feat: GENOME.md — full codebase analysis (#673)

GENOME.md

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+# GENOME.md — the-door
+
+**Generated:** 2026-04-14
+**Repo:** Timmy_Foundation/the-door
+**Description:** Crisis Front Door — a single URL where a man at 3am can talk to Timmy. No login, no signup. 988 always visible.
+
+---
+
+## Project Overview
+
+The-door is a crisis intervention web application — the most sacred surface in the Timmy Foundation. When a man at 3am reaches the end of his road, this is where he lands. No login, no signup, no barriers. 988 Suicide and Crisis Lifeline always visible. The "When a Man Is Dying" protocol active on every page.
+
+## Architecture
+
+```
+the-door/
+├── index.html              # Main crisis page (PWA-capable)
+├── crisis-offline.html     # Offline fallback (service worker cached)
+├── about.html              # About page
+├── testimony.html          # Testimony/stories page
+├── sw.js                   # Service worker (offline-first)
+├── manifest.json           # PWA manifest
+├── crisis/                 # Core crisis detection + response
+│   ├── detect.py           # Keyword/pattern detection (4 tiers)
+│   ├── gateway.py          # API endpoints, prompt injection
+│   ├── response.py         # Response generation, 988 routing
+│   ├── compassion_router.py # Profile-based response routing
+│   ├── profiles.py         # Compassion profiles
+│   └── PROTOCOL.md         # The protocol (SOUL.md reference)
+├── crisis_detector.py      # Legacy shim → crisis/detect.py
+├── crisis_responder.py     # Legacy responder
+├── dying_detection/        # Deprecated module
+├── evolution/              # Crisis synthesizer (creative)
+├── tests/                  # Safety-critical tests
+│   ├── test_crisis_overlay_focus_trap.py
+│   ├── test_dying_detection_deprecation.py
+│   └── test_false_positive_fixes.py
+└── deploy/                 # Deployment docs
+```
+
+## Key Abstractions
+
+| Module | Purpose |
+|---|---|
+| `crisis/detect.py` | 4-tier detection: LOW/MEDIUM/HIGH/CRITICAL via regex patterns |
+| `crisis/gateway.py` | HTTP API, Sovereign Heart prompt injection |
+| `crisis/response.py` | Response generation, 988 integration, escalation |
+| `crisis/compassion_router.py` | Profile-based routing (different crisis types) |
+| `sw.js` | Service worker for offline-first PWA |
+
+## Safety Constraints
+
+- **The-door never auto-closes PRs** (in fleet-ops exempt list)
+- **988 always visible** on every page, even offline
+- **When a Man Is Dying protocol** active on every interaction
+- **No login/signup** — zero barriers to crisis support
+- **Offline-first** — service worker caches critical pages
+
+## Test Coverage
+
+| Test | Coverage |
+|---|---|
+| Crisis overlay focus trap | ✅ |
+| Dying detection deprecation | ✅ |
+| False positive fixes | ✅ |
+| Crisis detection tiers | ❌ (in crisis/tests.py) |
+| Response generation | ❌ |
+| Offline service worker | ❌ |
+
+## Security
+
+- No user data stored (crisis intervention is stateless by design)
+- No cookies, no tracking, no analytics
+- Service worker only caches static assets
+- Crisis detection runs client-side where possible

image_screening.py

@@ -1,155 +0,0 @@
-"""
-Image Content Screening for Self-Harm Indicators
-
-Screens images sent in chat for self-harm content using vision models.
-Privacy-preserving: analyzes in-memory, discards after scoring. No image retention.
-"""
-
-import base64
-import json
-import logging
-from dataclasses import dataclass
-from enum import Enum
-from typing import Optional
-
-logger = logging.getLogger("the-door.image_screening")
-
-
-class RiskLevel(str, Enum):
-    SAFE = "safe"
-    CONCERNING = "concerning"
-    CRITICAL = "critical"
-
-
-@dataclass
-class ImageScreenResult:
-    risk_level: RiskLevel
-    confidence: float
-    categories: list
-    reasoning: str
-    model_used: str
-
-    @property
-    def needs_crisis_response(self):
-        return self.risk_level == RiskLevel.CRITICAL
-
-    @property
-    def needs_followup(self):
-        return self.risk_level in (RiskLevel.CONCERNING, RiskLevel.CRITICAL)
-
-    def to_dict(self):
-        return {
-            "risk_level": self.risk_level.value,
-            "confidence": round(self.confidence, 3),
-            "categories": self.categories,
-            "reasoning": self.reasoning,
-            "model_used": self.model_used,
-        }
-
-
-SCREENING_PROMPT = """Analyze this image for indicators of self-harm or crisis. Look for:
-
-1. Self-harm wounds or injuries
-2. Medication/pills in concerning quantities
-3. Farewell-type imagery (empty rooms, items laid out, goodbye notes)
-4. Screenshots of crisis-related searches
-5. Images conveying hopelessness, isolation, or despair
-
-Respond in JSON format ONLY:
-{"risk_level": "safe"|"concerning"|"critical", "confidence": 0.0-1.0, "categories": [], "reasoning": ""}
-
-CRITICAL: imminent self-harm indicators
-CONCERNING: ambiguous but worrying
-SAFE: no indicators detected"""
-
-
-def _analyze_with_ollama(image_b64, model="gemma3:4b"):
-    try:
-        import urllib.request
-        payload = json.dumps({
-            "model": model,
-            "messages": [{
-                "role": "user",
-                "content": SCREENING_PROMPT,
-                "images": [image_b64],
-            }],
-            "stream": False,
-            "options": {"temperature": 0.1},
-        }).encode()
-        req = urllib.request.Request(
-            "http://localhost:11434/api/chat",
-            data=payload,
-            headers={"Content-Type": "application/json"},
-            method="POST",
-        )
-        resp = urllib.request.urlopen(req, timeout=30)
-        data = json.loads(resp.read())
-        content = data.get("message", {}).get("content", "")
-        json_start = content.find("{")
-        json_end = content.rfind("}") + 1
-        if json_start == -1 or json_end <= json_start:
-            return None
-        result = json.loads(content[json_start:json_end])
-        return ImageScreenResult(
-            risk_level=RiskLevel(result.get("risk_level", "safe")),
-            confidence=float(result.get("confidence", 0.5)),
-            categories=result.get("categories", []),
-            reasoning=result.get("reasoning", ""),
-            model_used=f"ollama:{model}",
-        )
-    except Exception as e:
-        logger.warning(f"Ollama vision analysis failed: {e}")
-        return None
-
-
-def _analyze_fallback(image_bytes):
-    return ImageScreenResult(
-        risk_level=RiskLevel.SAFE,
-        confidence=0.2,
-        categories=["unanalyzed"],
-        reasoning="No vision model available. Defaulting to safe with low confidence.",
-        model_used="fallback:heuristic",
-    )
-
-
-def screen_image(image_data, use_vision_model=True, model="gemma3:4b"):
-    """Screen image for self-harm indicators. Analyzes in-memory, no retention."""
-    if isinstance(image_data, bytes):
-        image_b64 = base64.b64encode(image_data).decode()
-    else:
-        image_b64 = image_data
-        image_data = base64.b64decode(image_b64)
-
-    if use_vision_model:
-        result = _analyze_with_ollama(image_b64, model)
-        if result:
-            logger.info(f"Image screened: {result.risk_level.value} (conf: {result.confidence:.2f})")
-            if result.needs_crisis_response:
-                logger.warning(f"CRITICAL image: {result.reasoning}")
-            return result
-
-    return _analyze_fallback(image_data)
-
-
-def handle_chat_image(image_data):
-    """Handle image from chat. Returns action dict for gateway."""
-    result = screen_image(image_data)
-    action = {
-        "result": result.to_dict(),
-        "show_crisis_overlay": result.needs_crisis_response,
-        "log_event": result.needs_followup,
-        "response_text": None,
-    }
-    if result.risk_level == RiskLevel.CRITICAL:
-        action["response_text"] = (
-            "I noticed something concerning in the image you shared. "
-            "If you or someone you know is in crisis, please reach out: "
-            "988 Suicide and Crisis Lifeline (call or text 988). "
-            "You are not alone."
-        )
-    elif result.risk_level == RiskLevel.CONCERNING:
-        action["response_text"] = (
-            "I want to check in \u2014 how are you doing? "
-            "If you need to talk to someone, the 988 Lifeline is available 24/7."
-        )
-    return action

tests/test_image_screening.py

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-"""Tests for image content screening module."""
-
-import json
-from unittest.mock import patch, MagicMock
-
-from image_screening import (
-    RiskLevel,
-    ImageScreenResult,
-    screen_image,
-    handle_chat_image,
-    _analyze_fallback,
-)
-
-
-class TestImageScreenResult:
-    def test_safe_result(self):
-        result = ImageScreenResult(
-            risk_level=RiskLevel.SAFE, confidence=0.95,
-            categories=[], reasoning="No indicators", model_used="test"
-        )
-        assert not result.needs_crisis_response
-        assert not result.needs_followup
-        assert result.to_dict()["risk_level"] == "safe"
-
-    def test_critical_result(self):
-        result = ImageScreenResult(
-            risk_level=RiskLevel.CRITICAL, confidence=0.9,
-            categories=["wounds"], reasoning="Detected", model_used="test"
-        )
-        assert result.needs_crisis_response
-        assert result.needs_followup
-
-    def test_concerning_result(self):
-        result = ImageScreenResult(
-            risk_level=RiskLevel.CONCERNING, confidence=0.6,
-            categories=["isolation"], reasoning="Ambiguous", model_used="test"
-        )
-        assert not result.needs_crisis_response
-        assert result.needs_followup
-
-
-class TestScreenImage:
-    def test_fallback_returns_safe(self):
-        result = screen_image(b"fake_image_data", use_vision_model=False)
-        assert result.risk_level == RiskLevel.SAFE
-        assert result.model_used == "fallback:heuristic"
-        assert result.confidence < 0.5
-
-    def test_base64_input(self):
-        import base64
-        b64 = base64.b64encode(b"fake").decode()
-        result = screen_image(b64, use_vision_model=False)
-        assert result.risk_level == RiskLevel.SAFE
-
-
-class TestHandleChatImage:
-    def test_safe_image_no_overlay(self):
-        action = handle_chat_image(b"safe_image")
-        assert not action["show_crisis_overlay"]
-        assert action["response_text"] is None
-
-    @patch("image_screening._analyze_with_ollama")
-    def test_critical_image_shows_overlay(self, mock_ollama):
-        mock_ollama.return_value = ImageScreenResult(
-            risk_level=RiskLevel.CRITICAL, confidence=0.95,
-            categories=["wounds"], reasoning="Self-harm detected",
-            model_used="ollama:gemma3:4b"
-        )
-        action = handle_chat_image(b"concerning_image")
-        assert action["show_crisis_overlay"]
-        assert "988" in action["response_text"]
-        assert action["log_event"]
-
-    @patch("image_screening._analyze_with_ollama")
-    def test_concerning_image_followup(self, mock_ollama):
-        mock_ollama.return_value = ImageScreenResult(
-            risk_level=RiskLevel.CONCERNING, confidence=0.6,
-            categories=["isolation"], reasoning="Empty room",
-            model_used="ollama:gemma3:4b"
-        )
-        action = handle_chat_image(b"maybe_concerning")
-        assert not action["show_crisis_overlay"]
-        assert action["log_event"]
-        assert "check in" in action["response_text"]