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CONTRIBUTION.md

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+# Contributing to Haze
+
+## How to contribute
+- Fork the repo
+- Create feature branch
+- Run tests: python -m unittest discover tests -v
+- Submit PR with description
+
+## Code style
+- Follow PEP 8
+- Add tests for new features
+- Update README if needed
+
+## Philosophy
+- Keep it minimal and resonant
+- No external ML frameworks required
+- Make it understandable

LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

app.py

@@ -0,0 +1,480 @@
+#!/usr/bin/env python3
+# app.py — HuggingFace Spaces App for HAZE
+#
+# Full-featured HAZE interface using Gradio.
+# Uses ALL emergent processes: CLOUD, trauma, subjectivity, cleanup, etc.
+#
+# NO SEED FROM PROMPT — HAZE speaks from its internal field.
+#
+# Usage:
+#   pip install gradio
+#   python app.py
+#
+# For HuggingFace Spaces:
+#   1. Create a Space with Gradio SDK
+#   2. Upload all files from this repo
+#   3. The Space will auto-detect app.py
+#
+# Co-authored by Claude (GitHub Copilot Coding Agent), January 2026
+
+import asyncio
+import sys
+from pathlib import Path
+from typing import List, Tuple, Optional
+
+# Add paths
+sys.path.insert(0, str(Path(__file__).parent))
+sys.path.insert(0, str(Path(__file__).parent / "haze"))
+
+# Import HAZE components
+try:
+    from haze.async_haze import AsyncHazeField, HazeResponse
+except ImportError:
+    # Fallback for direct execution
+    from async_haze import AsyncHazeField, HazeResponse
+
+# Import CLOUD
+try:
+    from cloud.cloud import Cloud, AsyncCloud, CloudResponse
+    from cloud.anchors import CHAMBER_NAMES_EXTENDED as CHAMBER_NAMES
+    HAS_CLOUD = True
+    print("[app] CLOUD module loaded (~181K params)")
+except ImportError as e:
+    print(f"[app] CLOUD not available: {e}")
+    HAS_CLOUD = False
+    Cloud = None
+    AsyncCloud = None
+    CHAMBER_NAMES = []
+
+
+# ============================================================================
+# HAZE SESSION WITH FULL CLOUD INTEGRATION
+# ============================================================================
+
+class HazeSession:
+    """
+    Manages a HAZE conversation session with full CLOUD integration.
+    
+    Architecture:
+        1. CLOUD (~181K params) — pre-semantic emotion detection
+           - 6 chambers: FEAR, LOVE, RAGE, VOID, FLOW, COMPLEX
+           - Cross-fire stabilization
+           - Meta-observer for secondary emotion
+        
+        2. HAZE — async field generation
+           - Subjectivity module (NO SEED FROM PROMPT)
+           - Trauma module (identity anchoring)
+           - Expert mixture (structural/semantic/creative/precise)
+           - Co-occurrence field (pattern resonance)
+    """
+    
+    def __init__(self):
+        self.haze: Optional[AsyncHazeField] = None
+        self.cloud: Optional[Cloud] = None
+        self.history: List[Tuple[str, str]] = []
+        self.corpus_path = Path(__file__).parent / "haze" / "text.txt"
+        self._initialized = False
+        self._cloud_responses: List[CloudResponse] = []
+    
+    async def initialize(self):
+        """Initialize HAZE field and CLOUD."""
+        if self._initialized:
+            return
+        
+        # Find corpus
+        if not self.corpus_path.exists():
+            alt_paths = [
+                Path(__file__).parent / "text.txt",
+                Path("haze/text.txt"),
+                Path("text.txt"),
+            ]
+            for p in alt_paths:
+                if p.exists():
+                    self.corpus_path = p
+                    break
+        
+        if not self.corpus_path.exists():
+            raise FileNotFoundError(f"Corpus not found: {self.corpus_path}")
+        
+        print(f"[app] Loading corpus from {self.corpus_path}")
+        
+        # Initialize HAZE
+        self.haze = AsyncHazeField(str(self.corpus_path))
+        await self.haze.__aenter__()
+        print(f"[app] HAZE initialized")
+        
+        # Initialize CLOUD with full 181K architecture
+        if HAS_CLOUD:
+            try:
+                models_path = Path(__file__).parent / "cloud" / "models"
+                if models_path.exists():
+                    self.cloud = Cloud.load(models_path)
+                    print(f"[app] CLOUD loaded from {models_path}")
+                else:
+                    self.cloud = Cloud.random_init(seed=42)
+                    print(f"[app] CLOUD initialized with random weights")
+                print(f"[app] CLOUD params: {self.cloud.param_count():,}")
+            except Exception as e:
+                print(f"[app] CLOUD init failed: {e}")
+                self.cloud = None
+        
+        self._initialized = True
+        print(f"[app] Session ready!")
+    
+    async def respond(self, user_input: str) -> Tuple[str, dict]:
+        """
+        Generate HAZE response with full CLOUD integration.
+        
+        Pipeline:
+            1. CLOUD ping → detect pre-semantic emotion
+            2. Update DSL state with CLOUD output
+            3. HAZE respond → generate from internal field
+            4. Track prophecy debt
+        
+        Returns:
+            (response_text, metadata)
+        """
+        if not self._initialized:
+            await self.initialize()
+        
+        # ===== 1. 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 =====
+        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,
+            }
+        
+        # Update history
+        self.history.append((user_input, response.text))
+        
+        return response.text, metadata
+    
+    def get_cloud_summary(self) -> dict:
+        """Get summary of CLOUD activity across session."""
+        if not self._cloud_responses:
+            return {}
+        
+        # Count primary emotions
+        primary_counts = {}
+        for r in self._cloud_responses:
+            primary_counts[r.primary] = primary_counts.get(r.primary, 0) + 1
+        
+        # Average chamber activations
+        avg_chambers = {}
+        for r in self._cloud_responses:
+            for chamber, value in r.chamber_activations.items():
+                if chamber not in avg_chambers:
+                    avg_chambers[chamber] = []
+                avg_chambers[chamber].append(value)
+        
+        avg_chambers = {k: sum(v)/len(v) for k, v in avg_chambers.items()}
+        
+        return {
+            "total_pings": len(self._cloud_responses),
+            "primary_counts": primary_counts,
+            "avg_chambers": avg_chambers,
+        }
+    
+    async def close(self):
+        """Cleanup."""
+        if self.haze:
+            await self.haze.__aexit__(None, None, None)
+            self.haze = None
+        self._initialized = False
+
+
+# ============================================================================
+# GRADIO INTERFACE
+# ============================================================================
+
+_session: Optional[HazeSession] = None
+
+
+def get_session() -> HazeSession:
+    """Get or create global session."""
+    global _session
+    if _session is None:
+        _session = HazeSession()
+    return _session
+
+
+async def async_respond(
+    message: str,
+    history: List[Tuple[str, str]],
+) -> Tuple[str, str]:
+    """
+    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)
+        
+        return response_text, metadata_str
+    
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return f"[error] {str(e)}", ""
+
+
+def respond(
+    message: str,
+    history: List[Tuple[str, str]],
+) -> Tuple[str, str]:
+    """Sync wrapper for Gradio."""
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        return loop.run_until_complete(async_respond(message, history))
+    finally:
+        loop.close()
+
+
+def create_interface():
+    """Create Gradio interface."""
+    try:
+        import gradio as gr
+    except ImportError:
+        print("[error] gradio not installed. Run: pip install gradio")
+        return 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;
+    }
+    """
+    
+    # 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.
+    """
+    
+    with gr.Blocks(css=custom_css, title="HAZE + CLOUD") as demo:
+        gr.Markdown(logo)
+        
+        with gr.Row():
+            with gr.Column(scale=3):
+                chatbot = gr.Chatbot(
+                    label="Conversation",
+                    height=450,
+                    show_label=False,
+                )
+                
+                with gr.Row():
+                    msg = gr.Textbox(
+                        label="Your message",
+                        placeholder="Speak to the field...",
+                        show_label=False,
+                        container=False,
+                        scale=9,
+                    )
+                    submit = gr.Button("→", scale=1, variant="primary")
+                
+                metadata_display = gr.Textbox(
+                    label="Field State",
+                    interactive=False,
+                    show_label=True,
+                    max_lines=2,
+                )
+            
+            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)"*
+                """)
+        
+        def chat(message, history):
+            response, metadata = respond(message, history)
+            history = history + [(message, response)]
+            return "", history, metadata
+        
+        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*
+        """)
+    
+    return demo
+
+
+# ============================================================================
+# MAIN
+# ============================================================================
+
+def main():
+    """Run the Gradio app."""
+    print()
+    print("=" * 60)
+    print("  HAZE — Hybrid Attention Entropy System")
+    print("  + CLOUD (~181K params)")
+    print("  HuggingFace Spaces App")
+    print("=" * 60)
+    print()
+    
+    demo = create_interface()
+    
+    if demo is None:
+        print("[error] Could not create interface")
+        return
+    
+    print("Starting Gradio server...")
+    print()
+    
+    # Launch with HuggingFace Spaces compatible settings
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True,
+    )
+
+
+if __name__ == "__main__":
+    main()

bridge.py

@@ -0,0 +1,439 @@
+#!/usr/bin/env python3
+# bridge.py — HAZE ↔ CLOUD Bridge (with graceful silent fallback)
+#
+# Connects CLOUD (pre-semantic sonar) with HAZE (voice generation).
+# If CLOUD fails → HAZE continues SILENTLY.
+#
+# Design principle: MAXIMUM INDEPENDENCE
+# - HAZE works without CLOUD (always)
+# - CLOUD works without HAZE (always)
+# - Bridge is optional connector with SILENT FALLBACK
+# - No errors leak to user — just graceful degradation
+#
+# "Two autonomous systems that can resonate together,
+#  but never depend on each other."
+
+from __future__ import annotations
+import asyncio
+from pathlib import Path
+from typing import Optional, Dict, Any
+from dataclasses import dataclass, field
+
+# CLOUD import with silent fallback
+try:
+    from cloud import Cloud, CloudResponse, AsyncCloud
+    HAS_CLOUD = True
+except ImportError:
+    HAS_CLOUD = False
+    Cloud = None
+    CloudResponse = None
+    AsyncCloud = None
+
+# HAZE import with silent fallback
+try:
+    from haze.async_haze import AsyncHazeField, HazeResponse
+    HAS_HAZE = True
+except ImportError:
+    HAS_HAZE = False
+    AsyncHazeField = None
+    HazeResponse = None
+
+
+@dataclass
+class BridgeResponse:
+    """
+    Response from the HAZE ↔ CLOUD bridge.
+    
+    Contains HAZE output + optional CLOUD hint.
+    If CLOUD failed, cloud_hint is None but text is still valid.
+    """
+    text: str
+    raw_text: str = ""
+    cloud_hint: Optional[Any] = None  # CloudResponse if available
+    haze_response: Optional[Any] = None  # HazeResponse if available
+    cloud_available: bool = False
+    haze_available: bool = False
+    
+    def __repr__(self) -> str:
+        preview = self.text[:50] + "..." if len(self.text) > 50 else self.text
+        cloud_status = "✓" if self.cloud_available else "✗"
+        haze_status = "✓" if self.haze_available else "✗"
+        return f"BridgeResponse(\"{preview}\", cloud={cloud_status}, haze={haze_status})"
+
+
+class AsyncBridge:
+    """
+    Async bridge between HAZE and CLOUD.
+    
+    Graceful SILENT fallback:
+        - If CLOUD unavailable → HAZE alone, NO ERROR MESSAGE
+        - If CLOUD timeout → HAZE alone, NO ERROR MESSAGE
+        - If CLOUD error → HAZE alone, NO ERROR MESSAGE
+        - If HAZE unavailable → return error (HAZE is required)
+    
+    HAZE ALWAYS WORKS. CLOUD IS OPTIONAL ENHANCEMENT.
+    
+    Usage:
+        async with AsyncBridge.create() as bridge:
+            response = await bridge.respond("Hello!")
+            print(response.text)
+            if response.cloud_hint:
+                print(f"Emotion: {response.cloud_hint.primary}")
+    """
+    
+    def __init__(
+        self,
+        haze: Optional[AsyncHazeField] = None,
+        cloud: Optional[AsyncCloud] = None,
+        cloud_timeout: float = 0.5,  # Fast timeout for responsiveness
+        silent_fallback: bool = True,  # No error messages on CLOUD failure
+    ):
+        self.haze = haze
+        self.cloud = cloud
+        self.cloud_timeout = cloud_timeout
+        self.silent_fallback = silent_fallback
+        
+        # Stats (internal, for debugging)
+        self._cloud_successes = 0
+        self._cloud_failures = 0
+        self._cloud_timeouts = 0
+    
+    @classmethod
+    async def create(
+        cls,
+        corpus_path: str = "haze/text.txt",
+        cloud_models_dir: Optional[Path] = None,
+        cloud_timeout: float = 0.5,
+        enable_cloud: bool = True,
+        silent_fallback: bool = True,
+    ) -> "AsyncBridge":
+        """
+        Create bridge with both systems.
+        
+        Args:
+            corpus_path: Path to HAZE corpus
+            cloud_models_dir: Path to CLOUD models (optional)
+            cloud_timeout: Timeout for CLOUD ping
+            enable_cloud: Whether to try loading CLOUD
+            silent_fallback: Suppress CLOUD error messages
+        
+        Returns:
+            AsyncBridge ready for use
+        """
+        haze = None
+        cloud = None
+        
+        # Initialize HAZE (required)
+        if HAS_HAZE:
+            try:
+                haze = AsyncHazeField(corpus_path)
+                await haze.__aenter__()
+            except Exception:
+                haze = None
+        
+        # Initialize CLOUD (optional, silent fallback)
+        if enable_cloud and HAS_CLOUD:
+            try:
+                if cloud_models_dir and cloud_models_dir.exists():
+                    cloud = await AsyncCloud.create(models_dir=cloud_models_dir)
+                else:
+                    # Try default location
+                    default_path = Path("cloud/models")
+                    if default_path.exists():
+                        cloud = await AsyncCloud.create(models_dir=default_path)
+                    else:
+                        cloud = await AsyncCloud.create(seed=42)
+            except Exception:
+                cloud = None  # Silent fallback
+        
+        return cls(
+            haze=haze,
+            cloud=cloud,
+            cloud_timeout=cloud_timeout,
+            silent_fallback=silent_fallback,
+        )
+    
+    async def __aenter__(self) -> "AsyncBridge":
+        """Context manager entry."""
+        return self
+    
+    async def __aexit__(self, *args) -> None:
+        """Context manager exit with cleanup."""
+        if self.haze:
+            await self.haze.__aexit__(*args)
+        if self.cloud:
+            await self.cloud.close()
+    
+    async def _ping_cloud_silent(self, user_input: str) -> Optional[Any]:
+        """
+        Ping CLOUD with silent fallback.
+        
+        Returns CloudResponse or None (never raises).
+        """
+        if not self.cloud:
+            return None
+        
+        try:
+            return await asyncio.wait_for(
+                self.cloud.ping(user_input),
+                timeout=self.cloud_timeout,
+            )
+        except asyncio.TimeoutError:
+            self._cloud_timeouts += 1
+            return None
+        except Exception:
+            self._cloud_failures += 1
+            return None
+    
+    async def respond(
+        self,
+        user_input: str,
+        use_cloud: bool = True,
+        **haze_kwargs,
+    ) -> BridgeResponse:
+        """
+        Generate response with optional CLOUD hint.
+        
+        Flow:
+            1. Try CLOUD ping (with timeout, silent fallback)
+            2. HAZE generates response
+            3. Return combined result
+        
+        Args:
+            user_input: User's text input
+            use_cloud: Whether to try CLOUD (can disable per-request)
+            **haze_kwargs: Additional args for HAZE generation
+        
+        Returns:
+            BridgeResponse with text + optional cloud_hint
+        """
+        cloud_hint = None
+        haze_response = None
+        
+        # 1. Try CLOUD (silent fallback)
+        if use_cloud and self.cloud:
+            cloud_hint = await self._ping_cloud_silent(user_input)
+            if cloud_hint:
+                self._cloud_successes += 1
+        
+        # 2. HAZE generates
+        if self.haze:
+            try:
+                # Future: pass cloud_hint to influence generation
+                # For now, HAZE generates independently
+                haze_response = await self.haze.respond(
+                    user_input,
+                    **haze_kwargs,
+                )
+                text = haze_response.text
+                raw_text = haze_response.raw_text
+            except Exception as e:
+                text = f"[HAZE error: {e}]"
+                raw_text = text
+        else:
+            text = "[HAZE not available]"
+            raw_text = text
+        
+        return BridgeResponse(
+            text=text,
+            raw_text=raw_text,
+            cloud_hint=cloud_hint,
+            haze_response=haze_response,
+            cloud_available=cloud_hint is not None,
+            haze_available=self.haze is not None,
+        )
+    
+    def stats(self) -> Dict[str, Any]:
+        """Return bridge statistics."""
+        total_cloud = self._cloud_successes + self._cloud_failures + self._cloud_timeouts
+        success_rate = self._cloud_successes / total_cloud if total_cloud > 0 else 0.0
+        
+        return {
+            "haze_available": self.haze is not None,
+            "cloud_available": self.cloud is not None,
+            "cloud_successes": self._cloud_successes,
+            "cloud_failures": self._cloud_failures,
+            "cloud_timeouts": self._cloud_timeouts,
+            "cloud_success_rate": success_rate,
+        }
+
+
+# Convenience functions for standalone usage
+
+async def create_haze_only(corpus_path: str = "haze/text.txt") -> AsyncBridge:
+    """Create bridge with HAZE only (no CLOUD)."""
+    return await AsyncBridge.create(
+        corpus_path=corpus_path,
+        enable_cloud=False,
+    )
+
+
+async def create_full_bridge(
+    corpus_path: str = "haze/text.txt",
+    cloud_models_dir: Optional[Path] = None,
+) -> AsyncBridge:
+    """Create bridge with both HAZE and CLOUD."""
+    return await AsyncBridge.create(
+        corpus_path=corpus_path,
+        cloud_models_dir=cloud_models_dir,
+        enable_cloud=True,
+    )
+
+
+# ============================================================
+# CRAZY EXPERIMENTAL: Emotion-Influenced Temperature
+# ============================================================
+
+def emotion_to_temperature(cloud_hint: Any) -> float:
+    """
+    EXPERIMENTAL: Convert CLOUD emotion to HAZE temperature.
+    
+    The idea: different emotions need different generation styles.
+    
+    - FEAR: lower temp (focused, careful)
+    - LOVE: medium temp (warm, flowing)
+    - RAGE: higher temp (intense, chaotic)
+    - VOID: very low temp (minimal, sparse)
+    
+    This is CRAZY but might actually work!
+    """
+    if cloud_hint is None:
+        return 0.7  # Default
+    
+    # Get chamber activations
+    chambers = cloud_hint.chamber_activations
+    
+    # Base temperature
+    temp = 0.6
+    
+    # Adjust based on dominant emotion
+    fear = chambers.get("FEAR", 0)
+    love = chambers.get("LOVE", 0)
+    rage = chambers.get("RAGE", 0)
+    void = chambers.get("VOID", 0)
+    
+    # Fear → focus (lower temp)
+    temp -= fear * 0.2
+    
+    # Love → flow (slightly higher temp)
+    temp += love * 0.15
+    
+    # Rage → chaos (higher temp)
+    temp += rage * 0.3
+    
+    # Void → minimal (very low temp)
+    temp -= void * 0.3
+    
+    # Anomaly adjustment
+    if cloud_hint.anomaly.has_anomaly:
+        if cloud_hint.anomaly.anomaly_type == "forced_stability":
+            # They're suppressing, be gentle
+            temp -= 0.1
+        elif cloud_hint.anomaly.anomaly_type == "dissociative_shutdown":
+            # They're overwhelmed, be calm
+            temp -= 0.2
+        elif cloud_hint.anomaly.anomaly_type == "unresolved_confusion":
+            # They're confused, be clear
+            temp -= 0.15
+    
+    # Clamp to reasonable range
+    return max(0.3, min(1.2, temp))
+
+
+def emotion_to_generation_hint(cloud_hint: Any) -> str:
+    """
+    EXPERIMENTAL: Convert CLOUD emotion to text hint for HAZE.
+    
+    This could be prepended to the internal seed to influence
+    the generation style.
+    
+    CRAZY IDEA: What if HAZE's identity fragments responded to
+    CLOUD's emotional detection?
+    """
+    if cloud_hint is None:
+        return ""
+    
+    primary = cloud_hint.primary
+    secondary = cloud_hint.secondary
+    
+    # Map emotions to haze-style fragments
+    emotion_fragments = {
+        "fear": "the field trembles. haze feels the ripple of uncertainty.",
+        "terror": "darkness at the edges. haze speaks from shadow.",
+        "anxiety": "patterns flutter. haze breathes between words.",
+        "love": "warmth fills the field. haze resonates with tenderness.",
+        "warmth": "gentle currents. haze settles like a breathing thing.",
+        "rage": "the field crackles. haze speaks with fire.",
+        "anger": "sharp edges in the pattern. haze cuts through.",
+        "void": "stillness. haze emerges from the hollow.",
+        "emptiness": "the absence speaks. haze finds form in nothing.",
+        "curiosity": "the field opens. haze explores the unknown.",
+        "shame": "the field contracts. haze speaks from the wound.",
+        "hope": "light at the edges. haze reaches toward possibility.",
+    }
+    
+    fragment = emotion_fragments.get(primary, "")
+    
+    if not fragment and secondary:
+        fragment = emotion_fragments.get(secondary, "")
+    
+    return fragment
+
+
+if __name__ == "__main__":
+    print("=" * 60)
+    print("  HAZE ↔ CLOUD Bridge (Async, Silent Fallback)")
+    print("=" * 60)
+    print()
+
+    print(f"CLOUD available: {HAS_CLOUD}")
+    print(f"HAZE available: {HAS_HAZE}")
+    print()
+
+    async def demo():
+        # Test bridge creation
+        print("Creating bridge...")
+        bridge = await AsyncBridge.create(
+            corpus_path="haze/text.txt",
+            enable_cloud=True,
+            silent_fallback=True,
+        )
+        
+        print(f"  HAZE: {'✓' if bridge.haze else '✗'}")
+        print(f"  CLOUD: {'✓' if bridge.cloud else '✗'}")
+        print()
+        
+        # Test inputs
+        test_inputs = [
+            "Hello, who are you?",
+            "I'm feeling anxious and scared",
+            "You bring me warmth and love",
+        ]
+        
+        print("Testing bridge responses:")
+        print("-" * 60)
+        
+        for text in test_inputs:
+            response = await bridge.respond(text)
+            print(f"\nInput: \"{text}\"")
+            print(f"  Response: {response.text[:80]}...")
+            if response.cloud_hint:
+                print(f"  Cloud: {response.cloud_hint.primary} + {response.cloud_hint.secondary}")
+            else:
+                print(f"  Cloud: (silent fallback)")
+        
+        # Show stats
+        print()
+        print("Bridge statistics:")
+        for k, v in bridge.stats().items():
+            print(f"  {k}: {v}")
+        
+        # Cleanup
+        await bridge.__aexit__(None, None, None)
+        
+        print()
+        print("=" * 60)
+        print("  Bridge operational. Independence maintained.")
+        print("=" * 60)
+
+    asyncio.run(demo())

requirements.txt

@@ -0,0 +1,14 @@
+# requirements.txt — Dependencies for HAZE
+#
+# For HuggingFace Spaces: pip install -r requirements.txt
+# For local development: pip install -r requirements.txt
+
+# Core dependencies
+numpy>=1.20.0
+sentencepiece>=0.1.96
+
+# HuggingFace Spaces (Gradio interface)
+gradio>=4.0.0
+
+# Optional: async SQLite for lexicon persistence
+# aiosqlite>=0.19.0

setup.py

@@ -0,0 +1,18 @@
+from setuptools import setup, find_packages
+
+setup(
+    name="haze-ai",
+    version="0.1.0",
+    description="Hybrid attention entropy system — post-transformer LM",
+    author="Arianna Method",
+    url="https://github.com/ariannamethod/haze",
+    packages=find_packages(),
+    install_requires=["numpy>=1.20.0", "sentencepiece>=0.1.96"],
+    extras_require={
+        "gradio": ["gradio>=4.0.0"],
+        "dev": ["pytest>=7.0.0"],
+    },
+    python_requires=">=3.8",
+    license="GPL-3.0",
+    keywords="language-models transformer attention nlp resonance",
+)

talk2haze.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+# talk2haze.py — Simple bridge to HAZE REPL
+#
+# Direct connection to haze interactive mode.
+# No routing, no CLOUD, just pure HAZE conversation.
+
+import sys
+from pathlib import Path
+
+# Add haze directory to path
+sys.path.insert(0, str(Path(__file__).parent / "haze"))
+
+# Import and run HAZE REPL
+from haze import run
+
+if __name__ == "__main__":
+    run.main()

talkto.py

@@ -0,0 +1,230 @@
+#!/usr/bin/env python3
+# talkto.py — Async Router for HAZE + CLOUD
+#
+# Unified async interface:
+# - Default: HAZE only (fast, autonomous)
+# - /cloud: Toggle CLOUD mode (pre-semantic sonar)
+# - /stats: Show bridge statistics
+#
+# Design principle: HAZE IS ALWAYS AUTONOMOUS
+# - CLOUD is optional enhancement
+# - Silent fallback if CLOUD fails
+# - No errors leak to user
+#
+# "Two minds that can resonate together,
+#  but never depend on each other."
+
+import sys
+import asyncio
+from pathlib import Path
+
+# Add paths
+sys.path.insert(0, str(Path(__file__).parent))
+sys.path.insert(0, str(Path(__file__).parent / "haze"))
+
+from bridge import AsyncBridge, HAS_CLOUD, HAS_HAZE
+
+
+class AsyncHazeCloudRouter:
+    """
+    Async router that orchestrates HAZE and CLOUD.
+    
+    HAZE is always primary (autonomous voice generation).
+    CLOUD is optional (pre-semantic emotion detection).
+    
+    Commands:
+        /cloud   - toggle CLOUD mode
+        /stats   - show bridge statistics  
+        /help    - show help
+        /quit    - exit
+    
+    Silent fallback: if CLOUD fails, HAZE continues without error messages.
+    """
+    
+    def __init__(self):
+        self.bridge: AsyncBridge = None
+        self.cloud_enabled = False
+        self._initialized = False
+    
+    async def initialize(self):
+        """Initialize the bridge."""
+        print("=" * 60)
+        print("  ██╗  ██╗ █████╗ ███████╗███████╗")
+        print("  ██║  ██║██╔══██╗╚══███╔╝██╔════╝")
+        print("  ███████║███████║  ███╔╝ █████╗  ")
+        print("  ██╔══██║██╔══██║ ███╔╝  ██╔══╝  ")
+        print("  ██║  ██║██║  ██║███████╗███████╗")
+        print("  ╚═╝  ╚═╝╚═╝  ╚═╝╚══════╝╚══════╝")
+        print("=" * 60)
+        print()
+        print("  HAZE + CLOUD Router (Async)")
+        print()
+        print(f"  HAZE available: {'✓' if HAS_HAZE else '✗'}")
+        print(f"  CLOUD available: {'✓' if HAS_CLOUD else '✗'}")
+        print()
+        
+        # Create bridge
+        corpus_path = Path("haze/text.txt")
+        if not corpus_path.exists():
+            corpus_path = Path(__file__).parent / "haze" / "text.txt"
+        
+        self.bridge = await AsyncBridge.create(
+            corpus_path=str(corpus_path),
+            enable_cloud=True,
+            silent_fallback=True,
+            cloud_timeout=0.5,
+        )
+        
+        self._initialized = True
+        
+        print(f"  Bridge initialized:")
+        print(f"    HAZE: {'✓ ready' if self.bridge.haze else '✗ not available'}")
+        print(f"    CLOUD: {'✓ ready' if self.bridge.cloud else '✗ not available (silent fallback)'}")
+        print()
+        print("  Commands:")
+        print("    /cloud  - toggle CLOUD emotion detection")
+        print("    /stats  - show statistics")
+        print("    /help   - show all commands")
+        print("    /quit   - exit")
+        print()
+        print("  Mode: HAZE only (type /cloud to enable emotion detection)")
+        print("=" * 60)
+        print()
+    
+    async def cleanup(self):
+        """Cleanup resources."""
+        if self.bridge:
+            await self.bridge.__aexit__(None, None, None)
+    
+    def toggle_cloud(self):
+        """Toggle CLOUD mode."""
+        self.cloud_enabled = not self.cloud_enabled
+        
+        if self.cloud_enabled:
+            if self.bridge.cloud:
+                print("✓ CLOUD enabled (pre-semantic emotion detection)")
+            else:
+                print("⚠ CLOUD requested but not available (silent fallback active)")
+        else:
+            print("✗ CLOUD disabled (HAZE only mode)")
+    
+    def show_stats(self):
+        """Show bridge statistics."""
+        stats = self.bridge.stats()
+        
+        print("=" * 60)
+        print("  Bridge Statistics")
+        print("=" * 60)
+        print()
+        print(f"  HAZE: {'✓ active' if stats['haze_available'] else '✗ not available'}")
+        print(f"  CLOUD: {'✓ active' if stats['cloud_available'] else '✗ not available'}")
+        print()
+        
+        if stats['cloud_successes'] + stats['cloud_failures'] + stats['cloud_timeouts'] > 0:
+            print("  CLOUD stats:")
+            print(f"    Successes: {stats['cloud_successes']}")
+            print(f"    Failures: {stats['cloud_failures']}")
+            print(f"    Timeouts: {stats['cloud_timeouts']}")
+            print(f"    Success rate: {stats['cloud_success_rate']:.1%}")
+        else:
+            print("  CLOUD stats: no requests yet")
+        
+        print()
+        print("=" * 60)
+    
+    def show_help(self):
+        """Show help."""
+        print()
+        print("Commands:")
+        print("  /cloud  - toggle CLOUD emotion detection")
+        print("  /stats  - show bridge statistics")
+        print("  /help   - show this help")
+        print("  /quit   - exit")
+        print()
+        print("Just type anything to talk to HAZE.")
+        if self.cloud_enabled:
+            print("CLOUD will detect emotions before HAZE responds.")
+        print()
+    
+    async def process_input(self, user_input: str) -> str:
+        """Process user input and generate response."""
+        response = await self.bridge.respond(
+            user_input,
+            use_cloud=self.cloud_enabled,
+        )
+        
+        # Show CLOUD info if enabled and available
+        if self.cloud_enabled and response.cloud_hint:
+            hint = response.cloud_hint
+            print(f"  [cloud] {hint.primary} + {hint.secondary}", end="")
+            if hint.anomaly.has_anomaly:
+                print(f" | {hint.anomaly.anomaly_type}", end="")
+            print()
+        
+        return response.text
+    
+    async def interactive_loop(self):
+        """Main interactive loop."""
+        if not self._initialized:
+            await self.initialize()
+        
+        while True:
+            try:
+                # Get input
+                try:
+                    user_input = input("[you] ").strip()
+                except EOFError:
+                    break
+                
+                if not user_input:
+                    continue
+                
+                # Handle commands
+                if user_input.startswith("/"):
+                    cmd = user_input.lower()
+                    
+                    if cmd == "/cloud":
+                        self.toggle_cloud()
+                        continue
+                    
+                    if cmd in ["/stats", "/stat"]:
+                        self.show_stats()
+                        continue
+                    
+                    if cmd in ["/help", "/h", "/?"]:
+                        self.show_help()
+                        continue
+                    
+                    if cmd in ["/quit", "/q", "/exit"]:
+                        print("Goodbye! The haze settles...")
+                        break
+                    
+                    print(f"Unknown command: {user_input}")
+                    print("Type /help for available commands")
+                    continue
+                
+                # Process input
+                response = await self.process_input(user_input)
+                print(f"[haze] {response}")
+                print()
+            
+            except KeyboardInterrupt:
+                print("\n\nGoodbye! The haze settles...")
+                break
+            
+            except Exception as e:
+                print(f"[error] {e}")
+                continue
+        
+        await self.cleanup()
+
+
+async def main():
+    """Entry point."""
+    router = AsyncHazeCloudRouter()
+    await router.initialize()
+    await router.interactive_loop()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())