(ultra_analysis["demographic"]["cultural_religious"][:1])
+        if ultra_analysis["demographic"]["age_category"] and ultra_analysis["demographic"]["age_category"] != "middle_aged":
+            subject_desc.append(ultra_analysis["demographic"]["age_category"].replace("_", " "))
+        if ultra_analysis["demographic"]["gender"]:
+            subject_desc.append(ultra_analysis["demographic"]["gender"])
+        
+        if subject_desc:
+            full_subject = " ".join(subject_desc)
+            article = "An" if full_subject[0].lower() in 'aeiou' else "A"
+        else:
+            article = "A"
+        components.append(article)
+        
+        # 2. ULTRA CONTEXTUAL ADJECTIVES (max 2-3 per Flux rules)
+        adjectives = []
+        
+        # Age-based adjectives
+        age_cat = ultra_analysis["demographic"]["age_category"]
+        if age_cat and age_cat in self.quality_descriptors_ultra["based_on_age"]:
+            adjectives.extend(self.quality_descriptors_ultra["based_on_age"][age_cat][:2])
+        
+        # Emotion-based adjectives
+        emotion = ultra_analysis["emotional_state"]["primary_emotion"]
+        if emotion and emotion in self.quality_descriptors_ultra["based_on_emotion"]:
+            adjectives.extend(self.quality_descriptors_ultra["based_on_emotion"][emotion][:1])
+        
+        # Default if none found
+        if not adjectives:
+            adjectives = ["distinguished", "professional"]
+        
+        components.extend(adjectives[:2])  # Flux rule: max 2-3 adjectives
+        # 3. ULTRA ENHANCED SUBJECT
+        if subject_desc:
+            components.append(" ".join(subject_desc))
+        else:
+            components.append("person")
+        
+        # 4. ULTRA DETAILED FACIAL FEATURES
+        facial_details = []
+        
+        # Eyes
+        if ultra_analysis["facial_ultra"]["eyes"]:
+            eye_desc = ultra_analysis["facial_ultra"]["eyes"][0]
+            facial_details.append(f"with {eye_desc}")
+        
+        # Facial hair with ultra detail
+        if ultra_analysis["facial_ultra"]["facial_hair"]:
+            beard_details = ultra_analysis["facial_ultra"]["facial_hair"]
+            if any("silver" in detail or "gray" in detail or "grey" in detail for detail in beard_details):
+                facial_details.append("with a distinguished silver beard")
+            elif any("beard" in detail for detail in beard_details):
+                facial_details.append("with a full well-groomed beard")
+        
+        if facial_details:
+            components.extend(facial_details)
+        
+        # 5. CLOTHING AND ACCESSORIES ULTRA
+        clothing_details = []
+        
+        # Eyewear
+        if ultra_analysis["clothing_accessories"]["eyewear"]:
+            eyewear = ultra_analysis["clothing_accessories"]["eyewear"][0]
+            clothing_details.append(f"wearing {eyewear}")
+        
+        # Headwear
+        if ultra_analysis["clothing_accessories"]["headwear"]:
+            headwear = ultra_analysis["clothing_accessories"]["headwear"][0]
             if ultra_analysis["demographic"]["cultural_religious"]:
-                subject_desc.extend(ultra_analysis["demographic"]["cultural_religious"][:1])
-            if ultra_analysis["demographic"]["age_category"] and ultra_analysis["demographic"]["age_category"] != "middle_aged":
-                subject_desc.append(ultra_analysis["demographic"]["age_category"].replace("_", " "))
-            if ultra_analysis["demographic"]["gender"]:
-                subject_desc.append(ultra_analysis["demographic"]["gender"])
-            
-            if subject_desc:
-                full_subject = " ".join(subject_desc)
-                article = "An" if full_subject[0].lower() in 'aeiou' else "A"
-            else:
-                article = "A"
-            components.append(article)
-            
-            # 2. ULTRA CONTEXTUAL ADJECTIVES (max 2-3 per Flux rules)
-            adjectives = []
-            
-            # Age-based adjectives
-            age_cat = ultra_analysis["demographic"]["age_category"]
-            if age_cat and age_cat in self.quality_descriptors_ultra["based_on_age"]:
-                adjectives.extend(self.quality_descriptors_ultra["based_on_age"][age_cat][:2])
-            
-            # Emotion-based adjectives
-            emotion = ultra_analysis["emotional_state"]["primary_emotion"]
-            if emotion and emotion in self.quality_descriptors_ultra["based_on_emotion"]:
-                adjectives.extend(self.quality_descriptors_ultra["based_on_emotion"][emotion][:1])
-            
-            # Default if none found
-            if not adjectives:
-                adjectives = ["distinguished", "professional"]
-            
-            components.extend(adjectives[:2])  # Flux rule: max 2-3 adjectives
-            
-            # 3. ULTRA ENHANCED SUBJECT
-            if subject_desc:
-                components.append(" ".join(subject_desc))
-            else:
-                components.append("person")
-            
-            # 4. ULTRA DETAILED FACIAL FEATURES
-            facial_details = []
-            
-            # Eyes
-            if ultra_analysis["facial_ultra"]["eyes"]:
-                eye_desc = ultra_analysis["facial_ultra"]["eyes"][0]
-                facial_details.append(f"with {eye_desc}")
-            
-            # Facial hair with ultra detail
-            if ultra_analysis["facial_ultra"]["facial_hair"]:
-                beard_details = ultra_analysis["facial_ultra"]["facial_hair"]
-                if any("silver" in detail or "gray" in detail or "grey" in detail for detail in beard_details):
-                    facial_details.append("with a distinguished silver beard")
-                elif any("beard" in detail for detail in beard_details):
-                    facial_details.append("with a full well-groomed beard")
-            
-            if facial_details:
-                components.extend(facial_details)
-            
-            # 5. CLOTHING AND ACCESSORIES ULTRA
-            clothing_details = []
-            
-            # Eyewear
-            if ultra_analysis["clothing_accessories"]["eyewear"]:
-                eyewear = ultra_analysis["clothing_accessories"]["eyewear"][0]
-                clothing_details.append(f"wearing {eyewear}")
-            
-            # Headwear
-            if ultra_analysis["clothing_accessories"]["headwear"]:
-                headwear = ultra_analysis["clothing_accessories"]["headwear"][0]
-                if ultra_analysis["demographic"]["cultural_religious"]:
-                    clothing_details.append("wearing a traditional black hat")
-                else:
-                    clothing_details.append(f"wearing a {headwear}")
-            
-            if clothing_details:
-                components.extend(clothing_details)
-            
-            # 6. ULTRA POSE AND BODY LANGUAGE
-            pose_description = "positioned with natural dignity"
-            
-            if ultra_analysis["pose_composition"]["posture"]:
-                posture = ultra_analysis["pose_composition"]["posture"][0]
-                pose_description = f"maintaining {posture}"
-            elif ultra_analysis["technical_analysis"]["shot_type"] == "portrait":
-                pose_description = "captured in contemplative portrait pose"
-            
-            components.append(pose_description)
-            
-            # 7. ULTRA ENVIRONMENTAL CONTEXT
-            environment_desc = "in a thoughtfully composed environment"
-            
-            if ultra_analysis["environmental"]["setting_type"]:
-                setting_map = {
-                    "residential": "in an intimate home setting",
-                    "office": "in a professional office environment", 
-                    "religious": "in a sacred traditional space",
-                    "formal": "in a distinguished formal setting"
-                }
-                environment_desc = setting_map.get(ultra_analysis["environmental"]["setting_type"], "in a carefully arranged professional setting")
-            
-            components.append(environment_desc)
-            
-            # 8. ULTRA SOPHISTICATED LIGHTING
-            lighting_desc = "illuminated by sophisticated portrait lighting that emphasizes character and facial texture"
-            
-            if ultra_analysis["environmental"]["lighting_analysis"]:
-                primary_light = ultra_analysis["environmental"]["lighting_analysis"][0]
-                if "dramatic" in primary_light:
-                    lighting_desc = "bathed in dramatic chiaroscuro lighting that creates compelling depth and shadow play"
-                elif "natural" in primary_light or "window" in primary_light:
-                    lighting_desc = "graced by gentle natural lighting that brings out intricate facial details and warmth"
-                elif "soft" in primary_light:
-                    lighting_desc = "softly illuminated to reveal nuanced expressions and character"
-            
-            components.append(lighting_desc)
-            
-            # 9. ULTRA TECHNICAL SPECIFICATIONS
-            if ultra_analysis["technical_analysis"]["shot_type"] in ["portrait", "headshot", "close-up"]:
-                camera_setup = "Shot on Phase One XF IQ4, 85mm f/1.4 lens, f/2.8 aperture"
-            elif ultra_analysis["demographic"]["cultural_religious"]:
-                camera_setup = "Shot on Hasselblad X2D, 90mm lens, f/2.8 aperture" 
+                clothing_details.append("wearing a traditional black hat")
             else:
-                camera_setup = "Shot on Phase One XF, 80mm lens, f/4 aperture"
-            
-            components.append(camera_setup)
-            
-            # 10. ULTRA QUALITY DESIGNATION
-            quality_designation = "professional portrait photography"
-            
-            if ultra_analysis["demographic"]["cultural_religious"]:
-                quality_designation = "fine art documentary photography"
-            elif ultra_analysis["emotional_state"]["primary_emotion"]:
-                quality_designation = "expressive portrait photography"
-            
-            components.append(quality_designation)
-            
-            # ULTRA FINAL ASSEMBLY
-            prompt = ", ".join(components)
-            
-            # Ultra cleaning and optimization
-            prompt = re.sub(r'\s+', ' ', prompt)
-            prompt = re.sub(r',\s*,+', ',', prompt)
-            prompt = re.sub(r'\s*,\s*', ', ', prompt)
-            prompt = prompt.replace(" ,", ",")
-            
-            if prompt:
-                prompt = prompt[0].upper() + prompt[1:]
-            
-            return prompt
+                clothing_details.append(f"wearing a {headwear}")
+        
+        if clothing_details:
+            components.extend(clothing_details)
+        
+        # 6. ULTRA POSE AND BODY LANGUAGE
+        pose_description = "positioned with natural dignity"
+        
+        if ultra_analysis["pose_composition"]["posture"]:
+            posture = ultra_analysis["pose_composition"]["posture"][0]
+            pose_description = f"maintaining {posture}"
+        elif ultra_analysis["technical_analysis"]["shot_type"] == "portrait":
+            pose_description = "captured in contemplative portrait pose"
+        
+        components.append(pose_description)
+        
+        # 7. ULTRA ENVIRONMENTAL CONTEXT
+        environment_desc = "in a thoughtfully composed environment"
+        
+        if ultra_analysis["environmental"]["setting_type"]:
+            setting_map = {
+                "residential": "in an intimate home setting",
+                "office": "in a professional office environment", 
+                "religious": "in a sacred traditional space",
+                "formal": "in a distinguished formal setting"
+            }
+            environment_desc = setting_map.get(ultra_analysis["environmental"]["setting_type"], "in a carefully arranged professional setting")
+        
+        components.append(environment_desc)
+        
+        # 8. ULTRA SOPHISTICATED LIGHTING
+        lighting_desc = "illuminated by sophisticated portrait lighting that emphasizes character and facial texture"
+        
+        if ultra_analysis["environmental"]["lighting_analysis"]:
+            primary_light = ultra_analysis["environmental"]["lighting_analysis"][0]
+            if "dramatic" in primary_light:
+                lighting_desc = "bathed in dramatic chiaroscuro lighting that creates compelling depth and shadow play"
+            elif "natural" in primary_light or "window" in primary_light:
+                lighting_desc = "graced by gentle natural lighting that brings out intricate facial details and warmth"
+            elif "soft" in primary_light:
+                lighting_desc = "softly illuminated to reveal nuanced expressions and character"
+        
+        components.append(lighting_desc)
+        # 9. ULTRA TECHNICAL SPECIFICATIONS
+        if ultra_analysis["technical_analysis"]["shot_type"] in ["portrait", "headshot", "close-up"]:
+            camera_setup = "Shot on Phase One XF IQ4, 85mm f/1.4 lens, f/2.8 aperture"
+        elif ultra_analysis["demographic"]["cultural_religious"]:
+            camera_setup = "Shot on Hasselblad X2D, 90mm lens, f/2.8 aperture" 
+        else:
+            camera_setup = "Shot on Phase One XF, 80mm lens, f/4 aperture"
+        
+        components.append(camera_setup)
+        
+        # 10. ULTRA QUALITY DESIGNATION
+        quality_designation = "professional portrait photography"
+        
+        if ultra_analysis["demographic"]["cultural_religious"]:
+            quality_designation = "fine art documentary photography"
+        elif ultra_analysis["emotional_state"]["primary_emotion"]:
+            quality_designation = "expressive portrait photography"
+        
+        components.append(quality_designation)
+        
+        # ULTRA FINAL ASSEMBLY
+        prompt = ", ".join(components)
+        
+        # Ultra cleaning and optimization
+        prompt = re.sub(r'\s+', ' ', prompt)
+        prompt = re.sub(r',\s*,+', ',', prompt)
+        prompt = re.sub(r'\s*,\s*', ', ', prompt)
+        prompt = prompt.replace(" ,", ",")
+        
+        if prompt:
+            prompt = prompt[0].upper() + prompt[1:]
+        
+        return prompt
     
-        def calculate_ultra_supreme_score(self, prompt, ultra_analysis):
-            """ULTRA SUPREME INTELLIGENCE SCORING"""
+    def calculate_ultra_supreme_score(self, prompt, ultra_analysis):
+        """ULTRA SUPREME INTELLIGENCE SCORING"""
         
         score = 0
         breakdown = {}
@@ -497,9 +497,7 @@ class UltraSupremeAnalyzer:
         breakdown["environmental"] = env_score
         
         return min(score, 100), breakdown
-
-
-class UltraSupremeOptimizer:
+        class UltraSupremeOptimizer:
     def __init__(self):
         self.interrogator = None
         self.analyzer = UltraSupremeAnalyzer()
@@ -596,8 +594,7 @@ class UltraSupremeOptimizer:
                 gc.collect()
             else:
                 torch.cuda.empty_cache()
-            
-            # ULTRA COMPREHENSIVE ANALYSIS REPORT
+                # ULTRA COMPREHENSIVE ANALYSIS REPORT
             gpu_status = "⚡ ZeroGPU" if torch.cuda.is_available() else "💻 CPU"
             
             # Format detected elements
@@ -631,6 +628,7 @@ class UltraSupremeOptimizer:
 
 # Initialize the optimizer
 optimizer = UltraSupremeOptimizer()
+
 def process_ultra_supreme_analysis(image):
     """Ultra supreme analysis wrapper"""
     try:
@@ -675,8 +673,7 @@ def clear_outputs():
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
     return "", "", '<div style="text-align: center; padding: 1rem;"><div style="font-size: 2rem; color: #ccc;">--</div><div style="font-size: 0.875rem; color: #999;">Ultra Supreme Score</div></div>'
-
-def create_interface():
+    def create_interface():
     css = """
     @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700;800;900&display=swap');
     
@@ -730,6 +727,7 @@ def create_interface():
         margin: 0 !important;
         position: relative;
         z-index: 2;
+        color: #ffffff !important;
     }
     
     .prompt-output {
@@ -748,6 +746,21 @@ def create_interface():
         box-shadow: 0 25px 60px -5px rgba(0, 0, 0, 0.15) !important;
         transform: translateY(-2px) !important;
     }
+    
+    /* Fix para texto en el footer que aparece blanco sobre blanco */
+    footer, .footer, [class*="footer"] {
+        color: #374151 !important;
+    }
+    
+    /* Asegurar que todo el texto del footer sea visible */
+    footer *, .footer *, [class*="footer"] * {
+        color: #374151 !important;
+    }
+    
+    /* Fix específico para links en el footer */
+    footer a, .footer a, [class*="footer"] a {
+        color: #3b82f6 !important;
+    }
     """
     
     with gr.Blocks(
@@ -840,7 +853,7 @@ def create_interface():
         understanding and applies research-validated Flux rules with supreme intelligence.
         
         **🔬 Pariente AI Research Laboratory** • **🚀 Ultra Supreme Intelligence Engine**
-        """)
+        """, elem_classes=["footer-content"])
     
     return interface