Update app.py

app.py

@@ -47,6 +47,178 @@ except ImportError as e:
     CHAMBER_NAMES = []
 
 
+# ============================================================================
+# CONSTANTS
+# ============================================================================
+
+LOGO_TEXT = """
+<pre style="color: #ffb347; background: transparent;">
+██╗  ██╗ █████╗ ███████╗███████╗
+██║  ██║██╔══██╗╚══███╔╝██╔════╝
+███████║███████║  ███╔╝ █████╗  
+██╔══██║██╔══██║ ███╔╝  ██╔══╝  
+██║  ██║██║  ██║███████╗███████╗
+╚═╝  ╚═╝╚═╝  ╚═╝╚══════╝╚══════╝
+</pre>
+
+**Hybrid Attention Entropy System** + **CLOUD** (~181K params)
+
+*"emergence is not creation but recognition"*
+
+**NO SEED FROM PROMPT** — Haze speaks from its internal field, not your input.
+"""
+
+ARCHITECTURE_INFO = """
+### Architecture
+
+**CLOUD** (~181K params):
+- 6 Chambers: FEAR, LOVE, RAGE, VOID, FLOW, COMPLEX
+- Cross-fire stabilization
+- Meta-observer (secondary emotion)
+
+**HAZE** (emergent field):
+- Subjectivity (NO SEED FROM PROMPT)
+- Trauma module (identity)
+- Expert mixture (4 temperatures)
+- Co-occurrence field
+
+**DSL** (Arianna Method):
+- prophecy_debt: |destined - manifested|
+- pain, tension, dissonance
+
+### Philosophy
+
+> *"presence > intelligence"*
+> 
+> *"prophecy ≠ prediction"*
+> 
+> *"minimize(destined - manifested)"*
+"""
+
+FOOTER_TEXT = """
+---
+**Part of the Arianna Method** | [GitHub](https://github.com/ariannamethod/haze) | [Leo](https://github.com/ariannamethod/leo) | [PITOMADOM](https://github.com/ariannamethod/pitomadom)
+
+*Co-authored by Claude (GitHub Copilot Coding Agent), January 2026*
+"""
+
+
+# ============================================================================
+# HELPER FUNCTIONS
+# ============================================================================
+
+def format_cloud_metadata(cloud_data: dict) -> list:
+    """Format CLOUD metadata for display."""
+    meta_lines = []
+    if "primary" in cloud_data:
+        meta_lines.append(f"💭 {cloud_data['primary']}")
+    if "dominant_chamber" in cloud_data:
+        meta_lines.append(f"🏛️ {cloud_data['dominant_chamber']}")
+    return meta_lines
+
+
+def format_field_metadata(metadata: dict) -> str:
+    """Format field metadata into a readable string."""
+    meta_lines = []
+    
+    # CLOUD info
+    if "cloud" in metadata:
+        meta_lines.extend(format_cloud_metadata(metadata["cloud"]))
+    
+    # Temperature and timing
+    if "temperature" in metadata:
+        meta_lines.append(f"🌡️ {metadata['temperature']:.2f}")
+    meta_lines.append(f"⏱️ {metadata.get('generation_time', 'N/A')}")
+    
+    # DSL state
+    if "pain" in metadata:
+        meta_lines.append(f"💔 pain:{metadata['pain']:.2f}")
+    if "prophecy_debt" in metadata:
+        meta_lines.append(f"📜 debt:{metadata['prophecy_debt']:.2f}")
+    
+    # Trauma
+    if "trauma_level" in metadata:
+        meta_lines.append(f"🩹 trauma:{metadata['trauma_level']:.2f}")
+    
+    # Turn count
+    meta_lines.append(f"🔄 turn:{metadata.get('turn_count', 0)}")
+    
+    return " | ".join(meta_lines)
+
+
+def build_response_metadata(response: HazeResponse, cloud_data: dict, haze_field) -> dict:
+    """Build metadata dictionary from HAZE response and CLOUD data."""
+    metadata = {
+        "internal_seed": response.internal_seed,
+        "temperature": response.temperature,
+        "generation_time": f"{response.generation_time:.3f}s",
+        "turn_count": haze_field.turn_count,
+        "enrichment": response.enrichment_count,
+    }
+    
+    if cloud_data:
+        metadata["cloud"] = cloud_data
+    
+    # AMK state
+    if response.amk_state:
+        metadata["amk"] = response.amk_state
+        metadata["prophecy_debt"] = response.amk_state.get("debt", 0)
+        metadata["pain"] = response.amk_state.get("pain", 0)
+    
+    # Trauma info
+    if response.trauma:
+        metadata["trauma_level"] = response.trauma.level
+        metadata["trauma_triggers"] = list(response.trauma.trigger_words)[:5]
+    
+    # Trauma influence
+    if response.trauma_influence:
+        metadata["trauma_influence"] = {
+            "temp_modifier": response.trauma_influence.temperature_modifier,
+            "identity_weight": response.trauma_influence.identity_weight,
+            "should_prefix": response.trauma_influence.should_prefix,
+        }
+    
+    # Expert mixture
+    if response.expert_mixture:
+        metadata["experts"] = response.expert_mixture
+    
+    # Pulse
+    if response.pulse:
+        metadata["pulse"] = {
+            "novelty": response.pulse.novelty,
+            "arousal": response.pulse.arousal,
+            "entropy": response.pulse.entropy,
+        }
+    
+    return metadata
+
+
+def process_cloud_response(cloud_response: CloudResponse) -> dict:
+    """Process CLOUD response into metadata dictionary."""
+    cloud_data = {
+        "primary": cloud_response.primary,
+        "secondary": cloud_response.secondary,
+        "chambers": cloud_response.chamber_activations,
+        "iterations": cloud_response.iterations,
+        "anomaly": {
+            "has_anomaly": cloud_response.anomaly.has_anomaly,
+            "description": cloud_response.anomaly.description,
+            "severity": cloud_response.anomaly.severity,
+        } if cloud_response.anomaly else None,
+    }
+    
+    # Get dominant chamber
+    if cloud_response.chamber_activations:
+        dominant = max(
+            cloud_response.chamber_activations.items(),
+            key=lambda x: x[1]
+        )
+        cloud_data["dominant_chamber"] = dominant[0]
+        cloud_data["dominant_activation"] = dominant[1]
+    
+    return cloud_data
+
+
 # ============================================================================
 # HAZE SESSION WITH FULL CLOUD INTEGRATION
 # ============================================================================
@@ -137,91 +309,39 @@ class HazeSession:
         if not self._initialized:
             await self.initialize()
         
-        # ===== 1. CLOUD PING =====
+        # CLOUD ping
         cloud_data = {}
-        cloud_response = None
-        
-        if self.cloud:
-            try:
-                cloud_response = await self.cloud.ping(user_input)
-                self._cloud_responses.append(cloud_response)
-                
-                cloud_data = {
-                    "primary": cloud_response.primary,
-                    "secondary": cloud_response.secondary,
-                    "chambers": cloud_response.chamber_activations,
-                    "iterations": cloud_response.iterations,
-                    "anomaly": {
-                        "has_anomaly": cloud_response.anomaly.has_anomaly,
-                        "description": cloud_response.anomaly.description,
-                        "severity": cloud_response.anomaly.severity,
-                    } if cloud_response.anomaly else None,
-                }
-                
-                # Get dominant chamber
-                if cloud_response.chamber_activations:
-                    dominant = max(
-                        cloud_response.chamber_activations.items(),
-                        key=lambda x: x[1]
-                    )
-                    cloud_data["dominant_chamber"] = dominant[0]
-                    cloud_data["dominant_activation"] = dominant[1]
-                
-            except Exception as e:
-                cloud_data = {"error": str(e)}
-        
-        # ===== 2. UPDATE HAZE AMK FROM CLOUD =====
-        # CLOUD chambers directly influence HAZE field dynamics
-        if cloud_response and cloud_response.chamber_activations:
-            self.haze.update_from_cloud(cloud_response.chamber_activations)
-        
-        # ===== 3. HAZE RESPOND =====
+        cloud_response = await self._ping_cloud(user_input)
+        if cloud_response:
+            cloud_data = process_cloud_response(cloud_response)
+            # Update HAZE field from CLOUD chambers
+            if cloud_response.chamber_activations:
+                self.haze.update_from_cloud(cloud_response.chamber_activations)
+        
+        # HAZE respond
         response = await self.haze.respond(user_input)
         
-        # ===== BUILD METADATA =====
-        metadata = {
-            "internal_seed": response.internal_seed,
-            "temperature": response.temperature,
-            "generation_time": f"{response.generation_time:.3f}s",
-            "turn_count": self.haze.turn_count,
-            "enrichment": response.enrichment_count,
-        }
-        
-        if cloud_data:
-            metadata["cloud"] = cloud_data
-        
-        # AMK state from response (now integrated into HAZE)
-        if response.amk_state:
-            metadata["amk"] = response.amk_state
-            metadata["prophecy_debt"] = response.amk_state.get("debt", 0)
-            metadata["pain"] = response.amk_state.get("pain", 0)
-        
-        if response.trauma:
-            metadata["trauma_level"] = response.trauma.level
-            metadata["trauma_triggers"] = list(response.trauma.trigger_words)[:5]
-        
-        if response.trauma_influence:
-            metadata["trauma_influence"] = {
-                "temp_modifier": response.trauma_influence.temperature_modifier,
-                "identity_weight": response.trauma_influence.identity_weight,
-                "should_prefix": response.trauma_influence.should_prefix,
-            }
-        
-        if response.expert_mixture:
-            metadata["experts"] = response.expert_mixture
-        
-        if response.pulse:
-            metadata["pulse"] = {
-                "novelty": response.pulse.novelty,
-                "arousal": response.pulse.arousal,
-                "entropy": response.pulse.entropy,
-            }
+        # Build and return metadata
+        metadata = build_response_metadata(response, cloud_data, self.haze)
         
         # Update history
         self.history.append((user_input, response.text))
         
         return response.text, metadata
     
+    async def _ping_cloud(self, user_input: str) -> Optional[CloudResponse]:
+        """Ping CLOUD for emotion detection."""
+        if not self.cloud:
+            return None
+        
+        try:
+            cloud_response = await self.cloud.ping(user_input)
+            self._cloud_responses.append(cloud_response)
+            return cloud_response
+        except Exception as e:
+            print(f"[app] CLOUD ping failed: {e}")
+            return None
+    
     def get_cloud_summary(self) -> dict:
         """Get summary of CLOUD activity across session."""
         if not self._cloud_responses:
@@ -275,47 +395,13 @@ async def async_respond(
     message: str,
     history: List[Tuple[str, str]],
 ) -> Tuple[str, str]:
-    """
-    Async handler for Gradio.
-    """
+    """Async handler for Gradio."""
     session = get_session()
     
     try:
         response_text, metadata = await session.respond(message)
-        
-        # Format metadata for display
-        meta_lines = []
-        
-        # CLOUD info
-        if "cloud" in metadata:
-            cloud = metadata["cloud"]
-            if "primary" in cloud:
-                meta_lines.append(f"💭 {cloud['primary']}")
-            if "dominant_chamber" in cloud:
-                meta_lines.append(f"🏛️ {cloud['dominant_chamber']}")
-        
-        # Temperature and timing
-        if "temperature" in metadata:
-            meta_lines.append(f"🌡️ {metadata['temperature']:.2f}")
-        meta_lines.append(f"⏱️ {metadata.get('generation_time', 'N/A')}")
-        
-        # DSL state
-        if "pain" in metadata:
-            meta_lines.append(f"💔 pain:{metadata['pain']:.2f}")
-        if "prophecy_debt" in metadata:
-            meta_lines.append(f"📜 debt:{metadata['prophecy_debt']:.2f}")
-        
-        # Trauma
-        if "trauma_level" in metadata:
-            meta_lines.append(f"🩹 trauma:{metadata['trauma_level']:.2f}")
-        
-        # Turn count
-        meta_lines.append(f"🔄 turn:{metadata.get('turn_count', 0)}")
-        
-        metadata_str = " | ".join(meta_lines)
-        
+        metadata_str = format_field_metadata(metadata)
         return response_text, metadata_str
-    
     except Exception as e:
         import traceback
         traceback.print_exc()
@@ -336,44 +422,131 @@ def respond(
 
 
 def create_interface():
-    """Create Gradio interface."""
+    """Create and return Gradio interface with custom theme."""
     try:
         import gradio as gr
     except ImportError:
         print("[error] gradio not installed. Run: pip install gradio")
-        return None
+        return None, None
     
-    # Custom CSS for dark gothic theme
-    custom_css = """
-    .gradio-container {
-        background-color: #0a0a0c !important;
-    }
-    .chatbot .message {
-        font-family: 'SF Mono', 'Monaco', 'Inconsolata', 'Fira Code', monospace !important;
-    }
-    """
+    from gradio import ChatMessage
     
-    # ASCII art logo
-    logo = """
-```
-██╗  ██╗ █████╗ ███████╗███████╗
-██║  ██║██╔══██╗╚══███╔╝██╔════╝
-███████║███████║  ███╔╝ █████╗  
-██╔══██║██╔══██║ ███╔╝  ██╔══╝  
-██║  ██║██║  ██║███████╗███████╗
-╚═╝  ╚═╝╚═╝  ╚═╝╚══════╝╚══════╝
-```
-**Hybrid Attention Entropy System** + **CLOUD** (~181K params)
-
-*"emergence is not creation but recognition"*
-
-**NO SEED FROM PROMPT** — Haze speaks from its internal field, not your input.
+    # Create custom dark theme for HAZE
+    haze_theme = gr.themes.Base(
+        primary_hue="orange",
+        secondary_hue="slate",
+        neutral_hue="slate",
+        font=gr.themes.GoogleFont("IBM Plex Mono"),
+    ).set(
+        # Global colors
+        body_background_fill="#0a0a0c",
+        body_background_fill_dark="#0a0a0c",
+        background_fill_primary="#0a0a0c",
+        background_fill_primary_dark="#0a0a0c",
+        background_fill_secondary="#1a1a1f",
+        background_fill_secondary_dark="#1a1a1f",
+        
+        # Text colors - important for visibility!
+        body_text_color="#e8e8e8",
+        body_text_color_dark="#e8e8e8",
+        body_text_color_subdued="#d0d0d0",
+        body_text_color_subdued_dark="#d0d0d0",
+        
+        # Borders - remove white borders
+        border_color_primary="transparent",
+        border_color_primary_dark="transparent",
+        
+        # Input/textbox styling
+        input_background_fill="#1a1a1f",
+        input_background_fill_dark="#1a1a1f",
+        input_border_color="transparent",
+        input_border_color_dark="transparent",
+        input_border_width="0px",
+        
+        # Button styling
+        button_primary_background_fill="#ffb347",
+        button_primary_background_fill_dark="#ffb347",
+        button_primary_text_color="#0a0a0c",
+        button_primary_text_color_dark="#0a0a0c",
+        
+        # Block styling
+        block_background_fill="transparent",
+        block_background_fill_dark="transparent",
+        block_border_width="0px",
+        block_border_color="transparent",
+        block_border_color_dark="transparent",
+        
+        # Shadow removal
+        shadow_drop="none",
+        shadow_drop_lg="none",
+    )
+    
+    # Additional CSS for chatbot message visibility
+    custom_css = """
+        /* Force dark background and visible text for all elements */
+        * {
+            color: #e8e8e8 !important;
+        }
+        
+        /* Chatbot container - dark background */
+        .chatbot {
+            background: #0a0a0c !important;
+        }
+        
+        /* Message bubbles with strong contrast */
+        .message-wrap {
+            background: transparent !important;
+        }
+        
+        /* User messages - dark gray background with white text */
+        .message.user, .user-message, [data-testid="user"] {
+            background-color: #1a1a1f !important;
+            color: #ffffff !important;
+        }
+        
+        .message.user *, .user-message *, [data-testid="user"] * {
+            color: #ffffff !important;
+        }
+        
+        /* Bot/Haze messages - slightly lighter background with orange text */
+        .message.bot, .bot-message, [data-testid="bot"] {
+            background-color: #2a2a2f !important;
+            color: #ffb347 !important;
+        }
+        
+        .message.bot *, .bot-message *, [data-testid="bot"] * {
+            color: #ffb347 !important;
+        }
+        
+        /* Ensure markdown in messages is visible */
+        .message p, .message span, .message div {
+            color: inherit !important;
+        }
+        
+        /* Remove borders */
+        .contain, .block, .chatbot {
+            border: none !important;
+            box-shadow: none !important;
+        }
+        
+        /* Input field visibility */
+        input, textarea {
+            background-color: #1a1a1f !important;
+            color: #e8e8e8 !important;
+            border: 1px solid #333 !important;
+        }
+        
+        /* Markdown text visibility */
+        .markdown-body, .prose {
+            color: #d0d0d0 !important;
+        }
     """
     
-    with gr.Blocks(css=custom_css, title="HAZE + CLOUD") as demo:
-        gr.Markdown(logo)
+    with gr.Blocks(theme=haze_theme, css=custom_css) as demo:
+        gr.Markdown(LOGO_TEXT)
         
         with gr.Row():
+            # Main chat interface
             with gr.Column(scale=3):
                 chatbot = gr.Chatbot(
                     label="Conversation",
@@ -398,50 +571,27 @@ def create_interface():
                     max_lines=2,
                 )
             
+            # Sidebar with architecture info
             with gr.Column(scale=1):
-                gr.Markdown("""
-### Architecture
-
-**CLOUD** (~181K params):
-- 6 Chambers: FEAR, LOVE, RAGE, VOID, FLOW, COMPLEX
-- Cross-fire stabilization
-- Meta-observer (secondary emotion)
-
-**HAZE** (emergent field):
-- Subjectivity (NO SEED FROM PROMPT)
-- Trauma module (identity)
-- Expert mixture (4 temperatures)
-- Co-occurrence field
-
-**DSL** (Arianna Method):
-- prophecy_debt: |destined - manifested|
-- pain, tension, dissonance
-
-### Philosophy
-
-> *"presence > intelligence"*
-> 
-> *"prophecy ≠ prediction"*
-> 
-> *"minimize(destined - manifested)"*
-                """)
+                gr.Markdown(ARCHITECTURE_INFO)
         
+        # Chat handler
         def chat(message, history):
             response, metadata = respond(message, history)
-            history = history + [(message, response)]
+            history = history + [
+                ChatMessage(role="user", content=message),
+                ChatMessage(role="assistant", content=response)
+            ]
             return "", history, metadata
         
+        # Connect handlers
         msg.submit(chat, [msg, chatbot], [msg, chatbot, metadata_display])
         submit.click(chat, [msg, chatbot], [msg, chatbot, metadata_display])
         
-        gr.Markdown("""
----
-**Part of the Arianna Method** | [GitHub](https://github.com/ariannamethod/haze) | [Leo](https://github.com/ariannamethod/leo) | [PITOMADOM](https://github.com/ariannamethod/pitomadom)
-
-*Co-authored by Claude (GitHub Copilot Coding Agent), January 2026*
-        """)
+        # Footer
+        gr.Markdown(FOOTER_TEXT)
     
-    return demo
+    return demo, haze_theme, custom_css
 
 
 # ============================================================================
@@ -458,16 +608,19 @@ def main():
     print("=" * 60)
     print()
     
-    demo = create_interface()
+    result = create_interface()
     
-    if demo is None:
+    if result is None or result[0] is None:
         print("[error] Could not create interface")
         return
     
+    demo, theme, css = result
+    
     print("Starting Gradio server...")
     print()
     
     # Launch with HuggingFace Spaces compatible settings
+    # In Gradio 6.0+, theme and css are passed to launch()
     demo.launch(
         server_name="0.0.0.0",
         server_port=7860,