diff --git a/.claude/worktrees/objective-spence b/.claude/worktrees/objective-spence
deleted file mode 160000
index d3194de..0000000
--- a/.claude/worktrees/objective-spence
+++ /dev/null
@@ -1 +0,0 @@
-Subproject commit d3194de0b852752ccd70eb41bd743b2d52319a51
diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..14ddc3f
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,38 @@
+# Secrets — never copy into an image layer.  Keys are supplied at runtime via
+# Coolify's environment variables, or per-request from the web form.
+.env
+.env.*
+
+# Local virtualenvs and Python build noise.  Patterns are matched from the
+# build-context root, so nested matches need an explicit **/ prefix.
+venv/
+.venv/
+**/__pycache__/
+**/*.py[cod]
+**/*.egg-info/
+build/
+dist/
+
+# Version control and editor/tooling state
+.git/
+.gitignore
+.github/
+.claude/
+.idea/
+.vscode/
+
+# Runtime output — regenerated per audit, never part of the image
+output/
+reports/
+test_results/
+
+# Sample inputs and reference material.  Only needed for the CLI pipeline
+# (entry_points/run_pipeline.py), not for serving the app.  Drop these lines
+# if you want to run the CLI inside the container.
+test_files/
+semantic_checklist/
+docs/
+*.ipynb
+
+# Deployment docs — not needed inside the image
+DEPLOY.md
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..774f495
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,206 @@
+name: CI
+
+on:
+  pull_request:
+    branches: [main]
+
+# A new push to the same PR cancels the previous run.
+concurrency:
+  group: ci-${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  checks:
+    name: Dependencies, imports, pipeline
+    runs-on: ubuntu-latest
+    timeout-minutes: 10
+    steps:
+      - uses: actions/checkout@v7
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v9.0.0
+        with:
+          version: "0.11.21"
+          enable-cache: true
+
+      # Fails if pyproject.toml and uv.lock disagree — i.e. someone changed a
+      # dependency without re-running `uv lock`.
+      - name: uv.lock is up to date
+        run: uv lock --check
+
+      - name: Install locked dependencies
+        run: uv sync --frozen
+
+      # requirements.txt is a generated export, not a hand-maintained list.
+      # Regenerate it and fail if the committed copy differs.
+      - name: requirements.txt matches uv.lock
+        run: |
+          uv export --format requirements.txt --no-dev --no-emit-project \
+            --no-hashes -o requirements.txt
+          if ! git diff --exit-code --stat -- requirements.txt; then
+            echo "::error::requirements.txt is out of date. Regenerate it with the command in its header and commit the result."
+            exit 1
+          fi
+
+      - name: Entry points import
+        run: |
+          uv run python -c "
+          import entry_points.api_server, entry_points.run_pipeline, entry_points.generate_report
+          from vision_aid.ingestion.file_crawler import fetch_page, fetch_pages_nested
+          print('all entry points import cleanly')
+          "
+
+      # Exercises extractors, slicers, prompt templates and the programmatic
+      # checkers end to end. --dry-run makes no API calls, so this needs no key
+      # and costs nothing.
+      - name: Pipeline dry run
+        run: |
+          uv run python entry_points/run_pipeline.py \
+            --html test_files/dat_visionaid_home.html \
+            --dry-run --output-dir ./ci-output
+
+      - name: Pipeline produced expected output
+        run: |
+          uv run python - <<'PY'
+          import json, pathlib, sys
+
+          manifest = json.loads(pathlib.Path("ci-output/manifest.json").read_text())
+          prompts = list(pathlib.Path("ci-output/prompts").glob("*.json"))
+          findings = json.loads(
+              pathlib.Path("ci-output/programmatic_findings.json").read_text()
+          )
+
+          problems = []
+          if not manifest["dry_run"]:
+              problems.append("manifest says this was not a dry run")
+          if manifest["total_input_tokens"] or manifest["total_output_tokens"]:
+              problems.append("a dry run consumed tokens — an API call escaped")
+          if not manifest["prompts_dry_run"]:
+              problems.append("no prompts were generated")
+          if not findings:
+              problems.append("programmatic checkers found nothing")
+          if len(prompts) != len(manifest["prompts_dry_run"]):
+              problems.append(
+                  f"{len(prompts)} prompt files vs "
+                  f"{len(manifest['prompts_dry_run'])} in manifest"
+              )
+
+          if problems:
+              for p in problems:
+                  print(f"::error::{p}")
+              sys.exit(1)
+
+          print(
+              f"OK — {len(manifest['prompts_dry_run'])} prompts generated, "
+              f"{len(manifest['prompts_skipped'])} skipped, "
+              f"{len(findings)} programmatic findings"
+          )
+          PY
+
+      # index.html carries all of the front-end logic inline, so a syntax error
+      # there ships a broken page with nothing else to catch it.
+      - name: index.html JavaScript parses
+        run: |
+          python3 - <<'PY'
+          import pathlib, re
+          html = pathlib.Path("index.html").read_text(encoding="utf-8", errors="replace")
+          blocks = re.findall(r"<script[^>]*>(.*?)</script>", html, re.DOTALL)
+          assert blocks, "no <script> blocks found in index.html"
+          pathlib.Path("/tmp/inline.js").write_text("\n".join(blocks), encoding="utf-8")
+          print(f"extracted {len(blocks)} script block(s)")
+          PY
+          node --check /tmp/inline.js
+
+  docker:
+    name: Image builds and serves
+    runs-on: ubuntu-latest
+    timeout-minutes: 15
+    steps:
+      - uses: actions/checkout@v7
+
+      - name: Build image
+        run: docker build -t va-dat:ci .
+
+      # No API key is provided, so the audit runs in dry-run mode: programmatic
+      # checks only, no LLM calls, nothing billable.
+      - name: Start container
+        run: |
+          docker run -d --name va-dat-ci -p 8000:8000 \
+            --tmpfs /tmp:size=512m --read-only va-dat:ci
+
+      - name: Wait for healthcheck
+        run: |
+          for i in $(seq 1 30); do
+            status=$(docker inspect -f '{{.State.Health.Status}}' va-dat-ci)
+            echo "attempt $i: $status"
+            [ "$status" = "healthy" ] && exit 0
+            [ "$status" = "unhealthy" ] && break
+            sleep 2
+          done
+          echo "::error::container never became healthy"
+          docker logs va-dat-ci
+          exit 1
+
+      - name: Serves the site
+        run: |
+          code=$(curl -s -o /dev/null -w '%{http_code}' http://localhost:8000/)
+          [ "$code" = "200" ] || { echo "::error::GET / returned $code"; exit 1; }
+          code=$(curl -s -o /dev/null -w '%{http_code}' http://localhost:8000/styles.css)
+          [ "$code" = "200" ] || { echo "::error::GET /styles.css returned $code"; exit 1; }
+
+      - name: Audit endpoint returns a usable NDJSON stream
+        run: |
+          curl -s -D headers.txt -X POST http://localhost:8000/api/audit \
+            -H 'Content-Type: application/json' \
+            -d '{"html_content":"<html><head><title>CI</title></head><body><img src=x><a href=#>click here</a></body></html>"}' \
+            -o body.ndjson
+
+          grep -qi 'application/x-ndjson' headers.txt || {
+            echo "::error::expected an NDJSON content type"; cat headers.txt; exit 1; }
+
+          python3 - <<'PY'
+          import json, pathlib, sys
+
+          lines = [
+              json.loads(l)
+              for l in pathlib.Path("body.ndjson").read_text().splitlines()
+              if l.strip()
+          ]
+          results = [l for l in lines if l.get("type") == "result"]
+          progress = [l for l in lines if l.get("type") == "progress"]
+
+          problems = []
+          if len(results) != 1:
+              problems.append(f"expected exactly 1 result event, got {len(results)}")
+          if not progress:
+              problems.append("no progress events — the stream is not streaming")
+          if results:
+              r = results[0]
+              if not r.get("success"):
+                  problems.append(f"audit failed: {r.get('error')}")
+              if not r["summary"]["dry_run"]:
+                  problems.append("ran with an API key — CI must never bill an account")
+              if not r.get("programmatic_findings"):
+                  problems.append("no programmatic findings returned")
+
+          if problems:
+              for p in problems:
+                  print(f"::error::{p}")
+              sys.exit(1)
+
+          print(
+              f"OK — {len(progress)} progress events, "
+              f"{len(results[0]['programmatic_findings'])} findings"
+          )
+          PY
+
+      - name: Container logs
+        if: always()
+        run: docker logs va-dat-ci || true
+
+      - name: Stop container
+        if: always()
+        run: docker rm -f va-dat-ci || true
diff --git a/.github/workflows/production.yml b/.github/workflows/production.yml
deleted file mode 100644
index 84684dd..0000000
--- a/.github/workflows/production.yml
+++ /dev/null
@@ -1,36 +0,0 @@
-name: Vercel Production Deployment
-env:
-  VERCEL_ORG_ID: ${{ secrets.VERCEL_ORG_ID }}
-  VERCEL_PROJECT_ID: ${{ secrets.VERCEL_PROJECT_ID }}
-on:
-  push:
-    branches:
-      - main
-jobs:
-  Deploy-Production:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-
-      - name: Install uv (Manual)
-        run: |
-            curl -LsSf https://astral.sh/uv/install.sh | sh
-            echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Generate requirements.txt
-        run: |
-                # This pulls the dependencies out of pyproject.toml without needing poetry installed
-                grep -E '^[^#].*=' pyproject.toml | sed -E 's/ *=.*//' > requirements.txt
-
-      - name: Install Vercel CLI
-        run: npm install --global vercel@latest
-
-      - name: Pull Vercel Environment Information
-        run: vercel pull --yes --environment=production --token=${{ secrets.VERCEL_TOKEN }}
-
-      - name: Build Project Artifacts
-        run: vercel build --prod --token=${{ secrets.VERCEL_TOKEN }}
-
-      - name: Deploy Project Artifacts to Vercel
-        run: vercel deploy --prebuilt --prod --token=${{ secrets.VERCEL_TOKEN }}
\ No newline at end of file
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000..5e1f52f
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,123 @@
+name: Publish Image
+
+# Builds the application image and pushes it to GHCR so Coolify pulls a
+# finished image instead of building on the deployment host, then triggers the
+# deployment once the push has completed.
+
+on:
+  push:
+    branches: [main]
+  workflow_dispatch:
+
+concurrency:
+  group: publish-${{ github.ref }}
+  cancel-in-progress: true
+
+env:
+  # Must be lowercase — GHCR rejects mixed-case image names, and the org is "C4G".
+  IMAGE_BASE: ghcr.io/c4g/va-dat
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
+
+    steps:
+      - uses: actions/checkout@v7
+
+      - uses: docker/setup-buildx-action@v4
+
+      - uses: docker/login-action@v4
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Build and push
+        uses: docker/build-push-action@v7
+        with:
+          context: .
+          push: true
+          tags: |
+            ${{ env.IMAGE_BASE }}:latest
+            ${{ env.IMAGE_BASE }}:${{ github.sha }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+
+      # Deploy only what has been shown to start and serve. CI runs on pull
+      # requests, so a merge commit itself is never exercised before this point.
+      # No API key is supplied, so the audit runs in dry-run mode and bills
+      # nothing.
+      - name: Smoke test the published image
+        run: |
+          docker run -d --name va-dat-smoke -p 8000:8000 \
+            --tmpfs /tmp:size=512m --read-only "${IMAGE_BASE}:${GITHUB_SHA}"
+
+          healthy=
+          for i in $(seq 1 30); do
+            status=$(docker inspect -f '{{.State.Health.Status}}' va-dat-smoke)
+            echo "attempt $i: $status"
+            if [ "$status" = "healthy" ]; then healthy=1; break; fi
+            if [ "$status" = "unhealthy" ]; then break; fi
+            sleep 2
+          done
+
+          if [ "$healthy" != "1" ]; then
+            echo "::error::published image never became healthy"
+            docker logs va-dat-smoke
+            exit 1
+          fi
+
+          code=$(curl -s -o /dev/null -w '%{http_code}' http://localhost:8000/)
+          [ "$code" = "200" ] || { echo "::error::GET / returned $code"; exit 1; }
+
+          curl -s -X POST http://localhost:8000/api/audit \
+            -H 'Content-Type: application/json' \
+            -d '{"html_content":"<html><head><title>smoke</title></head><body><img src=x></body></html>"}' \
+            -o body.ndjson
+
+          python3 - <<'PY'
+          import json, pathlib, sys
+          lines = [json.loads(l) for l in
+                   pathlib.Path("body.ndjson").read_text().splitlines() if l.strip()]
+          results = [l for l in lines if l.get("type") == "result"]
+          if len(results) != 1 or not results[0].get("success"):
+              print("::error::published image did not return a successful audit")
+              sys.exit(1)
+          if not results[0]["summary"]["dry_run"]:
+              print("::error::image ran with an API key — it must not carry credentials")
+              sys.exit(1)
+          print(f"OK — {len(results[0]['programmatic_findings'])} findings")
+          PY
+
+      - name: Stop smoke test container
+        if: always()
+        run: docker rm -f va-dat-smoke || true
+
+      - name: Summary
+        run: |
+          {
+            echo "### Image published"
+            echo ''
+            echo '```'
+            echo "${IMAGE_BASE}:latest"
+            echo "${IMAGE_BASE}:${GITHUB_SHA}"
+            echo '```'
+          } >> "$GITHUB_STEP_SUMMARY"
+
+      # Deploying from here (rather than letting Coolify redeploy on git push)
+      # avoids a race where Coolify pulls `:latest` before this workflow has
+      # finished pushing it. Skipped automatically until the secret exists.
+      # The secret is surfaced through `env` because the `secrets` context is
+      # not available in a step-level `if:`.
+      - name: Trigger Coolify deployment
+        env:
+          COOLIFY_TOKEN: ${{ secrets.COOLIFY_TOKEN }}
+          COOLIFY_APP_UUID: ${{ vars.COOLIFY_APP_UUID }}
+        if: ${{ env.COOLIFY_TOKEN != '' && env.COOLIFY_APP_UUID != '' }}
+        run: |
+          curl -fsS -X GET \
+            "https://coolify.c4g.dev/api/v1/deploy?uuid=${COOLIFY_APP_UUID}" \
+            -H "Authorization: Bearer ${COOLIFY_TOKEN}"
diff --git a/.gitignore b/.gitignore
index da2969c..4f3a6fb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -208,3 +208,9 @@ __marimo__/
 
 # Pipeline output
 output/
+
+# CI smoke-test output
+ci-output/
+
+# Stray git worktrees — never belong in the index
+.claude/worktrees/
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..e4fba21
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.12
diff --git a/CLAUDE.md b/CLAUDE.md
index ecf9ef7..4301431 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -13,83 +13,106 @@ The system has three planned stages:
 
 ```
 project-root/
-├── index.html                       # Team/project website (served via GitHub Pages)
-├── styles.css                       # Website styles
+├── index.html                       # Web UI + team site (served by api_server.py)
+├── styles.css
+├── entry_points/
+│   ├── api_server.py                # HTTP server: static site + /api/* audit endpoints
+│   ├── run_pipeline.py              # CLI pipeline orchestrator
+│   └── generate_report.py           # Pipeline output → unified CSV
 ├── processing_scripts/
 │   ├── llm/                         # Modular prompt system
-│   │   ├── registry.py              # PromptSpec dataclass + PROMPT_REGISTRY (21 specs)
-│   │   ├── slicers.py               # Payload slicer functions (one per prompt)
-│   │   ├── templates.py             # Prompt .txt parser + {payload} filler
-│   │   ├── semantic_checklist_01.txt # 7 prompts for semantic structure (CL01)
-│   │   ├── forms_checklist_02.txt   # 6 prompts for forms (CL02)
-│   │   └── nontext_checklist_03.txt # 8 prompts for non-text content (CL03)
+│   │   ├── registry.py              #   PromptSpec dataclass + PROMPT_REGISTRY
+│   │   ├── slicers.py               #   Payload slicer functions (one per prompt)
+│   │   ├── filters.py               #   Pass-1 payload filters
+│   │   ├── templates.py             #   Prompt .txt parser + {payload} filler
+│   │   └── *_checklist_0*.txt       #   Prompt templates (CL01/CL02/CL03)
 │   ├── llm_preprocessing/           # HTML → structured JSON extractors
-│   │   ├── semantic_checklist_01.py  # CL01 extractor
-│   │   ├── forms_checklist_02.py    # CL02 extractor
-│   │   └── nontext_checklist_03.py  # CL03 extractor
-│   └── programmatic/                # Rule-based checks (no API needed)
-│       └── semantic_checklist_01.py  # Missing alt, duplicate IDs, etc.
-├── entry_points/
-│   ├── run_pipeline.py              # Full pipeline orchestrator
-│   ├── generate_report.py           # JSON → CSV report generator
-│   └── get_visionaid_home.py        # HTML downloader
-├── test_files/                      # HTML files to analyze (may be very large, 500K+ tokens)
-└── docs/
-    └── modular-prompts-plan.md      # Architecture plan
+│   ├── programmatic/                # Rule-based checks (no API needed)
+│   └── llm_client/                  # Provider client (Anthropic/OpenAI/Gemini)
+├── vision_aid/ingestion/
+│   ├── file_crawler.py              # fetch_page / fetch_pages_nested (used by the server)
+│   └── pull_html.py                 # Standalone HTML download helper
+├── Dockerfile                       # Multi-stage uv build → runtime image
+├── docker-compose.yml               # Local run; Coolify deploys the image directly
+├── DEPLOY.md                        # Coolify deployment notes (proxy settings matter)
+├── .github/workflows/
+│   ├── ci.yml                       # On PR to main: deps, imports, pipeline, image smoke
+│   └── publish.yml                  # On push to main: build → GHCR → trigger Coolify
+├── test_files/                      # HTML inputs (may be very large, 500K+ tokens)
+└── docs/modular-prompts-plan.md
 ```
 
-## Website (GitHub Pages)
+## Environment & Dependencies
 
-The team project website lives at the repo root (`index.html` + `styles.css`). It is a pure HTML5/CSS3 static site with no build tools or JavaScript frameworks — served directly via GitHub Pages from the main branch root.
+This project uses **uv**. There is no `pip install` step and no hand-managed `venv`.
 
-- Design: DM Serif Display + DM Sans, teal/amber palette, fully responsive
-- Accessibility: skip link, semantic HTML, ARIA landmarks, WCAG AA contrast
-- Lighthouse scores (last measured): Performance 92 · Accessibility 96 · Best Practices 100 · SEO 100
-- Do not add JavaScript or external CSS frameworks to the website
+```bash
+uv sync                    # create .venv and install from uv.lock
+uv run python <script>     # run inside that environment
+```
 
-## Tech Stack
+- `pyproject.toml` is the **only** hand-edited dependency list.
+- `uv.lock` is committed and pins everything, including transitive deps.
+- `requirements.txt` is a **generated export** — never edit it by hand. Regenerate with the command in its header. CI fails if it drifts from the lock.
+- `.python-version` pins 3.12 to match the container. `requires-python` is `>=3.11`.
 
-- Python 3.x (use virtual environment: `source venv/bin/activate`)
-- Anthropic API (Claude Sonnet 4 preferred for testing — `claude-sonnet-4-20250514`)
-- HTML preprocessing: BeautifulSoup / custom parsers (check imports in preprocessing files)
-- Accessibility tools context: axe-core, Lighthouse, WAVE (hybrid approach)
-- Output format: CSV with columns — ID, element_name, browser_combination, page_title, issue_title, steps_to_reproduce, actual_result, expected_result, recommendation, wcag_sc, category, log_date, reported_by
+After changing a dependency: edit `pyproject.toml`, run `uv lock`, regenerate `requirements.txt`, and commit all three.
 
 ## Pipeline Usage
 
 ```bash
-# Dry run (no API key needed) — generates prompts and payloads
-python entry_points/run_pipeline.py --html test_files/home.html --dry-run
+# Dry run (no API key needed, no cost) — generates prompts and payloads
+uv run python entry_points/run_pipeline.py --html test_files/dat_visionaid_home.html --dry-run
+
+# Full run (spends money — see the rules below)
+uv run python entry_points/run_pipeline.py --html test_files/home.html
 
-# Full run with API
-python entry_points/run_pipeline.py --html test_files/home.html
+# CSV report from pipeline output
+uv run python entry_points/generate_report.py
 
-# Generate CSV report from pipeline output
-python entry_points/generate_report.py
+# Web UI + API at http://localhost:8000
+uv run python entry_points/api_server.py
 ```
 
+## The Web Server
+
+`entry_points/api_server.py` is the single server — it serves `index.html`/`styles.css` and handles the audit endpoints. There is deliberately no second copy of the audit logic; the project previously carried two and they drifted until one silently skipped LLM deduplication.
+
+The audit endpoints (`/api/audit`, `/api/audit/url`, `/api/audit/url/nested`) return an **NDJSON stream**: progress events one JSON object per line, then a final `{"type":"result"}`. Parsing that body with a single `JSON.parse`/`res.json()` throws "Unexpected non-whitespace character after JSON" — the front end branches on content type to handle it.
+
+## Deployment
+
+Docker image → GHCR (`ghcr.io/c4g/va-dat`) → Coolify at `https://va-dat.c4g.dev`.
+`publish.yml` builds and pushes on merge to `main`, then triggers the Coolify deploy. Coolify runs the **image**, not `docker-compose.yml` — the hardening in that compose file (`read_only`, `tmpfs`) applies to local runs only. See `DEPLOY.md`, especially the proxy buffering/timeout section: buffering breaks the progress stream, and short read timeouts cut off long audits.
+
 ## Code Patterns
 
 - Prompt templates are `.txt` files with `{payload}` placeholders, parsed by `processing_scripts/llm/templates.py`
-- The registry (`processing_scripts/llm/registry.py`) maps each evaluation task to its prompt file, slicer, and WCAG criteria
-- Slicer functions (`processing_scripts/llm/slicers.py`) extract targeted JSON slices from extractor payloads
-- The pipeline runner (`entry_points/run_pipeline.py`) orchestrates: programmatic checks → extraction → slicing → prompt filling → API calls
+- `processing_scripts/llm/registry.py` maps each evaluation task to its prompt file, slicer, and WCAG criteria
+- Slicer functions in `processing_scripts/llm/slicers.py` extract targeted JSON slices from extractor payloads
+- `entry_points/run_pipeline.py` orchestrates: programmatic checks → extraction → slicing → prompt filling → API calls
 - File encoding: use `encoding='utf-8', errors='replace'` when reading HTML files
 
 ## Style Rules
 
-- Python code: standard PEP 8
+- Python: standard PEP 8
 - Docstrings for all new functions
 - Use `pathlib.Path` for file paths (consistent with existing scripts)
 - Commit messages: descriptive, prefixed with area (e.g., "prompts: add element-specific template system")
+- `index.html` contains the entire front end inline (HTML + CSS + JS). Keep it dependency-free — no frameworks, no external CDN assets.
 
 ## Do NOT
 
-- Do not call the Anthropic API or spend any money unless explicitly testing
-- Do not install new dependencies without team agreement
-- Do not modify test_files/ or any HTML input files
+- **Do not call any LLM API or spend money unless explicitly asked to test.** Prefer `--dry-run`.
+- Do not install or remove dependencies without team agreement.
+- Do not modify `test_files/` or any HTML input files.
+- Do not edit `requirements.txt` by hand — regenerate it.
+- Do not add `ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY:-}` (or similar) to `docker-compose.yml`. Compose auto-loads `./.env` for `${...}` interpolation, so that line silently injects a developer's personal key into the container and bills it for every anonymous audit.
 
 ## Common Issues
 
-- HTML test files can be enormous (500K+ tokens) — do not try to read them fully into context
-- Some files may have non-UTF-8 encoding; always use `errors='replace'`
+- **A dry run can still spend money.** `dry_run` is derived from whether a key resolves, and `_resolve_api_key` falls back to the environment — and `api_server.py` calls `load_dotenv()`. Sending `api_key: ""` from a client does **not** force a dry run when `.env` exists. To guarantee no spend, run with no key available at all.
+- **Silent dry runs.** A request with no resolvable key returns `200 OK` with no LLM findings and no CSV. The only signal is `summary.dry_run` in the response.
+- HTML test files can be enormous (500K+ tokens) — do not read them fully into context.
+- Some files may have non-UTF-8 encoding; always use `errors='replace'`.
+- Model defaults differ by entry point: `run_pipeline.py` uses `claude-sonnet-4-20250514`, `api_server.py` uses `claude-haiku-4-5-20251001`.
diff --git a/DEPLOY.md b/DEPLOY.md
new file mode 100644
index 0000000..ee300fb
--- /dev/null
+++ b/DEPLOY.md
@@ -0,0 +1,108 @@
+# Deploying to Coolify
+
+The app is a single long-running container serving both the website
+(`index.html`, `styles.css`) and the audit API from
+`entry_points/api_server.py`.
+
+## Quick start
+
+```bash
+docker compose up --build                    # http://localhost:8000
+HOST_PORT=8789 docker compose up --build     # if 8000 is already in use
+```
+
+In Coolify: create a **Docker Compose** resource pointed at this repo, set the
+environment variables below, attach a domain, and deploy.
+
+## Environment variables
+
+| Variable | Required | Notes |
+|---|---|---|
+| `HOST` | No | Set to `0.0.0.0` by the image; do not override |
+| `PORT` | No | Defaults to 8000; Coolify may inject its own |
+| `HOST_PORT` | No | Local-only: host port for `docker compose up` (default 8000) |
+
+**No provider API keys are configured, deliberately.** Each user pastes their
+own key into the web form, so the deployment never bills a shared account.
+Per-request keys take priority over the environment
+(`api_server.py:_resolve_api_key`).
+
+Consequence worth knowing: **a request with no resolvable key silently runs a
+dry run** — programmatic checks only, no LLM findings, no CSV report, and a
+`200 OK` either way. The only signal is `summary.dry_run` in the response. A
+user who forgets to paste a key gets a partial-looking audit rather than an
+error.
+
+### The `.env` interpolation trap
+
+Do **not** add lines like this back into `docker-compose.yml`:
+
+```yaml
+environment:
+  ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY:-}     # DON'T
+```
+
+Compose automatically loads `./.env` from the project directory to resolve
+`${...}`. On any developer machine with a local `.env`, that line silently
+injects their personal key into the container, and every anonymous audit bills
+it. This was verified the hard way: with that line present, a container built
+from an image containing no `.env`, started from a shell exporting no key,
+still made live API calls.
+
+`.dockerignore` keeps `.env` out of the *image*; interpolation reads it from
+the *host* at run time. Different mechanism — `.dockerignore` cannot stop it.
+Coolify deployments are not affected (`.env` is gitignored, so it never reaches
+the clone), but local `docker compose up` is.
+
+## Proxy configuration — read this one
+
+`POST /api/audit`, `/api/audit/url`, and `/api/audit/url/nested` return an
+**NDJSON stream** that stays open for the whole audit, emitting progress events
+as it goes. Two default proxy behaviours will break this:
+
+1. **Response buffering.** A proxy that buffers will hold every progress event
+   until the audit finishes, so the browser shows a frozen progress bar and then
+   all results at once. Disable buffering for `/api/`.
+2. **Read timeouts.** A single-page audit runs 17+ sequential LLM calls and
+   commonly takes 1–3 minutes; a nested crawl audits every discovered page and
+   can run far longer. Anything under ~10 minutes risks cutting audits off
+   mid-run. Traefik's default is generous, but Coolify installs vary — verify.
+
+In Coolify, set these as Traefik labels on the service, or raise the equivalent
+values in the proxy settings:
+
+```yaml
+labels:
+  - traefik.http.routers.audit.middlewares=audit-buffering@docker
+  - traefik.http.middlewares.audit-buffering.buffering.maxResponseBodyBytes=0
+  - traefik.http.services.audit.loadbalancer.responseForwarding.flushInterval=100ms
+```
+
+`flushInterval` is the important one — it forces Traefik to flush each chunk
+through rather than accumulating it.
+
+If you front this with nginx instead:
+
+```nginx
+location /api/ {
+    proxy_buffering off;
+    proxy_read_timeout 600s;
+}
+```
+
+## Sizing
+
+Audits are I/O-bound on the LLM API, not CPU-bound. 512 MB RAM and 0.5 vCPU is
+enough for light use. The server is a stdlib `ThreadingHTTPServer`, so each
+concurrent audit holds a thread and its own temp directory — fine for a handful
+of simultaneous users, not for public high traffic. The `tmpfs` mount is capped
+at 512 MB; a large nested crawl of very large pages could approach that, so
+raise it if you see failures writing temp files.
+
+## Serving
+
+`entry_points/api_server.py` is the single server: it serves `index.html` and
+`styles.css` at `/`, and handles the audit endpoints under `/api/`. There is
+deliberately no second, serverless copy of the audit logic — the project
+previously carried two, and they drifted until one silently skipped the LLM
+deduplication pass. Keep the logic in one place.
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..555501e
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,67 @@
+# syntax=docker/dockerfile:1
+
+# Serves the team website and the accessibility audit API
+# (entry_points/api_server.py).  Built for a long-running container, which is
+# what makes the NDJSON progress stream viable — unlike a serverless function,
+# there is no execution-time cap to work around.
+
+# ── Build stage: resolve and install into /app/.venv ──────────────────────────
+FROM python:3.12-slim AS builder
+
+# uv installs from uv.lock, so the image gets exactly the versions committed to
+# the repo.  Pinned deliberately rather than tracking latest mid-semester.
+COPY --from=ghcr.io/astral-sh/uv:0.11.21 /uv /bin/uv
+
+ENV UV_COMPILE_BYTECODE=1 \
+    UV_LINK_MODE=copy \
+    UV_PYTHON=python3.12 \
+    UV_PYTHON_DOWNLOADS=never \
+    UV_NO_CACHE=1
+
+WORKDIR /app
+
+# Dependencies first, without the project itself, so editing source does not
+# invalidate this layer.  --frozen fails the build if uv.lock is out of date
+# with pyproject.toml, which keeps the two from silently diverging.
+COPY pyproject.toml uv.lock ./
+RUN uv sync --frozen --no-install-project --no-dev
+
+# Application code.  See .dockerignore — .env and venv/ are deliberately
+# excluded so no API key is ever baked into an image layer.
+COPY . .
+RUN uv sync --frozen --no-dev
+
+# ── Runtime stage: no uv, no build tooling ───────────────────────────────────
+FROM python:3.12-slim
+
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+# Single chowned COPY: the virtualenv lands in exactly one layer.  Copying it
+# after creating it, rather than chown -R'ing it in place, keeps the image from
+# carrying two copies.  The path must match the builder's so the venv's
+# absolute shebangs stay valid.
+RUN useradd --create-home --uid 10001 appuser
+WORKDIR /app
+COPY --from=builder --chown=appuser:appuser /app /app
+
+# Put the synced virtualenv first on PATH so `python` is the project's.
+ENV PATH="/app/.venv/bin:$PATH"
+
+# api_server.py reads both of these from the environment (see its main()).
+# Coolify injects its own PORT; HOST must be 0.0.0.0 to accept traffic from
+# outside the container.
+ENV HOST=0.0.0.0 \
+    PORT=8000
+
+EXPOSE 8000
+
+# Run unprivileged.  /tmp must stay writable: every audit runs inside a
+# tempfile.mkdtemp() directory that is removed when the request completes.
+USER appuser
+
+# GET / serves index.html; urlopen raises on any non-2xx, failing the check.
+HEALTHCHECK --interval=30s --timeout=5s --start-period=10s --retries=3 \
+    CMD ["python", "-c", "import os,urllib.request; urllib.request.urlopen('http://127.0.0.1:' + os.environ.get('PORT','8000') + '/', timeout=4)"]
+
+CMD ["python", "entry_points/api_server.py"]
diff --git a/README.md b/README.md
index c836b49..9e4598c 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,9 @@
-# visionaid-a11y-llm-audit
+# va-dat — Vision Aid Digital Accessibility Testing
 
 LLM-powered tools that analyze websites for WCAG accessibility issues and generate structured remediation reports. A Computing for Good course project at Georgia Tech (OMSCS) partnered with the Vision Aid Digital Accessibility Testing Team.
 
+There are two ways to use it: a **web app** (paste a URL or HTML, get findings and a CSV) and a **CLI pipeline** for batch or scripted runs. Both share the same analysis code.
+
 ## How It Works
 
 The pipeline takes a raw HTML file and produces targeted accessibility findings through four steps:
@@ -38,10 +40,13 @@ HTML file (e.g. 1.9 MB)
 ## Repository Structure
 
 ```
-├── entry_points/                                # Pipeline entry points
-│   ├── run_pipeline.py                          # Main entry point — runs the full pipeline
-│   ├── generate_report.py                       # Combines findings into unified CSV report
-│   └── get_visionaid_home.py                    # Downloads visionaid.org homepage
+├── index.html                                   # Web UI + team site (all markup/CSS/JS inline)
+├── styles.css
+│
+├── entry_points/                                # Entry points
+│   ├── api_server.py                            # Serves the site and the /api/* audit endpoints
+│   ├── run_pipeline.py                          # Runs the full pipeline from the CLI
+│   └── generate_report.py                       # Combines findings into unified CSV report
 │
 ├── processing_scripts/
 │   ├── llm/                                     # Modular prompt system + templates
@@ -67,28 +72,35 @@ HTML file (e.g. 1.9 MB)
 │   │   ├── forms_checklist_02.py                #   Form accessibility checks
 │   │   └── nontext_checklist_03.py              #   Non-text content checks
 │   │
-│   ├── docs/pipeline.md                         # Pipeline architecture documentation
-│   └── accessibility_audit_walkthrough.ipynb    # Andrew's end-to-end audit notebook
+│   └── docs/pipeline.md                         # Pipeline architecture documentation
+│
+├── vision_aid/ingestion/
+│   ├── file_crawler.py                         # fetch_page / fetch_pages_nested (used by the server)
+│   └── pull_html.py                            # Standalone HTML download helper
+│
+├── Dockerfile                                  # Multi-stage uv build → runtime image
+├── docker-compose.yml                          # Local run (Coolify deploys the image directly)
+├── DEPLOY.md                                   # Coolify deployment notes
+│
+├── .github/workflows/
+│   ├── ci.yml                                  # On PR to main: deps, imports, pipeline, image smoke test
+│   └── publish.yml                             # On push to main: build → GHCR → trigger Coolify
 │
-├── test_files/                                 # EXISTING — HTML files to analyze
+├── test_files/                                 # HTML files to analyze
 │   ├── home.html                               #   visionaid.org homepage (~1.9 MB)
 │   └── dat_visionaid_home.html                 #   Smaller trimmed variant (~143 KB)
 │
-├── semantic_checklist/                         # EXISTING — Source of truth: Deque WCAG checklist PDFs
+├── semantic_checklist/                         # Source of truth: Deque WCAG checklist PDFs
 │   ├── 01-semantic-checklist.pdf
 │   ├── 02-forms-checklist.pdf
 │   └── 03-nontext-checklist.pdf
 │
-├── vision_aid/ingestion/                       # EXISTING — HTML download utility
-│   └── pull_html.py
-│
 ├── pipeline_walkthrough.ipynb                  # Colab-compatible step-by-step pipeline notebook
 │
 ├── docs/                                       # Architecture documentation
 │   └── modular-prompts-plan.md                 #   Full architectural plan
 │
-├── reports/                                    # LLM audit result files
-│   └── audit_*.json                            #   Raw JSON output from llm_client runs
+├── reports/                                    # Raw JSON output from standalone llm_client runs
 │
 ├── test_results/
 │   ├── chatgpt/                                # Legacy ChatGPT testing results
@@ -167,16 +179,51 @@ The normalizer registry mirrors the prompt registry — one normalizer function
 
 ## Setup
 
-Requires Python 3.11+.
+This project uses [uv](https://docs.astral.sh/uv/). It installs the right
+Python version itself, so there is no separate Python install or `venv` step.
+
+```bash
+# Install uv once (see the uv docs for Windows/other options)
+curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Create the environment and install exactly the locked dependencies
+uv sync
+```
+
+Then either prefix commands with `uv run`, or activate the environment:
+
+```bash
+uv run python entry_points/run_pipeline.py --help
+# or
+source .venv/bin/activate        # Linux/macOS
+# or: .venv\Scripts\activate      # Windows
+```
+
+`uv.lock` pins every dependency, including transitive ones, so everyone gets
+an identical environment. To change a dependency, edit `pyproject.toml` and run
+`uv lock`, then regenerate the pip fallback below.
+
+<details>
+<summary>Using pip instead (graders, Colab, or no uv available)</summary>
+
+`requirements.txt` is a generated export of `uv.lock` — **do not edit it by
+hand**; regenerate it with the command in its header. Python 3.11+ required.
 
 ```bash
 python -m venv venv
 source venv/bin/activate        # Linux/macOS
 # or: venv\Scripts\activate     # Windows
 
-pip install -e .
+pip install -r requirements.txt
+pip install -e . --no-deps
 ```
 
+If `python -m venv` fails with an `ensurepip` error, your Python is missing its
+venv module (`sudo apt install python3-venv` on Debian/Ubuntu). uv avoids this
+problem entirely.
+
+</details>
+
 Create a `.env` file with your provider API key(s) (only needed for live runs, not dry-run):
 
 ```
@@ -185,6 +232,54 @@ OPENAI_API_KEY=sk-...
 GEMINI_API_KEY=AIza...
 ```
 
+`.env` is gitignored. Never commit a key.
+
+> **A run with no resolvable key silently becomes a dry run** — programmatic
+> checks only, no LLM findings, no CSV, and still a `200 OK` from the web app.
+> The only signal is `summary.dry_run` in the response.
+
+## Running the Web App
+
+```bash
+uv run python entry_points/api_server.py      # http://localhost:8000
+```
+
+Serves the UI and the audit API from one process. Users can paste their own API
+key into the form instead of configuring one server-side; per-request keys take
+priority over the environment.
+
+The audit endpoints stream **NDJSON** — progress events, one JSON object per
+line, then a final `{"type":"result"}` object. Parsing that body with a single
+`res.json()` fails; the front end branches on content type.
+
+To run it in a container:
+
+```bash
+docker compose up --build                     # http://localhost:8000
+HOST_PORT=8789 docker compose up --build      # if 8000 is taken
+```
+
+## Deployment
+
+Merges to `main` build the image, push it to `ghcr.io/c4g/va-dat`, and trigger a
+Coolify deploy to <https://va-dat.c4g.dev>. See [DEPLOY.md](DEPLOY.md) — in
+particular the proxy settings, since response buffering breaks the progress
+stream and short read timeouts cut off long audits.
+
+Note that Coolify runs the **image**; the hardening in `docker-compose.yml`
+(`read_only`, `tmpfs`) applies to local runs only.
+
+## Continuous Integration
+
+`.github/workflows/ci.yml` runs on every PR to `main` and needs no API key —
+everything it does is free:
+
+- `uv.lock` is in sync with `pyproject.toml`, and `requirements.txt` matches the lock
+- entry points import
+- a full pipeline dry run, asserting prompts generated, findings found, and zero tokens consumed
+- `index.html`'s inline JavaScript parses
+- the Docker image builds, becomes healthy, serves the site, and returns a valid NDJSON audit
+
 ## Running the Pipeline
 
 ### Dry run (no API calls, no cost)
@@ -192,7 +287,7 @@ GEMINI_API_KEY=AIza...
 Generates all prompts and saves them as JSON files so you can inspect them before spending money:
 
 ```bash
-python entry_points/run_pipeline.py --html test_files/dat_visionaid_home.html --dry-run
+uv run python entry_points/run_pipeline.py --html test_files/dat_visionaid_home.html --dry-run
 ```
 
 ### Live run
@@ -200,7 +295,7 @@ python entry_points/run_pipeline.py --html test_files/dat_visionaid_home.html --
 Sends prompts to the LLM and saves responses:
 
 ```bash
-python entry_points/run_pipeline.py --html test_files/home.html
+uv run python entry_points/run_pipeline.py --html test_files/home.html
 ```
 
 ### Generate report
@@ -208,8 +303,8 @@ python entry_points/run_pipeline.py --html test_files/home.html
 After a live run, combine all findings into a single CSV:
 
 ```bash
-python entry_points/generate_report.py
-python entry_points/generate_report.py --output-dir ./output --report-dir ./test_results/claude/
+uv run python entry_points/generate_report.py
+uv run python entry_points/generate_report.py --output-dir ./output --report-dir ./test_results/claude/
 ```
 
 ### Pipeline options
diff --git a/api/index.py b/api/index.py
deleted file mode 100644
index 1a51e62..0000000
--- a/api/index.py
+++ /dev/null
@@ -1,392 +0,0 @@
-import json
-import os
-import re
-import shutil
-import sys
-import tempfile
-from pathlib import Path
-from fastapi import FastAPI, Request, HTTPException
-from fastapi.responses import StreamingResponse
-from fastapi.middleware.cors import CORSMiddleware
-
-# ── Project root and sys.path ─────────────────────────────────────────────────
-PROJECT_ROOT = Path(__file__).resolve().parent.parent
-sys.path.insert(0, str(PROJECT_ROOT))
-
-# Load Environment / Imports
-from entry_points.run_pipeline import run_pipeline
-from entry_points.generate_report import generate_report
-from vision_aid.ingestion.file_crawler import fetch_page, fetch_pages_nested
-from processing_scripts.llm_client.client import is_openai_model, is_gemini_model
-
-_PAGE_MARKER = re.compile(r"<!--\s*PAGE:\s*(.*?)\s*-->")
-
-app = FastAPI()
-
-# Automatic CORS handling
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# --- REUSE YOUR EXISTING LOGIC FUNCTIONS ---
-# Copy-paste split_pages, _resolve_api_key, run_audit, and _try_parse_json
-# EXACTLY as they are in your current script.
-
-def _resolve_api_key(data: dict, model: str) -> str:
-    """Return the appropriate API key for *model* from the request body or env.
-
-    OpenAI models use the ``openai_api_key`` field / ``OPENAI_API_KEY`` env.
-    Gemini models use the ``gemini_api_key`` field / ``GEMINI_API_KEY`` env.
-    Anthropic models use the ``api_key`` field / ``ANTHROPIC_API_KEY`` env.
-    Per-request keys take priority over environment variables.
-    """
-    if is_openai_model(model):
-        return (
-            data.get("openai_api_key", "").strip()
-            or os.getenv("OPENAI_API_KEY", "")
-        )
-    if is_gemini_model(model):
-        return (
-            data.get("gemini_api_key", "").strip()
-            or os.getenv("GEMINI_API_KEY", "")
-        )
-    return (
-        data.get("api_key", "").strip()
-        or os.getenv("ANTHROPIC_API_KEY", "")
-    )
-
-def run_audit(html_content: str, api_key: str, model: str, progress_callback=None) -> dict:
-    """Write *html_content* to a temp file, run the pipeline, return results.
-
-    If *api_key* is empty, the pipeline runs in dry-run mode (programmatic
-    checks only, no LLM calls).
-    """
-    dry_run = not api_key
-    # Vercel allows writing to /tmp for ephemeral storage
-    tmp_dir = tempfile.mkdtemp(prefix="visionaid_audit_", dir="/tmp")
-    try:
-        html_path = Path(tmp_dir) / "input.html"
-        html_path.write_text(html_content, encoding="utf-8")
-
-
-        # 2. DEFINE AND CREATE THE OUTPUT DIRECTORY
-        output_dir = Path(tmp_dir) / "output"
-        output_dir.mkdir(parents=True, exist_ok=True)
-
-        manifest = run_pipeline(
-            html_path=str(html_path),
-            output_dir=output_dir,
-            api_key=api_key if api_key else None,
-            model=model,
-            dry_run=dry_run,
-            include_summaries=False,
-            progress_callback=progress_callback,
-        )
-
-        # Read programmatic findings
-        prog_path = output_dir / "programmatic_findings.json"
-        programmatic_findings = (
-            json.loads(prog_path.read_text(encoding="utf-8"))
-            if prog_path.exists()
-            else []
-        )
-
-        # Read per-prompt LLM results
-        llm_results = {}
-        prompts_dir = output_dir / "prompts"
-        if prompts_dir.exists():
-            for prompt_file in sorted(prompts_dir.glob("*.json")):
-                data = json.loads(prompt_file.read_text(encoding="utf-8"))
-                name = data.get("prompt_name", prompt_file.stem)
-                # ``api_result`` is only absent when the pipeline actually ran
-                # in dry-run mode (no key). If it's present but unsuccessful,
-                # a real API call was attempted and failed — that's an error,
-                # not a dry run, and must not be silently treated as one.
-                api_result = data.get("api_result")
-                parsed = None
-                if api_result is None:
-                    status = "dry_run"
-                elif api_result.get("success"):
-                    status = "success"
-                    parsed = _try_parse_json(api_result.get("response", ""))
-                else:
-                    status = "error"
-                usage = (api_result or {}).get("usage", {})
-                llm_results[name] = {
-                    "checklist": data.get("checklist"),
-                    "wcag_criteria": data.get("wcag_criteria", []),
-                    "status": status,
-                    "error": (api_result or {}).get("error"),
-                    "parsed": parsed,
-                    "input_tokens": usage.get("input_tokens"),
-                    "output_tokens": usage.get("output_tokens"),
-                    "duration_seconds": (api_result or {}).get("duration_seconds"),
-                }
-
-        # Generate CSV report (only meaningful when LLM ran)
-        csv_content = None
-        if not dry_run:
-            try:
-                if progress_callback:
-                    progress_callback({
-                        "type": "progress",
-                        "stage": "report_generating",
-                        "message": "Generating report…",
-                    })
-                report_dir = Path(tmp_dir) / "reports"
-                report_path = generate_report(output_dir, report_dir)
-                csv_content = report_path.read_text(encoding="utf-8")
-                if progress_callback:
-                    progress_callback({
-                        "type": "progress",
-                        "stage": "report_complete",
-                        "message": "Report ready",
-                    })
-            except Exception as csv_err:
-                print(f"  Warning: CSV generation failed: {csv_err}")
-
-        return {
-            "success": True,
-            "programmatic_findings": programmatic_findings,
-            "llm_results": llm_results,
-            "csv_report": csv_content,
-            "skipped_prompts": manifest.get("prompts_skipped", []),
-            "summary": {
-                "programmatic_count": manifest.get("programmatic_findings_count", 0),
-                "programmatic_by_checker": manifest.get(
-                    "programmatic_findings_by_checker", {}
-                ),
-                "llm_prompts_run": len(llm_results),
-                "llm_prompts_skipped": len(manifest.get("prompts_skipped", [])),
-                "total_input_tokens": manifest.get("total_input_tokens", 0),
-                "total_output_tokens": manifest.get("total_output_tokens", 0),
-                "estimated_cost_usd": manifest.get("estimated_cost_usd"),
-                "model": model,
-                "dry_run": dry_run,
-            },
-        }
-
-    except Exception as exc:
-        return {"success": False, "error": str(exc)}
-
-    finally:
-        shutil.rmtree(tmp_dir, ignore_errors=True)
-
-
-
-def _try_parse_json(text: str):
-    """Parse JSON from an LLM response, stripping markdown code fences."""
-    m = re.search(r"```(?:json)?\s*\n?(.*?)```", text, re.DOTALL)
-    cleaned = m.group(1).strip() if m else text.strip()
-    try:
-        return json.loads(cleaned)
-    except (json.JSONDecodeError, ValueError):
-        return {"raw": text}
-
-def split_pages(html: str) -> list[tuple[str, str]]:
-    """Split concatenated HTML from ``fetch_pages_nested`` into per-page chunks.
-
-    Returns a list of ``(url, html)`` tuples.  If no PAGE markers are found
-    the entire string is returned as a single page with url ``"unknown"``.
-    """
-    markers = list(_PAGE_MARKER.finditer(html))
-    if not markers:
-        return [("unknown", html)]
-
-    pages: list[tuple[str, str]] = []
-    for i, m in enumerate(markers):
-        url = m.group(1)
-        start = m.end()
-        end = markers[i + 1].start() if i + 1 < len(markers) else len(html)
-        pages.append((url, html[start:end].strip()))
-    return pages
-
-
-
-# --- NEW FASTAPI ENDPOINTS ---
-
-@app.post("/api/audit")
-async def audit_html(request: Request):
-    """
-    Standard JSON response for Vercel compatibility.
-    Fixes the 'Unexpected character after JSON' error.
-    """
-    try:
-        data = await request.json()
-        html_content = data.get("html_content", "").strip()
-        model = data.get("model", "claude-haiku-4-5-20251001")
-        api_key = _resolve_api_key(data, model)
-
-        if not html_content:
-            return {"success": False, "error": "html_content is required"}
-
-        # run_audit handles the pipeline and returns a dictionary.
-        # We no longer need the lambda progress_callback because
-        # standard JSON responses are not streams.
-        result = run_audit(html_content, api_key, model)
-
-        # Ensure the result is marked as success/failure for the frontend
-        if "success" not in result:
-            result["success"] = True
-
-        return result
-
-    except Exception as e:
-        import traceback
-        return {
-            "success": False,
-            "error": f"Audit failed: {str(e)}",
-            "traceback": traceback.format_exc()
-        }
-
-@app.post("/api/audit/url")
-async def handle_url_audit(request: Request):
-    try:
-        data = await request.json()
-        url = data.get("url", "").strip()
-        if not url.startswith('http'): url = f"https://{url}"
-
-        # Log for debugging
-        print(f"Starting audit for: {url}")
-
-        html_content = fetch_page(url)
-        model = data.get("model", "claude-haiku-4-5-20251001")
-        api_key = _resolve_api_key(data, model)
-
-        # Pass a specific tmp directory to run_audit
-        result = run_audit(html_content, api_key, model)
-
-        return result
-
-    except Exception as e:
-        import traceback
-        # This will print the EXACT error (like "Read-only file system") to Vercel Logs
-        error_details = traceback.format_exc()
-        print(error_details)
-        return {
-            "success": False,
-            "error": str(e),
-            "traceback": error_details if not os.getenv("PROD") else None
-        }
-
-@app.post("/api/audit/url/nested")
-async def handle_nested_audit(request: Request):
-    """
-    Fetches multiple pages from a site (crawling), audits each page,
-    and returns a merged result.
-    """
-    try:
-        data = await request.json()
-        url = data.get("url", "").strip()
-        model = data.get("model", "claude-haiku-4-5-20251001")
-        api_key = _resolve_api_key(data, model)
-
-        if not url:
-            return {"success": False, "error": "URL is required"}
-
-        # Ensure protocol for the crawler
-        if not url.startswith(("http://", "https://")):
-            url = f"https://{url}"
-
-        # 1. Fetch all pages using the crawler
-        # This returns a large HTML string with markers and a crawl tree dict
-        full_html, crawl_tree = fetch_pages_nested(url)
-
-        # 2. Split the massive HTML into individual (url, html) tuples
-        pages = split_pages(full_html)
-
-        # 3. Initialize the merged response object
-        merged_results = {
-            "success": True,
-            "crawl_tree": crawl_tree,
-            "page_results": {},
-            "programmatic_findings": [],
-            "llm_results": {},
-            "pages_audited": [],
-            "summary": {
-                "programmatic_count": 0,
-                "llm_prompts_run": 0,
-                "model": model,
-                "pages": len(pages),
-                "dry_run": not api_key
-            }
-        }
-
-        # 4. Loop through each discovered page and run the audit
-        for page_url, page_html in pages:
-            # Re-use your existing run_audit helper
-            res = run_audit(page_html, api_key, model)
-
-            if not res.get("success"):
-                continue
-
-            merged_results["pages_audited"].append(page_url)
-
-            # Map results to the specific URL for the frontend tree view
-            merged_results["page_results"][page_url] = {
-                "programmatic_findings": res.get("programmatic_findings", []),
-                "llm_results": res.get("llm_results", {}),
-                "summary": res.get("summary", {})
-            }
-
-            # Flatten programmatic findings into the top-level list
-            for finding in res.get("programmatic_findings", []):
-                finding["page_url"] = page_url
-                merged_results["programmatic_findings"].append(finding)
-
-            # Update global counters
-            merged_results["summary"]["programmatic_count"] += res["summary"].get("programmatic_count", 0)
-            merged_results["summary"]["llm_prompts_run"] += res["summary"].get("llm_prompts_run", 0)
-
-        return merged_results
-
-    except Exception as e:
-        import traceback
-        return {
-            "success": False,
-            "error": f"Nested audit failed: {str(e)}",
-            "traceback": traceback.format_exc()
-        }
-
-@app.post("/api/validate-key")
-async def handle_validate_key(request: Request):
-    """
-    Validates the format of the provided API key.
-    Replaces the old self._handle_validate_key() logic.
-    """
-    try:
-        data = await request.json()
-        key = data.get("api_key", "").strip()
-        provider = data.get("provider", "anthropic")
-
-        if not key:
-            return {"valid": False, "error": "No key provided"}
-
-        # Basic format validation to prevent unnecessary API calls
-        if provider == "anthropic":
-            # Anthropic keys usually start with sk-ant-
-            is_valid = key.startswith("sk-ant") and len(key) > 20
-        elif provider == "openai":
-            # OpenAI keys usually start with sk-
-            is_valid = key.startswith("sk-") and len(key) > 20
-        elif provider == "gemini":
-            # Gemini key formats vary (e.g. "AIza...", "AQ...") — length is
-            # the only reliable signal without calling the API.
-            is_valid = len(key) > 20
-        else:
-            is_valid = len(key) > 10
-
-        if is_valid:
-            return {"valid": True}
-        else:
-            return {"valid": False, "error": f"Invalid {provider} key format"}
-
-    except Exception as e:
-        return {"valid": False, "error": f"Server error during validation: {str(e)}"}
-
-# --- STATIC FILE SERVING ---
-# Vercel serves index.html and styles.css automatically if they are in the root.
-# You don't need code for this anymore!
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..2e35e9a
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,56 @@
+# Deployment for Coolify (and plain `docker compose up` locally).
+#
+# Coolify supplies the domain, TLS and reverse proxy itself.  No API keys are
+# configured here or in Coolify: users supply their own in the web form.
+# See DEPLOY.md for the proxy timeout/buffering settings this app needs.
+
+services:
+  audit:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    image: visionaid-a11y-audit:latest
+    restart: unless-stopped
+
+    environment:
+      HOST: 0.0.0.0
+      PORT: 8000
+
+    # No provider API keys are passed in, by design: every user supplies their
+    # own key in the web form, so the deployment never bills a shared account.
+    #
+    # Do NOT "helpfully" add lines like `ANTHROPIC_API_KEY: ${ANTHROPIC_API_KEY:-}`.
+    # Compose auto-loads ./.env for ${...} interpolation, so on any developer
+    # machine that has a local .env, that line silently injects their personal
+    # key into the container and every anonymous audit bills it. .dockerignore
+    # keeps .env out of the *image*, but interpolation reads it from the *host*
+    # at run time — a different mechanism that .dockerignore cannot stop.
+    #
+    # If a server-side fallback key is ever genuinely wanted, set it in
+    # Coolify's Environment Variables UI rather than reintroducing that line.
+
+    # Coolify normally routes to the container through its own proxy and you
+    # can drop this mapping.  Keep it for local `docker compose up`; set
+    # HOST_PORT to avoid clashing with a dev server already on 8000.
+    ports:
+      - "${HOST_PORT:-8000}:8000"
+
+    healthcheck:
+      test:
+        - CMD
+        - python
+        - -c
+        - import os,urllib.request; urllib.request.urlopen('http://127.0.0.1:' + os.environ.get('PORT','8000') + '/', timeout=4)
+      interval: 30s
+      timeout: 5s
+      start_period: 10s
+      retries: 3
+
+    # Audits write only to per-request tempfile.mkdtemp() directories, so the
+    # root filesystem can stay read-only; /tmp below is the one writable mount.
+    read_only: true
+    tmpfs:
+      - /tmp:size=512m
+
+    security_opt:
+      - no-new-privileges:true
diff --git a/docs/modular-prompts-plan.md b/docs/modular-prompts-plan.md
index 22e0a41..3ff9b86 100644
--- a/docs/modular-prompts-plan.md
+++ b/docs/modular-prompts-plan.md
@@ -7,7 +7,7 @@
 The project has two parallel approaches to accessibility auditing, at different stages of completion:
 
 ```
-visionaid-a11y-llm-audit/
+va-dat/
 │
 ├── scripts/
 │   └── run_factorial_test.py          # Approach A: Monolithic (BROKEN — imports missing modules)
@@ -143,7 +143,7 @@ Output is a list of finding dicts, each with `issue_code`, `element` (tag/id/cla
 ### 2.2 Directory Structure
 
 ```
-visionaid-a11y-llm-audit/
+va-dat/
 ├── processing_scripts/
 │   ├── llm/                       # Modular prompt system + prompt text files
 │   │   ├── __init__.py
diff --git a/entry_points/vercel_handler.py b/entry_points/vercel_handler.py
deleted file mode 100644
index bf1b3d6..0000000
--- a/entry_points/vercel_handler.py
+++ /dev/null
@@ -1,547 +0,0 @@
-#!/usr/bin/env python3
-"""Serve the team website and run the accessibility audit API locally.
-
-This server has two responsibilities:
-  1. Serve static files (index.html, styles.css) from the project root.
-  2. Handle POST /api/audit — receive raw HTML, run the pipeline, return JSON.
-
-Usage:
-    python entry_points/api_server.py
-    python entry_points/api_server.py --port 8080
-
-Then open http://localhost:8000 in your browser.
-
-API keys can be supplied per-request in the POST body, or set in the .env
-file / environment.  Per-request keys take priority.
-
-  Anthropic models → field ``api_key``        / env ``ANTHROPIC_API_KEY``
-  OpenAI models    → field ``openai_api_key`` / env ``OPENAI_API_KEY``
-"""
-
-import json
-import os
-import re
-import shutil
-import sys
-import tempfile
-from http.server import BaseHTTPRequestHandler
-from pathlib import Path
-
-# ── Project root and sys.path ─────────────────────────────────────────────────
-PROJECT_ROOT = Path(__file__).resolve().parent.parent
-sys.path.insert(0, str(PROJECT_ROOT))
-
-# Vercel injects environment variables from the dashboard,
-# but load_dotenv is kept safe if used in a local wrapper.
-try:
-    from dotenv import load_dotenv
-except ImportError:
-    pass
-
-
-# Load .env if it exists (mostly for local testing, Vercel uses dashboard envs)
-if PROJECT_ROOT.joinpath(".env").exists():
-    try:
-        load_dotenv(PROJECT_ROOT / ".env")
-    except NameError:
-        pass
-
-from entry_points.run_pipeline import run_pipeline  # noqa: E402
-from entry_points.generate_report import generate_report  # noqa: E402
-from vision_aid.ingestion.file_crawler import fetch_page, fetch_pages_nested  # noqa: E402
-from processing_scripts.llm_client.client import is_openai_model, is_gemini_model  # noqa: E402
-
-
-# ── Multi-page splitting ─────────────────────────────────────────────────────
-
-_PAGE_MARKER = re.compile(r"<!--\s*PAGE:\s*(.*?)\s*-->")
-
-
-def split_pages(html: str) -> list[tuple[str, str]]:
-    """Split concatenated HTML from ``fetch_pages_nested`` into per-page chunks.
-
-    Returns a list of ``(url, html)`` tuples.  If no PAGE markers are found
-    the entire string is returned as a single page with url ``"unknown"``.
-    """
-    markers = list(_PAGE_MARKER.finditer(html))
-    if not markers:
-        return [("unknown", html)]
-
-    pages: list[tuple[str, str]] = []
-    for i, m in enumerate(markers):
-        url = m.group(1)
-        start = m.end()
-        end = markers[i + 1].start() if i + 1 < len(markers) else len(html)
-        pages.append((url, html[start:end].strip()))
-    return pages
-
-STATIC_DIR = PROJECT_ROOT  # index.html and styles.css live at the repo root
-
-
-# ── Key resolution ────────────────────────────────────────────────────────────
-
-def _resolve_api_key(data: dict, model: str) -> str:
-    """Return the appropriate API key for *model* from the request body or env.
-
-    OpenAI models use the ``openai_api_key`` field / ``OPENAI_API_KEY`` env.
-    Gemini models use the ``gemini_api_key`` field / ``GEMINI_API_KEY`` env.
-    Anthropic models use the ``api_key`` field / ``ANTHROPIC_API_KEY`` env.
-    Per-request keys take priority over environment variables.
-    """
-    if is_openai_model(model):
-        return (
-            data.get("openai_api_key", "").strip()
-            or os.getenv("OPENAI_API_KEY", "")
-        )
-    if is_gemini_model(model):
-        return (
-            data.get("gemini_api_key", "").strip()
-            or os.getenv("GEMINI_API_KEY", "")
-        )
-    return (
-        data.get("api_key", "").strip()
-        or os.getenv("ANTHROPIC_API_KEY", "")
-    )
-
-
-# ── Audit logic ───────────────────────────────────────────────────────────────
-
-def run_audit(html_content: str, api_key: str, model: str, progress_callback=None) -> dict:
-    """Write *html_content* to a temp file, run the pipeline, return results.
-
-    If *api_key* is empty, the pipeline runs in dry-run mode (programmatic
-    checks only, no LLM calls).
-    """
-    dry_run = not api_key
-    # Vercel allows writing to /tmp for ephemeral storage
-    tmp_dir = tempfile.mkdtemp(prefix="visionaid_", dir="/tmp")
-    try:
-        html_path = Path(tmp_dir) / "input.html"
-        html_path.write_text(html_content, encoding="utf-8")
-#         tmp_dir = tempfile.mkdtemp(prefix="visionaid_audit_", dir="/tmp")
-
-        # 2. DEFINE AND CREATE THE OUTPUT DIRECTORY
-        output_dir = Path(tmp_dir) / "output"
-        output_dir.mkdir(parents=True, exist_ok=True)
-
-        manifest = run_pipeline(
-            html_path=str(html_path),
-            output_dir=output_dir,
-            api_key=api_key if api_key else None,
-            model=model,
-            dry_run=dry_run,
-            include_summaries=False,
-            progress_callback=progress_callback,
-        )
-
-        # Read programmatic findings
-        prog_path = output_dir / "programmatic_findings.json"
-        programmatic_findings = (
-            json.loads(prog_path.read_text(encoding="utf-8"))
-            if prog_path.exists()
-            else []
-        )
-
-        # Read per-prompt LLM results
-        llm_results = {}
-        prompts_dir = output_dir / "prompts"
-        if prompts_dir.exists():
-            for prompt_file in sorted(prompts_dir.glob("*.json")):
-                data = json.loads(prompt_file.read_text(encoding="utf-8"))
-                name = data.get("prompt_name", prompt_file.stem)
-                # ``api_result`` is only absent when the pipeline actually ran
-                # in dry-run mode (no key). If it's present but unsuccessful,
-                # a real API call was attempted and failed — that's an error,
-                # not a dry run, and must not be silently treated as one.
-                api_result = data.get("api_result")
-                parsed = None
-                if api_result is None:
-                    status = "dry_run"
-                elif api_result.get("success"):
-                    status = "success"
-                    parsed = _try_parse_json(api_result.get("response", ""))
-                else:
-                    status = "error"
-                usage = (api_result or {}).get("usage", {})
-                llm_results[name] = {
-                    "checklist": data.get("checklist"),
-                    "wcag_criteria": data.get("wcag_criteria", []),
-                    "status": status,
-                    "error": (api_result or {}).get("error"),
-                    "parsed": parsed,
-                    "input_tokens": usage.get("input_tokens"),
-                    "output_tokens": usage.get("output_tokens"),
-                    "duration_seconds": (api_result or {}).get("duration_seconds"),
-                }
-
-        # Generate CSV report (only meaningful when LLM ran)
-        csv_content = None
-        if not dry_run:
-            try:
-                if progress_callback:
-                    progress_callback({
-                        "type": "progress",
-                        "stage": "report_generating",
-                        "message": "Generating report…",
-                    })
-                report_dir = Path(tmp_dir) / "reports"
-                report_path = generate_report(output_dir, report_dir)
-                csv_content = report_path.read_text(encoding="utf-8")
-                if progress_callback:
-                    progress_callback({
-                        "type": "progress",
-                        "stage": "report_complete",
-                        "message": "Report ready",
-                    })
-            except Exception as csv_err:
-                print(f"  Warning: CSV generation failed: {csv_err}")
-
-        return {
-            "success": True,
-            "programmatic_findings": programmatic_findings,
-            "llm_results": llm_results,
-            "csv_report": csv_content,
-            "skipped_prompts": manifest.get("prompts_skipped", []),
-            "summary": {
-                "programmatic_count": manifest.get("programmatic_findings_count", 0),
-                "programmatic_by_checker": manifest.get(
-                    "programmatic_findings_by_checker", {}
-                ),
-                "llm_prompts_run": len(llm_results),
-                "llm_prompts_skipped": len(manifest.get("prompts_skipped", [])),
-                "total_input_tokens": manifest.get("total_input_tokens", 0),
-                "total_output_tokens": manifest.get("total_output_tokens", 0),
-                "estimated_cost_usd": manifest.get("estimated_cost_usd"),
-                "model": model,
-                "dry_run": dry_run,
-            },
-        }
-
-    except Exception as exc:
-        return {"success": False, "error": str(exc)}
-
-    finally:
-        shutil.rmtree(tmp_dir, ignore_errors=True)
-
-
-def _try_parse_json(text: str):
-    """Parse JSON from an LLM response, stripping markdown code fences."""
-    m = re.search(r"```(?:json)?\s*\n?(.*?)```", text, re.DOTALL)
-    cleaned = m.group(1).strip() if m else text.strip()
-    try:
-        return json.loads(cleaned)
-    except (json.JSONDecodeError, ValueError):
-        return {"raw": text}
-
-
-# ── Vercel HTTP handler ───────────────────────────────────────────────────────
-
-class handler(BaseHTTPRequestHandler):
-    """Vercel Serverless Function entry point. Must be named 'handler'."""
-
-    def log_message(self, fmt, *args):  # noqa: N802
-        sys.stderr.write(f"[{self.address_string()}] {fmt % args}\n")
-
-    # CORS helpers ─────────────────────────────────────────────────────────────
-
-    def _cors_headers(self):
-            self.send_header("Access-Control-Allow-Origin", "*")
-            self.send_header("Access-Control-Allow-Methods", "GET, POST, OPTIONS")
-            self.send_header("Access-Control-Allow-Headers", "Content-Type")
-
-    def do_OPTIONS(self):
-            self.send_response(200)
-            self._cors_headers()
-            self.end_headers()
-
-    def do_GET(self):
-            path = self.path.split("?")[0]
-
-            if path in ("/", "/index.html"):
-                self._serve_file(PROJECT_ROOT / "index.html", "text/html; charset=utf-8")
-            elif path == "/styles.css":
-                self._serve_file(PROJECT_ROOT / "styles.css", "text/css; charset=utf-8")
-            elif path == "/favicon.ico":
-                self._serve_file(PROJECT_ROOT / "favicon.ico", "image/x-icon")
-            else:
-                self.send_error(404, f"Path {path} not found")
-
-    def _serve_file(self, file_path: Path, content_type: str):
-            if not file_path.exists():
-                print(f"DEBUG: File not found at {file_path}") # Check Vercel logs for this
-                self.send_error(404, f"File {file_path.name} not found on server")
-                return
-
-            body = file_path.read_bytes()
-            self.send_response(200)
-            self.send_header("Content-Type", content_type)
-            self.send_header("Content-Length", str(len(body)))
-            self._cors_headers()
-            self.end_headers()
-            self.wfile.write(body)
-
-    # POST ─────────────────────────────────────────────────────────────────────
-
-    def do_POST(self):
-            # Strip trailing slashes and query parameters for the match
-            clean_path = self.path.split('?')[0].rstrip('/')
-
-            if clean_path == "/api/audit":
-                self._handle_audit()
-            elif clean_path == "/api/audit/url":
-                self._handle_url_audit(nested=False)
-            elif clean_path == "/api/audit/url/nested":
-                self._handle_url_audit(nested=True)
-            else:
-                self._send_json({"error": f"Endpoint {clean_path} Not Found"}, 404)
-
-    def _start_ndjson_stream(self):
-        """Send NDJSON response headers and return a send_event callable."""
-        self.send_response(200)
-        self.send_header("Content-Type", "application/x-ndjson; charset=utf-8")
-        self._cors_headers()
-        self.end_headers()
-
-        def send_event(obj: dict) -> None:
-            line = json.dumps(obj, ensure_ascii=False) + "\n"
-            self.wfile.write(line.encode("utf-8"))
-            self.wfile.flush()
-
-        return send_event
-
-    def _handle_audit(self):
-        length = int(self.headers.get("Content-Length", 0))
-        body = self.rfile.read(length)
-
-        try:
-            data = json.loads(body)
-        except json.JSONDecodeError:
-            self._send_json({"success": False, "error": "Invalid JSON body"}, 400)
-            return
-
-        html_content = data.get("html_content", "").strip()
-        if not html_content:
-            self._send_json(
-                {"success": False, "error": "html_content is required"}, 400
-            )
-            return
-
-        model = data.get("model", "claude-haiku-4-5-20251001")
-        api_key = _resolve_api_key(data, model)
-
-        print(
-            f"  Audit request: {len(html_content):,} chars, "
-            f"model={model}, api_key={'set' if api_key else 'not set'}"
-        )
-
-        send_event = self._start_ndjson_stream()
-        send_event({
-            "type": "progress",
-            "stage": "starting",
-            "message": "Starting audit…",
-        })
-        result = run_audit(html_content, api_key, model, progress_callback=send_event)
-        result["type"] = "result"
-        send_event(result)
-
-    def _handle_url_audit(self, nested: bool):
-        """Fetch HTML from a URL (and optionally its nested links) then audit."""
-        length = int(self.headers.get("Content-Length", 0))
-        body = self.rfile.read(length)
-
-        try:
-            data = json.loads(body)
-        except json.JSONDecodeError:
-            self._send_json({"success": False, "error": "Invalid JSON body"}, 400)
-            return
-
-        url = data.get("url", "").strip()
-        if not url:
-            self._send_json({"success": False, "error": "url is required"}, 400)
-            return
-
-        model = data.get("model", "claude-haiku-4-5-20251001")
-        api_key = _resolve_api_key(data, model)
-
-        print(
-            f"  URL audit request ({'nested' if nested else 'single'}): {url}, "
-            f"model={model}, api_key={'set' if api_key else 'not set'}"
-        )
-
-        if not nested:
-            send_event = self._start_ndjson_stream()
-            send_event({
-                "type": "progress",
-                "stage": "fetching",
-                "message": f"Fetching {url}…",
-            })
-            try:
-                html_content = fetch_page(url)
-            except Exception as exc:
-                result = {"type": "result", "success": False, "error": f"Failed to fetch URL: {exc}"}
-                send_event(result)
-                return
-            print(f"  [url_audit] Fetched {len(html_content):,} chars from {url}")
-            lower = html_content[:2000].lower()
-            if any(marker in lower for marker in ("just a moment", "cf-browser-verification", "ray id", "enable javascript and cookies")):
-                print("  [url_audit] WARNING: response looks like a bot-challenge page, not real content")
-            title_match = re.search(r"<title[^>]*>(.*?)</title>", html_content[:4000], re.IGNORECASE | re.DOTALL)
-            print(f"  [url_audit] <title>: {title_match.group(1).strip() if title_match else '(not found)'}")
-            send_event({
-                "type": "progress",
-                "stage": "fetch_complete",
-                "message": "Page fetched — starting analysis…",
-            })
-            result = run_audit(html_content, api_key, model, progress_callback=send_event)
-            result["type"] = "result"
-            send_event(result)
-            return
-
-        # ── Multi-page: stream progress, run pipeline per page, merge ─────
-        try:
-            html_content, crawl_tree = fetch_pages_nested(url)
-        except Exception as exc:
-            self._send_json({"success": False, "error": f"Failed to fetch URL: {exc}"}, 502)
-            return
-
-        pages = split_pages(html_content)
-        total_pages = len(pages)
-        print(f"  Split into {total_pages} page(s)")
-
-        _send_event = self._start_ndjson_stream()
-
-        _send_event({
-            "type": "progress",
-            "stage": "crawl_complete",
-            "total_pages": total_pages,
-            "message": f"Found {total_pages} page(s) to audit",
-        })
-
-        merged = {
-            "success": True,
-            "crawl_tree": crawl_tree,
-            "page_results": {},
-            "programmatic_findings": [],
-            "llm_results": {},
-            "csv_report": None,
-            "skipped_prompts": [],
-            "pages_audited": [],
-            "summary": {
-                "programmatic_count": 0,
-                "programmatic_by_checker": {},
-                "llm_prompts_run": 0,
-                "llm_prompts_skipped": 0,
-                "total_input_tokens": 0,
-                "total_output_tokens": 0,
-                "estimated_cost_usd": None,
-                "model": model,
-                "dry_run": not api_key,
-                "pages": total_pages,
-            },
-        }
-
-        total_cost = 0.0
-        csv_parts: list[str] = []
-        for page_idx, (page_url, page_html) in enumerate(pages, start=1):
-            _send_event({
-                "type": "progress",
-                "stage": "auditing_page",
-                "page": page_idx,
-                "total_pages": total_pages,
-                "page_url": page_url,
-                "message": f"Auditing page {page_idx}/{total_pages}: {page_url}",
-            })
-
-            print(f"  Auditing: {page_url}")
-            page_result = run_audit(page_html, api_key, model, progress_callback=_send_event)
-
-            if not page_result.get("success"):
-                print(f"    FAILED: {page_result.get('error')}")
-                _send_event({
-                    "type": "progress",
-                    "stage": "page_error",
-                    "page": page_idx,
-                    "page_url": page_url,
-                    "message": f"Failed: {page_result.get('error', 'unknown')}",
-                })
-                continue
-
-            merged["pages_audited"].append(page_url)
-
-            # Store per-page results for the tree view
-            merged["page_results"][page_url] = {
-                "programmatic_findings": page_result.get("programmatic_findings", []),
-                "llm_results": page_result.get("llm_results", {}),
-                "summary": page_result.get("summary", {}),
-            }
-
-            page_csv = page_result.get("csv_report")
-            if page_csv:
-                if not csv_parts:
-                    csv_parts.append(page_csv.rstrip("\n"))
-                else:
-                    lines = page_csv.split("\n", 1)
-                    if len(lines) > 1 and lines[1].strip():
-                        csv_parts.append(lines[1].rstrip("\n"))
-
-            for finding in page_result.get("programmatic_findings", []):
-                finding["page_url"] = page_url
-            merged["programmatic_findings"].extend(
-                page_result.get("programmatic_findings", [])
-            )
-
-            for name, result_data in page_result.get("llm_results", {}).items():
-                result_data["page_url"] = page_url
-                key = f"{name}|{page_url}"
-                merged["llm_results"][key] = result_data
-
-            merged["skipped_prompts"].extend(
-                page_result.get("skipped_prompts", [])
-            )
-
-            page_summary = page_result.get("summary", {})
-            merged["summary"]["programmatic_count"] += page_summary.get(
-                "programmatic_count", 0
-            )
-            merged["summary"]["llm_prompts_run"] += page_summary.get(
-                "llm_prompts_run", 0
-            )
-            merged["summary"]["llm_prompts_skipped"] += page_summary.get(
-                "llm_prompts_skipped", 0
-            )
-            merged["summary"]["total_input_tokens"] += page_summary.get(
-                "total_input_tokens", 0
-            )
-            merged["summary"]["total_output_tokens"] += page_summary.get(
-                "total_output_tokens", 0
-            )
-            if page_summary.get("estimated_cost_usd") is not None:
-                total_cost += page_summary["estimated_cost_usd"]
-
-            _send_event({
-                "type": "progress",
-                "stage": "page_complete",
-                "page": page_idx,
-                "total_pages": total_pages,
-                "page_url": page_url,
-                "message": f"Completed page {page_idx}/{total_pages}",
-            })
-
-        if total_cost > 0:
-            merged["summary"]["estimated_cost_usd"] = round(total_cost, 6)
-
-        if csv_parts:
-            merged["csv_report"] = "\n".join(csv_parts) + "\n"
-
-        # Final event: the full merged result
-        merged["type"] = "result"
-        _send_event(merged)
-
-    def _send_json(self, obj: dict, status: int = 200):
-        body = json.dumps(obj, indent=2, ensure_ascii=False).encode("utf-8")
-        self.send_response(status)
-        self.send_header("Content-Type", "application/json; charset=utf-8")
-        self.send_header("Content-Length", str(len(body)))
-        self._cors_headers()
-        self.end_headers()
-        self.wfile.write(body)
\ No newline at end of file
diff --git a/index.html b/index.html
index 4da65bd..0112713 100644
--- a/index.html
+++ b/index.html
@@ -938,7 +938,9 @@ <h2 id="audit-heading">Accessibility Audit Tool</h2>
       </div>
       <p class="sr-only" id="progressStatus" role="status" aria-live="polite">Audit started</p>`;
 
-                // Simulated progress to keep the user engaged while waiting for Vercel
+                // Simulated progress, used only when the backend returns a
+                // single buffered JSON object with no progress events. An
+                // NDJSON stream clears this and drives the bar for real.
                 let progressInterval = setInterval(() => {
                   const fillEl = document.getElementById("progressFill");
                   if (fillEl) {
@@ -961,8 +963,28 @@ <h2 id="audit-heading">Accessibility Audit Tool</h2>
                     throw new Error(`Server Error: ${res.status} - ${errorText}`);
                   }
 
-                  // Vercel returns the full result at once
-                  const data = await res.json();
+                  // The server (entry_points/api_server.py) answers audits with
+                  // an NDJSON stream: progress events, one JSON object per
+                  // line, then a final {"type":"result"} object.  Parsing that
+                  // with res.json() throws "Unexpected non-whitespace character
+                  // after JSON", so the content type decides which reader to
+                  // use — the plain-JSON branch is a fallback for any backend
+                  // that buffers the whole result into a single object.
+                  const contentType = res.headers.get("content-type") || "";
+                  let data;
+
+                  if (contentType.includes("ndjson")) {
+                    // Real progress events supersede the simulated bar.
+                    clearInterval(progressInterval);
+                    data = await readNdjsonStream(res, updateProgress);
+                    if (!data) {
+                      throw new Error(
+                              "The server closed the connection before returning a result."
+                      );
+                    }
+                  } else {
+                    data = await res.json();
+                  }
 
                   clearInterval(progressInterval);
 
@@ -987,7 +1009,7 @@ <h2 id="audit-heading">Accessibility Audit Tool</h2>
         <div class="audit-error" role="alert">
           <strong>Audit Failed</strong><br>
           ${esc(err.message)}<br><br>
-          <small>Check the Vercel Dashboard logs for Python tracebacks.</small>
+          <small>See the browser console for details. If this keeps happening, check that your API key is valid, or review the server logs.</small>
         </div>`;
                   resultsSection.style.display = "block";
                 } finally {
@@ -996,6 +1018,92 @@ <h2 id="audit-heading">Accessibility Audit Tool</h2>
                 }
               });
 
+      // Approximate completion percentage for each named pipeline stage.
+      // Per-prompt and per-page events are interpolated between these.
+      const STAGE_PCT = {
+        starting: 5,
+        fetching: 8,
+        fetch_complete: 12,
+        crawl_complete: 15,
+        programmatic_complete: 18,
+        extraction_complete: 20,
+        report_generating: 92,
+        report_complete: 97,
+      };
+
+      /**
+       * Read an NDJSON response body, dispatching each {"type":"progress"}
+       * event to onProgress and returning the final {"type":"result"} object,
+       * or null if the stream ended without one.
+       */
+      async function readNdjsonStream(res, onProgress) {
+        const reader = res.body.getReader();
+        const decoder = new TextDecoder();
+        let buffer = "";
+        let result = null;
+
+        const handleLine = (line) => {
+          const trimmed = line.trim();
+          if (!trimmed) return;
+          let evt;
+          try {
+            evt = JSON.parse(trimmed);
+          } catch {
+            // A single malformed line should not abort an otherwise good run.
+            return;
+          }
+          if (evt.type === "result") result = evt;
+          else if (evt.type === "progress" && onProgress) onProgress(evt);
+        };
+
+        for (;;) {
+          const { done, value } = await reader.read();
+          if (done) break;
+          buffer += decoder.decode(value, { stream: true });
+          const lines = buffer.split("\n");
+          buffer = lines.pop(); // last element may be a partial line
+          lines.forEach(handleLine);
+        }
+        handleLine(buffer); // flush whatever remained unterminated
+
+        return result;
+      }
+
+      /** Update the progress bar and status text from a server progress event. */
+      function updateProgress(evt) {
+        const msgEl = document.getElementById("progressMessage");
+        const fillEl = document.getElementById("progressFill");
+        const detailEl = document.getElementById("progressDetail");
+        const statusEl = document.getElementById("progressStatus");
+
+        if (evt.message) {
+          if (msgEl) msgEl.textContent = evt.message;
+          if (statusEl) statusEl.textContent = evt.message;
+        }
+
+        let pct = null;
+        if (evt.stage === "llm_progress" && evt.total) {
+          // Prompt calls dominate the runtime, so they own the 20–90% band.
+          pct = 20 + Math.round((evt.completed / evt.total) * 70);
+          if (detailEl) {
+            detailEl.textContent =
+                    `Prompt ${evt.completed} of ${evt.total}` +
+                    (evt.prompt_name ? ` — ${evt.prompt_name}` : "");
+          }
+        } else if (evt.stage === "auditing_page" && evt.total_pages) {
+          pct = Math.round((evt.page / evt.total_pages) * 90);
+          if (detailEl) {
+            detailEl.textContent = `Page ${evt.page} of ${evt.total_pages}`;
+          }
+        } else if (STAGE_PCT[evt.stage] != null) {
+          pct = STAGE_PCT[evt.stage];
+        }
+
+        if (pct != null && fillEl) {
+          fillEl.style.width = Math.min(pct, 95) + "%";
+        }
+      }
+
       // 868: Added 'data' parameter to access the csv_report string
       function attachResultListeners(container, data) {
         // Wire up page tree node toggles
diff --git a/processing_scripts/docs/pipeline.md b/processing_scripts/docs/pipeline.md
index 5f5962f..63435a6 100644
--- a/processing_scripts/docs/pipeline.md
+++ b/processing_scripts/docs/pipeline.md
@@ -316,7 +316,7 @@ Every prompt returns structured JSON. Example responses:
 ## File Map
 
 ```
-visionaid-a11y-llm-audit/
+va-dat/
 │
 ├── semantic_checklist/               <- Deque WCAG reference documents (source of truth)
 │   ├── 01-semantic-checklist.pdf
diff --git a/pyproject.toml b/pyproject.toml
index e1b88b9..29ab6af 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,9 +16,6 @@ dependencies = [
     "google-genai>=1.0.0",
     "python-dotenv~=1.2.1",
     "requests>=2.32.2",
-    "fastapi>=0.110.0",
-    "uvicorn>=0.27.0",
-    "python-multipart>=0.0.9",
 ]
 
 [tool.setuptools]
diff --git a/render.yaml b/render.yaml
deleted file mode 100644
index 1b5b6bc..0000000
--- a/render.yaml
+++ /dev/null
@@ -1,12 +0,0 @@
-services:
-  - type: web
-    name: visionaid-a11y-audit
-    runtime: python
-    buildCommand: poetry install --no-root
-    startCommand: python entry_points/api_server.py --host 0.0.0.0
-    envVars:
-      - key: PORT
-        generateValue: false        # Render injects this automatically
-      - key: ANTHROPIC_API_KEY
-        sync: false                 # Set this in the Render dashboard (optional —
-                                    # users can also supply their own key in the form)
diff --git a/requirements.txt b/requirements.txt
index 9c45b95..1a5da01 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,107 @@
-anthropic~=0.84.0
-beautifulsoup4>=4.9.2
-lxml>=4.9.0
-openai>=1.0.0
-google-genai>=1.0.0
-python-dotenv~=1.2.1
-requests>=2.32.2
-fastapi>=0.110.0
-uvicorn>=0.27.0
-python-multipart>=0.0.9
+# This file was autogenerated by uv via the following command:
+#    uv export --format requirements.txt --no-dev --no-emit-project --no-hashes -o requirements.txt
+annotated-types==0.8.0
+    # via pydantic
+anthropic==0.84.0
+    # via visionaid
+anyio==4.14.2
+    # via
+    #   anthropic
+    #   google-genai
+    #   httpx
+    #   openai
+beautifulsoup4==4.15.0
+    # via visionaid
+certifi==2026.7.22
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+cffi==2.1.1 ; platform_python_implementation != 'PyPy'
+    # via cryptography
+charset-normalizer==3.4.9
+    # via requests
+colorama==0.4.6 ; sys_platform == 'win32'
+    # via tqdm
+cryptography==50.0.0
+    # via google-auth
+distro==1.9.0
+    # via
+    #   anthropic
+    #   google-genai
+    #   openai
+docstring-parser==0.18.0
+    # via anthropic
+google-auth==2.56.3
+    # via google-genai
+google-genai==2.17.0
+    # via visionaid
+h11==0.16.0
+    # via httpcore
+httpcore==1.0.9
+    # via httpx
+httpx==0.28.1
+    # via
+    #   anthropic
+    #   google-genai
+    #   openai
+idna==3.18
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+jiter==0.16.0
+    # via
+    #   anthropic
+    #   openai
+lxml==6.1.1
+    # via visionaid
+openai==2.53.0
+    # via visionaid
+pyasn1==0.6.4
+    # via pyasn1-modules
+pyasn1-modules==0.4.2
+    # via google-auth
+pycparser==3.0 ; implementation_name != 'PyPy' and platform_python_implementation != 'PyPy'
+    # via cffi
+pydantic==2.13.4
+    # via
+    #   anthropic
+    #   google-genai
+    #   openai
+pydantic-core==2.46.4
+    # via pydantic
+python-dotenv==1.2.2
+    # via visionaid
+requests==2.34.2
+    # via
+    #   google-auth
+    #   google-genai
+    #   visionaid
+sniffio==1.3.1
+    # via
+    #   anthropic
+    #   google-genai
+    #   openai
+soupsieve==2.9.2
+    # via beautifulsoup4
+tenacity==9.1.4
+    # via google-genai
+tqdm==4.70.0
+    # via openai
+typing-extensions==4.16.0
+    # via
+    #   anthropic
+    #   anyio
+    #   beautifulsoup4
+    #   google-genai
+    #   openai
+    #   pydantic
+    #   pydantic-core
+    #   typing-inspection
+typing-inspection==0.4.2
+    # via pydantic
+urllib3==2.7.0
+    # via requests
+websockets==16.1.1
+    # via google-genai
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..4693dfe
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,981 @@
+version = 1
+revision = 3
+requires-python = ">=3.11"
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version < '3.14'",
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.8.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5f/56/a8120250d128bed162cd73c76d45f6ef9991f3e068f62a8ee060afa3104a/annotated_types-0.8.0.tar.gz", hash = "sha256:13b2beaad985e05e2d6407ee4c4f35590b11f8d693a258a561055cac8f64cab7", size = 15893, upload-time = "2026-07-23T20:16:13.995Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/99/91/8acff4f5e50511b911bbccb72b8628a49c68ce14148cd9f6431094859a90/annotated_types-0.8.0-py3-none-any.whl", hash = "sha256:f072f4d804ea359e4eaf198b1af7a8b0943881a87f31bb764f8bf219bb9419e0", size = 13427, upload-time = "2026-07-23T20:16:12.938Z" },
+]
+
+[[package]]
+name = "anthropic"
+version = "0.84.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "distro" },
+    { name = "docstring-parser" },
+    { name = "httpx" },
+    { name = "jiter" },
+    { name = "pydantic" },
+    { name = "sniffio" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/04/ea/0869d6df9ef83dcf393aeefc12dd81677d091c6ffc86f783e51cf44062f2/anthropic-0.84.0.tar.gz", hash = "sha256:72f5f90e5aebe62dca316cb013629cfa24996b0f5a4593b8c3d712bc03c43c37", size = 539457, upload-time = "2026-02-25T05:22:38.54Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/64/ca/218fa25002a332c0aa149ba18ffc0543175998b1f65de63f6d106689a345/anthropic-0.84.0-py3-none-any.whl", hash = "sha256:861c4c50f91ca45f942e091d83b60530ad6d4f98733bfe648065364da05d29e7", size = 455156, upload-time = "2026-02-25T05:22:40.468Z" },
+]
+
+[[package]]
+name = "anyio"
+version = "4.14.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/61/cc/a381afa6efea9f496eff839d4a6a1aed3bfafc7b3ab4b0d1b243a12573dd/anyio-4.14.2.tar.gz", hash = "sha256:cfa139f3ed1a23ee8f88a145ddb5ac7605b8bbfd8592baacd7ce3d8bb4313c7f", size = 260176, upload-time = "2026-07-12T20:29:07.082Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/da/35/f2287558c17e29fafc8ef3daf819bb9834061cfa43bff8014f7df7f63bdc/anyio-4.14.2-py3-none-any.whl", hash = "sha256:9f505dda5ac9f0c8309b5e8bd445a8c2bf7246f3ce950121e45ea15bc41d1494", size = 125813, upload-time = "2026-07-12T20:29:05.763Z" },
+]
+
+[[package]]
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diff --git a/vercel.json b/vercel.json
deleted file mode 100644
index 850e39f..0000000
--- a/vercel.json
+++ /dev/null
@@ -1,16 +0,0 @@
-{
-  "version": 2,
-  "rewrites": [
-    {
-          "source": "/styles.css",
-          "destination": "/styles.css"
-        },
-        {
-          "source": "/favicon.ico",
-          "destination": "/favicon.ico"
-        },
-    { "source": "/api/validate-key", "destination": "/api/index.py" },
-    { "source": "/api/(.*)", "destination": "/api/index.py" },
-    { "source": "/((?!api/).*)", "destination": "/index.html" }
-  ]
-}
\ No newline at end of file
diff --git a/vision_aid/agentic_processing/__init__.py b/vision_aid/agentic_processing/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/vision_aid/llm_client/__init__.py b/vision_aid/llm_client/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/vision_aid/llm_client/client.py b/vision_aid/llm_client/client.py
deleted file mode 100644
index 546261e..0000000
--- a/vision_aid/llm_client/client.py
+++ /dev/null
@@ -1,173 +0,0 @@
-"""
-AuditClient — thin wrappers around the Anthropic and OpenAI APIs.
-
-Loads API keys from the project root .env file automatically.
-"""
-
-import json
-import os
-from pathlib import Path
-
-import anthropic
-import openai
-from dotenv import load_dotenv
-
-# Project root is two levels up from this file (vision_aid/llm_client/)
-_ENV_FILE = Path(__file__).resolve().parents[2] / ".env"
-load_dotenv(_ENV_FILE)
-
-# OpenAI model prefixes used to detect provider from model ID
-_OPENAI_PREFIXES = ("gpt-", "o1", "o3", "o4")
-
-
-def is_openai_model(model: str) -> bool:
-    """Return True if *model* looks like an OpenAI model ID."""
-    return model.startswith(_OPENAI_PREFIXES)
-
-
-class AuditClient:
-    """
-    Sends filled accessibility-audit prompts to the Claude API and returns
-    parsed JSON responses.
-
-    Parameters
-    ----------
-    model : str
-        Claude model ID to use (default: claude-sonnet-4-6).
-    temperature : float
-        Sampling temperature (default: 0.1 for consistent structured output).
-    max_tokens : int
-        Maximum tokens to generate per response (default: 8192).
-    """
-
-    def __init__(
-        self,
-        model: str = "claude-sonnet-4-6",
-        temperature: float = 0.1,
-        max_tokens: int = 8192,
-    ):
-        api_key = os.environ.get("ANTHROPIC_API_KEY")
-        if not api_key:
-            raise EnvironmentError(
-                f"ANTHROPIC_API_KEY not found. Expected in {_ENV_FILE}"
-            )
-        self._client = anthropic.Anthropic(api_key=api_key)
-        self.model = model
-        self.temperature = temperature
-        self.max_tokens = max_tokens
-
-    def call(self, prompt_text: str, payload: dict) -> tuple[dict, dict]:
-        """
-        Fill `{payload}` in *prompt_text* with JSON-serialised *payload*,
-        send to the API, and return ``(parsed_response, usage)``.
-
-        The response is expected to be a JSON object (possibly wrapped in a
-        markdown code fence, which is stripped automatically).
-
-        Returns
-        -------
-        response_json : dict
-            Parsed JSON from the model's reply.
-        usage : dict
-            ``{"input_tokens": int, "output_tokens": int}``
-        """
-        filled = prompt_text.replace("{payload}", json.dumps(payload, separators=(",", ":")))
-
-        message = self._client.messages.create(
-            model=self.model,
-            max_tokens=self.max_tokens,
-            temperature=self.temperature,
-            messages=[{"role": "user", "content": filled}],
-        )
-
-        usage = {
-            "input_tokens": message.usage.input_tokens,
-            "output_tokens": message.usage.output_tokens,
-        }
-
-        raw = message.content[0].text.strip()
-
-        # Strip markdown code fences if present
-        if raw.startswith("```"):
-            raw = raw.split("\n", 1)[1]
-            raw = raw.rsplit("```", 1)[0].strip()
-
-        return json.loads(raw), usage
-
-
-class OpenAIAuditClient:
-    """
-    Sends filled accessibility-audit prompts to the OpenAI API and returns
-    parsed JSON responses.  Same interface as :class:`AuditClient`.
-
-    Parameters
-    ----------
-    model : str
-        OpenAI model ID to use (default: gpt-4o).
-    temperature : float
-        Sampling temperature (default: 0.1 for consistent structured output).
-    max_tokens : int
-        Maximum tokens to generate per response (default: 8192).
-    """
-
-    def __init__(
-        self,
-        model: str = "gpt-4o",
-        temperature: float = 0.1,
-        max_tokens: int = 8192,
-    ):
-        api_key = os.environ.get("OPENAI_API_KEY")
-        if not api_key:
-            raise EnvironmentError(
-                f"OPENAI_API_KEY not found. Expected in {_ENV_FILE}"
-            )
-        self._client = openai.OpenAI(api_key=api_key)
-        self.model = model
-        self.temperature = temperature
-        self.max_tokens = max_tokens
-
-    def call(self, prompt_text: str, payload: dict) -> tuple[dict, dict]:
-        """
-        Fill `{payload}` in *prompt_text* with JSON-serialised *payload*,
-        send to the API, and return ``(parsed_response, usage)``.
-
-        Returns
-        -------
-        response_json : dict
-            Parsed JSON from the model's reply.
-        usage : dict
-            ``{"input_tokens": int, "output_tokens": int}``
-        """
-        filled = prompt_text.replace("{payload}", json.dumps(payload, separators=(",", ":")))
-
-        response = self._client.chat.completions.create(
-            model=self.model,
-            max_tokens=self.max_tokens,
-            temperature=self.temperature,
-            messages=[{"role": "user", "content": filled}],
-        )
-
-        usage = {
-            "input_tokens": response.usage.prompt_tokens,
-            "output_tokens": response.usage.completion_tokens,
-        }
-
-        raw = response.choices[0].message.content.strip()
-
-        # Strip markdown code fences if present
-        if raw.startswith("```"):
-            raw = raw.split("\n", 1)[1]
-            raw = raw.rsplit("```", 1)[0].strip()
-
-        return json.loads(raw), usage
-
-
-def create_audit_client(
-    model: str = "claude-sonnet-4-6",
-    temperature: float = 0.1,
-    max_tokens: int = 8192,
-) -> AuditClient | OpenAIAuditClient:
-    """Factory that returns the right client class based on the model ID."""
-    if is_openai_model(model):
-        return OpenAIAuditClient(model=model, temperature=temperature, max_tokens=max_tokens)
-    return AuditClient(model=model, temperature=temperature, max_tokens=max_tokens)
diff --git a/vision_aid/llm_client/prompt_loader.py b/vision_aid/llm_client/prompt_loader.py
deleted file mode 100644
index 60d33d6..0000000
--- a/vision_aid/llm_client/prompt_loader.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""
-Parses the numbered prompt sections from the LLM prompt-library text files.
-
-File format (see processing_scripts/llm/*.txt):
-    ---------------------------------
-    N) PROMPT TITLE
-    ---------------------------------
-    <prompt body>
-    Data: {payload}
-
-    ---------------------------------
-    N+1) NEXT PROMPT TITLE
-    ...
-"""
-
-import re
-from pathlib import Path
-
-
-_HEADER_RE = re.compile(r"^\s{2,}(\d+)\)\s+.+", re.MULTILINE)
-_DIVIDER_RE = re.compile(r"^\s*-{5,}\s*$", re.MULTILINE)
-
-
-def load_prompts(filepath: str | Path) -> dict[int, str]:
-    """
-    Parse *filepath* and return ``{prompt_number: prompt_text}`` for every
-    numbered section found.  Divider lines (``---...``) are stripped.
-
-    The returned text for each prompt still contains the ``{payload}``
-    placeholder so the caller can fill it later.
-    """
-    text = Path(filepath).read_text(encoding="utf-8")
-
-    # Find all header positions and their prompt numbers
-    headers = list(_HEADER_RE.finditer(text))
-    if not headers:
-        return {}
-
-    prompts: dict[int, str] = {}
-
-    for i, header in enumerate(headers):
-        num = int(header.group(1))
-        start = header.end()  # character just after the header line
-        end = headers[i + 1].start() if i + 1 < len(headers) else len(text)
-
-        body = text[start:end]
-
-        # Remove divider lines
-        body = _DIVIDER_RE.sub("", body)
-
-        prompts[num] = body.strip()
-
-    return prompts
-
-
-def load_all_prompts(prompts_dir: str | Path) -> dict[str, dict[int, str]]:
-    """
-    Load all ``*.txt`` prompt files in *prompts_dir*.
-
-    Returns ``{stem: {prompt_number: prompt_text}}``, e.g.::
-
-        {
-            "semantic_checklist_01": {1: "...", 2: "...", ...},
-            "forms_checklist_02":    {1: "...", ...},
-            "nontext_checklist_03":  {1: "...", ...},
-        }
-    """
-    prompts_dir = Path(prompts_dir)
-    result: dict[str, dict[int, str]] = {}
-    for txt_file in sorted(prompts_dir.glob("*.txt")):
-        result[txt_file.stem] = load_prompts(txt_file)
-    return result
diff --git a/vision_aid/llm_client/runner.py b/vision_aid/llm_client/runner.py
deleted file mode 100644
index 97bbee8..0000000
--- a/vision_aid/llm_client/runner.py
+++ /dev/null
@@ -1,159 +0,0 @@
-"""
-High-level runner that executes one or all checklist prompt sets against
-pre-built JSON payloads, collects responses, and aggregates token usage.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from .client import AuditClient
-
-
-def run_checklist(
-    client: AuditClient,
-    prompts: dict[int, str],
-    slices: dict[str, Any | None],
-    *,
-    verbose: bool = True,
-) -> dict[str, Any]:
-    """
-    Run every prompt in *prompts* against the matching payload slice in *slices*.
-
-    Parameters
-    ----------
-    client : AuditClient
-        Configured API client.
-    prompts : dict[int, str]
-        ``{prompt_number: prompt_text}`` from ``load_prompts()``.
-    slices : dict[str, payload | None]
-        Mapping of label → payload dict (or ``None`` to skip).
-        Labels are used as keys in the returned results dict.
-    verbose : bool
-        Print progress to stdout while running.
-
-    Returns
-    -------
-    dict with keys:
-        - ``"results"``  : ``{label: response_json}``
-        - ``"skipped"``  : list of skipped labels
-        - ``"usage"``    : ``{"input_tokens": int, "output_tokens": int}``
-        - ``"errors"``   : ``{label: error_message}``
-    """
-    results: dict[str, Any] = {}
-    skipped: list[str] = []
-    errors: dict[str, str] = {}
-    total_input = 0
-    total_output = 0
-
-    # Map labels → prompt numbers by position (label order == prompt order)
-    labels = list(slices.keys())
-
-    for idx, label in enumerate(labels):
-        prompt_num = idx + 1
-        payload = slices[label]
-
-        if payload is None:
-            if verbose:
-                print(f"  [{prompt_num}] {label} — SKIPPED (filtered by Pass 1)")
-            skipped.append(label)
-            continue
-
-        # Skip if the payload is empty after filtering
-        if _is_empty_payload(payload):
-            if verbose:
-                print(f"  [{prompt_num}] {label} — SKIPPED (empty payload after filtering)")
-            skipped.append(label)
-            continue
-
-        prompt_text = prompts.get(prompt_num)
-        if prompt_text is None:
-            if verbose:
-                print(f"  [{prompt_num}] {label} — SKIPPED (no prompt text found)")
-            skipped.append(label)
-            continue
-
-        if verbose:
-            print(f"  [{prompt_num}] {label} ...", end=" ", flush=True)
-
-        try:
-            response, usage = client.call(prompt_text, payload)
-            results[label] = response
-            total_input += usage["input_tokens"]
-            total_output += usage["output_tokens"]
-            if verbose:
-                print(f"✓ ({usage['input_tokens']}in / {usage['output_tokens']}out tokens)")
-        except Exception as exc:
-            errors[label] = str(exc)
-            if verbose:
-                print(f"✗ ERROR: {exc}")
-
-    return {
-        "results": results,
-        "skipped": skipped,
-        "usage": {"input_tokens": total_input, "output_tokens": total_output},
-        "errors": errors,
-    }
-
-
-def run_all(
-    client: AuditClient,
-    all_prompts: dict[str, dict[int, str]],
-    all_slices: dict[str, dict[str, Any | None]],
-    *,
-    verbose: bool = True,
-) -> dict[str, Any]:
-    """
-    Run all checklists in *all_slices* and return a consolidated report dict.
-
-    Parameters
-    ----------
-    client : AuditClient
-    all_prompts : dict[str, dict[int, str]]
-        ``{checklist_stem: {prompt_num: prompt_text}}`` from ``load_all_prompts()``.
-    all_slices : dict[str, dict[str, payload|None]]
-        ``{checklist_stem: {label: payload|None}}``.
-    verbose : bool
-
-    Returns
-    -------
-    dict with keys:
-        - ``"checklists"`` : ``{stem: checklist_result}``
-        - ``"total_usage"`` : ``{"input_tokens": int, "output_tokens": int}``
-    """
-    checklists: dict[str, Any] = {}
-    grand_input = 0
-    grand_output = 0
-
-    for stem, slices in all_slices.items():
-        prompts = all_prompts.get(stem, {})
-        if verbose:
-            print(f"\n--- {stem} ---")
-
-        result = run_checklist(client, prompts, slices, verbose=verbose)
-        checklists[stem] = result
-        grand_input += result["usage"]["input_tokens"]
-        grand_output += result["usage"]["output_tokens"]
-
-    return {
-        "checklists": checklists,
-        "total_usage": {"input_tokens": grand_input, "output_tokens": grand_output},
-    }
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def _is_empty_payload(payload: Any) -> bool:
-    """Return True if every list-valued entry in the payload is empty."""
-    if not isinstance(payload, dict):
-        return False
-    for v in payload.values():
-        if isinstance(v, list) and v:
-            return False
-        if isinstance(v, dict) and v:
-            return False
-        if isinstance(v, str) and v.strip():
-            return False
-    return True
diff --git a/vision_aid/programmatic/__init__.py b/vision_aid/programmatic/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/vision_aid/programmatic/base.py b/vision_aid/programmatic/base.py
deleted file mode 100644
index 02935e1..0000000
--- a/vision_aid/programmatic/base.py
+++ /dev/null
@@ -1,104 +0,0 @@
-from abc import abstractmethod, ABC
-from bs4 import BeautifulSoup
-import sys
-
-class AuditBase(ABC):
-
-    def __init__(
-        self,
-        file_path:str
-    ):
-        self.file_path = file_path
-        self.soup = self.get_soup()
-        self.results = []
-
-    @staticmethod
-    def css_path(el):
-        """
-        Generate a CSS-like DOM path for an element.
-        """
-        path = []
-        while el and el.name:
-            sibling_index = 1
-            sibling = el
-            while sibling.previous_sibling:
-                sibling = sibling.previous_sibling
-                if getattr(sibling, "name", None) == el.name:
-                    sibling_index += 1
-
-            if sibling_index > 1:
-                path.append(f"{el.name}:nth-of-type({sibling_index})")
-            else:
-                path.append(el.name)
-
-            el = el.parent
-
-        return " > ".join(reversed(path))
-
-    @staticmethod
-    def element_location(element):
-        """
-        Build structured location metadata for an element.
-        """
-        if element is None:
-            return None
-
-        attrs = dict(element.attrs)
-
-        return {
-            "tag": element.name,
-            "id": attrs.get("id"),
-            "class": attrs.get("class"),
-            "css_path": AuditBase.css_path(element),
-            "attributes": attrs,
-            "text_preview": element.get_text(strip=True)[:80]
-        }
-
-    @staticmethod
-    def issue(rule_id, rule_name, element, description):
-        """
-        Create a standardized issue object.
-        """
-        return {
-            "rule_id": rule_id,
-            "rule_name": rule_name,
-            "location": AuditBase.element_location(element),
-            "description": description
-        }
-    
-    @staticmethod
-    def parse_results(results):
-        if not results:
-            print("No issues found.")
-            sys.exit(0)
-
-        for r in results:
-            print(f"[{r['rule_id']}] {r['rule_name']}")
-
-            location = r.get("location")
-
-            if location:
-                if location.get("css_path"):
-                    print(f"  Location: {location['css_path']}")
-                if location.get("id"):
-                    print(f"  ID: {location['id']}")
-                if location.get("attributes"):
-                    print(f"  Attributes: {location['attributes']}")
-                if location.get("text_preview"):
-                    print(f"  Text Preview: \"{location['text_preview']}\"")
-
-            print(f"  Description: {r['description']}")
-            print()
-    
-    def get_soup(self):
-        with open(self.file_path, "r", encoding="utf-8") as f:
-            soup = BeautifulSoup(f.read(), "lxml")
-        return soup
-    
-    @abstractmethod
-    def run_audit(self):
-        """
-        Abstract method for defining programmatic
-        audits for each of the element types. 
-        """
-        pass
\ No newline at end of file
diff --git a/vision_aid/programmatic/forms.py b/vision_aid/programmatic/forms.py
deleted file mode 100644
index b792ad4..0000000
--- a/vision_aid/programmatic/forms.py
+++ /dev/null
@@ -1,131 +0,0 @@
-from base import AuditBase
-import sys
-import os
-
-class FormAudit(AuditBase):
-    def __init__(self, file_path):
-        super().__init__(file_path)
-        self.form_controls = self.get_controls()
-
-    def get_controls(self):
-        form_controls = []
-
-        for control in self.soup.find_all(["input", "select", "textarea"]):
-
-            # Skip non-interactive input types
-            if control.name == "input":
-                input_type = (control.get("type") or "text").lower()
-
-                if input_type in [
-                    "hidden",
-                    "submit",
-                    "reset",
-                    "button",
-                    "image"
-                ]:
-                    continue
-
-            form_controls.append(control)
-        
-        return form_controls
-    
-    def set_legend(self):
-        for fieldset in self.soup.find_all("fieldset"):
-
-            legend = fieldset.find("legend")
-
-            # FORM_GROUP_001 — Fieldset missing legend
-            if not legend:
-                self.results.append(FormAudit.issue(
-                    "FORM_GROUP_001",
-                    "Fieldset missing legend",
-                    fieldset,
-                    "Fieldset does not contain a legend element."
-                ))
-    
-    def set_required_fields(self):
-        for control in self.form_controls:
-
-            # Already programmatically required
-            if "required" in control.attrs:
-                continue
-
-            control_id = control.get("id")
-
-            # Heuristic: label contains *
-            if control_id:
-                label = self.soup.find("label", attrs={"for": control_id})
-                if label and "*" in label.get_text():
-                    self.results.append(FormAudit.issue(
-                        "FORM_REQUIRED_001",
-                        "Required field not programmatically designated",
-                        control,
-                        "Field appears visually required but lacks 'required' attribute."
-                    ))
-    
-    def set_aria(self):
-        for control in self.form_controls:
-
-            describedby = control.get("aria-describedby")
-            if describedby:
-                ids = describedby.split()
-                for ref_id in ids:
-                    if not self.soup.find(id=ref_id):
-                        self.results.append(FormAudit.issue(
-                            "FORM_INSTR_001",
-                            "aria-describedby reference not found",
-                            control,
-                            f"aria-describedby references missing ID '{ref_id}'."
-                        ))
-    
-    def set_message_assc(self):
-        for control in self.form_controls:
-
-            if control.get("aria-invalid") == "true":
-                describedby = control.get("aria-describedby")
-
-                if not describedby:
-                    self.results.append(FormAudit.issue(
-                        "FORM_ERROR_001",
-                        "Error message not programmatically associated",
-                        control,
-                        "Invalid form control lacks aria-describedby linking to error message."
-                    ))
-
-    def set_custom_interactive(self):
-        for el in self.soup.find_all(True):
-
-            if el.get("onclick") and el.name not in ["button", "a", "input"]:
-                if not el.get("role"):
-                    self.results.append(FormAudit.issue(
-                        "FORM_CUSTOM_001",
-                        "Custom interactive element missing role",
-                        el,
-                        "Element has click handler but no semantic role."
-                    ))
-
-    def run_audit(self):
-        self.set_legend()
-        self.set_required_fields()
-        self.set_aria()
-        self.set_message_assc()
-        self.set_custom_interactive()
-        return self.results
-
-if __name__=='__main__':
-    if len(sys.argv) != 2:
-        print("Usage: python audit_forms.py <file.html>")
-        sys.exit(1)
-
-    file_path = sys.argv[1]
-
-    if not os.path.exists(file_path):
-        print(f"Error: File '{file_path}' not found.")
-        sys.exit(1)
-
-    form_audit = FormAudit(file_path)
-    results = form_audit.run_audit()
-
-    print("\n=== FORM ACCESSIBILITY AUDIT RESULTS ===\n")
-
-    form_audit.parse_results(results)
\ No newline at end of file
diff --git a/vision_aid/programmatic/nontext.py b/vision_aid/programmatic/nontext.py
deleted file mode 100644
index 97fd5c3..0000000
--- a/vision_aid/programmatic/nontext.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from base import AuditBase
-import sys
-import os
-
-class NontextAudit(AuditBase):
-    def __init__(self, file_path):
-        super().__init__(file_path)
-
-    def set_img_alt_txt(self):
-        # Images that convey content MUST have programmatically-discernible alternative text.
-        for img in self.soup.find_all("img"):
-
-            # NON_TEXT_001 — Missing alt attribute
-            if img.get("alt") is None:
-                self.results.append(NontextAudit.issue(
-                    "NON_TEXT_001",
-                    "Image missing alt attribute",
-                    img,
-                    "<img> element does not contain an alt attribute."
-                ))
-
-        # All actionable images MUST have alternative text.
-        for a in self.soup.find_all("a"):
-            img = a.find("img")
-            if img:
-                alt = img.get("alt")
-                if alt is None or alt.strip() == "":
-                    self.results.append(NontextAudit.issue(
-                        "NON_TEXT_002",
-                        "Actionable image missing alt text",
-                        img,
-                        "Image inside a link must have non-empty alt text."
-                    ))
-
-        # Form inputs with type="image" MUST have alternative text.
-        for inp in self.soup.find_all("input", attrs={"type": "image"}):
-            alt = inp.get("alt")
-            if alt is None or alt.strip() == "":
-                self.results.append(NontextAudit.issue(
-                    "NON_TEXT_003",
-                    "Image input missing alt text",
-                    inp,
-                    "Form input type='image' must have non-empty alt text."
-                ))
-
-    def set_img_maps(self):
-        # The alternative text for the <area> element MUST be available.
-        for area in self.soup.find_all("area"):
-            alt = area.get("alt")
-            if alt is None or alt.strip() == "":
-                self.results.append(NontextAudit.issue(
-                    "NON_TEXT_004",
-                    "Image map area missing alt text",
-                    area,
-                    "<area> element must have non-empty alt text."
-                ))
-    
-    def set_svg(self):
-        # SVG SHOULD NOT be embedded via <object> or <iframe>.
-        for obj in self.soup.find_all(["object", "iframe"]):
-            src = obj.get("data") or obj.get("src")
-            if src and src.lower().endswith(".svg"):
-                self.results.append(NontextAudit.issue(
-                    "NON_TEXT_005",
-                    "SVG embedded via object or iframe",
-                    obj,
-                    "SVG should not be embedded using <object> or <iframe>."
-                ))
-
-    def set_canvas(self):
-        # All <canvas> elements MUST have a text alternative.
-        for canvas in self.soup.find_all("canvas"):
-            if not canvas.get_text(strip=True):
-                self.results.append(NontextAudit.issue(
-                    "NON_TEXT_006",
-                    "Canvas missing fallback text",
-                    canvas,
-                    "<canvas> element must contain fallback text content."
-                ))
-
-    def set_plug_ins(self):
-        # All <object> elements MUST have alternative text.
-        for obj in self.soup.find_all("object"):
-            if not obj.get_text(strip=True):
-                self.results.append(NontextAudit.issue(
-                    "NON_TEXT_007",
-                    "Object missing alternative text",
-                    obj,
-                    "<object> element must contain alternative text content."
-                ))
-    
-    def run_audit(self):
-        self.set_img_alt_txt()
-        self.set_img_maps()
-        self.set_svg()
-        self.set_canvas()
-        self.set_plug_ins()
-        return self.results
-
-if __name__ == "__main__":
-
-    if len(sys.argv) != 2:
-        print("Usage: python audit_nontext.py <file.html>")
-        sys.exit(1)
-
-    file_path = sys.argv[1]
-
-    if not os.path.exists(file_path):
-        print(f"Error: File '{file_path}' not found.")
-        sys.exit(1)
-
-    nontext_audit = NontextAudit(file_path)
-    results = nontext_audit.run_audit()
-
-    print("\n=== NON-TEXT ACCESSIBILITY AUDIT RESULTS ===\n")
-
-    nontext_audit.parse_results(results)
\ No newline at end of file
diff --git a/vision_aid/programmatic/semantic.py b/vision_aid/programmatic/semantic.py
deleted file mode 100644
index 9793eba..0000000
--- a/vision_aid/programmatic/semantic.py
+++ /dev/null
@@ -1,408 +0,0 @@
-from base import AuditBase
-import sys
-import os
-import re
-
-class SemanticAudit(AuditBase):
-    def __init__(self, file_path):
-        super().__init__(file_path)
-        self.LANGUAGE_TAG_REGEX = re.compile(r"^[a-zA-Z]{2,3}(-[a-zA-Z0-9]{2,8})*$")
-
-    def set_titles(self):
-        titles = self.soup.find_all("title")
-
-        # PAGE_TITLE_001 — Missing <title>
-        if len(titles) == 0:
-            head = self.soup.find("head")
-            self.results.append(SemanticAudit.issue(
-                "PAGE_TITLE_001",
-                "Missing <title>",
-                head,
-                "The page does not contain a <title> element."
-            ))
-
-        # PAGE_TITLE_002 — Multiple <title>
-        if len(titles) > 1:
-            for t in titles:
-                self.results.append(SemanticAudit.issue(
-                    "PAGE_TITLE_002",
-                    "Multiple <title> elements",
-                    t,
-                    "More than one <title> element found."
-                ))
-
-        # PAGE_TITLE_003 — Empty <title>
-        if titles and not titles[0].get_text(strip=True):
-            self.results.append(SemanticAudit.issue(
-                "PAGE_TITLE_003",
-                "Empty <title>",
-                titles[0],
-                "<title> element exists but contains no text."
-            ))
-
-    def set_lang(self):
-        html = self.soup.find("html")
-        if html:
-            lang = html.get("lang")
-
-            if not lang:
-                self.results.append(SemanticAudit.issue(
-                    "LANG_001",
-                    "Missing primary language",
-                    html,
-                    "<html> element missing lang attribute."
-                ))
-
-            # LANG_002 — Invalid primary language code
-            elif not self.LANGUAGE_TAG_REGEX.match(lang):
-                self.results.append(SemanticAudit.issue(
-                    "LANG_002",
-                    "Invalid primary language code",
-                    html,
-                    f"Invalid language code '{lang}' on <html>."
-                ))
-
-        # LANG_003 — Invalid inline language code
-        for el in self.soup.find_all(attrs={"lang": True}):
-            lang_val = el.get("lang")
-            if not self.LANGUAGE_TAG_REGEX.match(lang_val):
-                self.results.append(SemanticAudit.issue(
-                    "LANG_003",
-                    "Invalid inline language code",
-                    el,
-                    f"Invalid lang attribute '{lang_val}'."
-                ))
-
-    def set_landmark(self):
-        # HTML5 landmark elements mapped to ARIA landmark roles
-        landmark_map = {
-            "header": "banner",
-            "nav": "navigation",
-            "main": "main",
-            "footer": "contentinfo",
-            "aside": "complementary"
-        }
-
-        aria_landmark_roles = {
-            "banner",
-            "navigation",
-            "main",
-            "contentinfo",
-            "complementary",
-            "search",
-            "form",
-            "region"
-        }
-
-        landmarks = []
-
-        # Collect semantic landmark elements
-        for tag, role in landmark_map.items():
-            for el in self.soup.find_all(tag):
-                landmarks.append((el, role))
-
-        # Collect ARIA landmark roles
-        for el in self.soup.find_all(attrs={"role": True}):
-            role = el.get("role")
-            if role in aria_landmark_roles:
-                landmarks.append((el, role))
-
-        role_map = {}
-
-        for el, role in landmarks:
-            role_map.setdefault(role, []).append(el)
-
-        # LAND_001 — No main landmark
-        if "main" not in role_map:
-            body = self.soup.find("body")
-            self.results.append(SemanticAudit.issue(
-                "LAND_001",
-                "Missing main landmark",
-                body,
-                "Page does not contain a <main> landmark."
-            ))
-
-        # LAND_002 — Multiple main landmarks
-        if len(role_map.get("main", [])) > 1:
-            for el in role_map["main"]:
-                self.results.append(SemanticAudit.issue(
-                    "LAND_002",
-                    "Multiple main landmarks",
-                    el,
-                    "More than one 'main' landmark found."
-                ))
-
-        # LAND_003 — Multiple banner landmarks
-        if len(role_map.get("banner", [])) > 1:
-            for el in role_map["banner"]:
-                self.results.append(SemanticAudit.issue(
-                    "LAND_003",
-                    "Multiple banner landmarks",
-                    el,
-                    "More than one 'banner' landmark found."
-                ))
-
-        # LAND_004 — Multiple contentinfo landmarks
-        if len(role_map.get("contentinfo", [])) > 1:
-            for el in role_map["contentinfo"]:
-                self.results.append(SemanticAudit.issue(
-                    "LAND_004",
-                    "Multiple contentinfo landmarks",
-                    el,
-                    "More than one 'contentinfo' landmark found."
-                ))
-
-        # LAND_005 — Multiple same-type landmarks without labels
-        for role, elements in role_map.items():
-            if len(elements) > 1:
-                for el in elements:
-                    if not el.get("aria-label") and not el.get("aria-labelledby"):
-                        self.results.append(SemanticAudit.issue(
-                            "LAND_005",
-                            f"Multiple '{role}' landmarks without accessible labels",
-                            el,
-                            f"Multiple '{role}' landmarks should have distinguishing aria-label or aria-labelledby attributes."
-                        ))
-
-        # LAND_006 — Content outside landmark regions
-        landmark_elements = [el for el, _ in landmarks]
-        body = self.soup.find("body")
-
-        if body:
-            for child in body.find_all(recursive=False):
-                if child not in landmark_elements and child.name not in ["script", "style"]:
-                    if child.get_text(strip=True):
-                        self.results.append(SemanticAudit.issue(
-                            "LAND_006",
-                            "Content outside landmark regions",
-                            child,
-                            "Content found directly under <body> that is not contained within a landmark region."
-                        ))
-                        break
-    
-    def set_headings(self):
-        headings = self.soup.find_all(re.compile("^h[1-6]$"))
-        previous_level = 0
-
-        # HEAD_001 — Skipped heading levels
-        for h in headings:
-            level = int(h.name[1])
-            if previous_level and level > previous_level + 1:
-                self.results.append(SemanticAudit.issue(
-                    "HEAD_001",
-                    "Skipped heading level",
-                    h,
-                    "Heading level skipped (e.g., h2 to h4)."
-                ))
-            previous_level = level
-
-        h1_elements = [h for h in headings if h.name == "h1"]
-
-        # HEAD_003 — Missing <h1>
-        if len(h1_elements) == 0:
-            body = self.soup.find("body")
-            self.results.append(SemanticAudit.issue(
-                "HEAD_003",
-                "Missing <h1> element",
-                body,
-                "The page does not contain an <h1> element."
-            ))
-
-        # HEAD_002 — Multiple <h1>
-        if len(h1_elements) > 1:
-            for h in h1_elements:
-                self.results.append(SemanticAudit.issue(
-                    "HEAD_002",
-                    "Multiple <h1> elements",
-                    h,
-                    "More than one <h1> found."
-                ))
-
-        # HEAD_004 — Empty heading
-        for h in headings:
-            if not h.get_text(strip=True):
-                self.results.append(SemanticAudit.issue(
-                    "HEAD_004",
-                    "Empty heading",
-                    h,
-                    "Heading element contains no text."
-                ))
-
-    def set_links(self):
-        for a in self.soup.find_all("a"):
-
-            # LINK_001 — Missing accessible name
-            if not a.get_text(strip=True) and not a.get("aria-label"):
-                self.results.append(SemanticAudit.issue(
-                    "LINK_001",
-                    "Link without accessible name",
-                    a,
-                    "Link has no text or aria-label."
-                ))
-
-            # LINK_002 — Anchor without href
-            if not a.get("href"):
-                self.results.append(SemanticAudit.issue(
-                    "LINK_002",
-                    "Anchor without href",
-                    a,
-                    "Anchor element does not contain an href attribute."
-                ))
-
-    def set_skip_links(self):
-        skip_links = []
-
-        # Identify potential skip links (anchor with internal target and "skip" text)
-        for a in self.soup.find_all("a"):
-            href = a.get("href")
-            text = a.get_text(strip=True).lower()
-
-            if href and href.startswith("#") and "skip" in text:
-                skip_links.append(a)
-
-        # NAV_001 — Skip link not present
-        if not skip_links:
-            body = self.soup.find("body")
-            self.results.append(SemanticAudit.issue(
-                "NAV_001",
-                "Skip link not present",
-                body,
-                "Page does not contain a skip navigation link."
-            ))
-        else:
-            # Validate each skip link
-            for skip in skip_links:
-                target_id = skip.get("href")[1:]
-                target_element = self.soup.find(id=target_id)
-
-                # NAV_002 — Skip link target missing
-                if not target_element:
-                    self.results.append(SemanticAudit.issue(
-                        "NAV_002",
-                        "Skip link target does not exist",
-                        skip,
-                        f"Skip link points to '#{target_id}' but no element with that ID exists."
-                    ))
-
-            # NAV_003 — Skip link is not first focusable element
-            focusable_elements = []
-
-            for el in self.soup.find_all(True):
-                if el.name in ["a", "button", "input", "select", "textarea"]:
-                    if el.name != "a" or el.get("href"):
-                        focusable_elements.append(el)
-                elif el.get("tabindex") and el.get("tabindex").isdigit():
-                    if int(el.get("tabindex")) >= 0:
-                        focusable_elements.append(el)
-
-            if focusable_elements:
-                first_focusable = focusable_elements[0]
-                if first_focusable not in skip_links:
-                    self.results.append(SemanticAudit.issue(
-                        "NAV_003",
-                        "Skip link is not first focusable element",
-                        first_focusable,
-                        "The first focusable element on the page is not a skip navigation link."
-                    ))
-
-    def set_tab_index(self):
-        # FOCUS_001 — Positive tabindex used
-        for el in self.soup.find_all(attrs={"tabindex": True}):
-            val = el.get("tabindex")
-            if val.isdigit() and int(val) > 0:
-                self.results.append(SemanticAudit.issue(
-                    "FOCUS_001",
-                    "Positive tabindex used",
-                    el,
-                    "tabindex greater than 0 should not be used."
-                ))
-
-    def set_tables(self):
-        for table in self.soup.find_all("table"):
-
-            # TABLE_001 — Missing caption
-            if not table.find("caption"):
-                self.results.append(SemanticAudit.issue(
-                    "TABLE_001",
-                    "Missing table caption",
-                    table,
-                    "Data table does not contain a <caption>."
-                ))
-
-            # TABLE_002 — Missing headers
-            if not table.find("th"):
-                self.results.append(SemanticAudit.issue(
-                    "TABLE_002",
-                    "Missing table headers",
-                    table,
-                    "Table does not contain <th> elements."
-                ))
-    
-    def set_iframes(self):
-        for iframe in self.soup.find_all("iframe"):
-
-            # IFRAME_001 — Missing title
-            title = iframe.get("title")
-            if not title:
-                self.results.append(SemanticAudit.issue(
-                    "IFRAME_001",
-                    "Missing iframe title",
-                    iframe,
-                    "Iframe does not have a title attribute."
-                ))
-
-            # IFRAME_002 — Empty title
-            elif not title.strip():
-                self.results.append(SemanticAudit.issue(
-                    "IFRAME_002",
-                    "Empty iframe title",
-                    iframe,
-                    "Iframe title attribute is empty."
-                ))
-    
-    def set_duplicate_ids(self):
-        ids = {}
-        for el in self.soup.find_all(attrs={"id": True}):
-            id_val = el.get("id")
-            if id_val in ids:
-                self.results.append(SemanticAudit.issue(
-                    "PARSE_001",
-                    "Duplicate ID",
-                    el,
-                    f"Duplicate id '{id_val}' found."
-                ))
-            else:
-                ids[id_val] = el
-
-    def run_audit(self):
-        self.set_titles()
-        self.set_lang()
-        self.set_landmark()
-        self.set_headings()
-        self.set_links()
-        self.set_skip_links()
-        self.set_tab_index()
-        self.set_tables()
-        self.set_iframes()
-        self.set_duplicate_ids()
-        return self.results
-    
-if __name__=='__main__':
-    if len(sys.argv) != 2:
-        print("Usage: python audit.py <file.html>")
-        sys.exit(1)
-
-    file_path = sys.argv[1]
-
-    if not os.path.exists(file_path):
-        print(f"Error: File '{file_path}' not found.")
-        sys.exit(1)
-
-    semantic_audit = SemanticAudit(file_path=file_path)
-    results = semantic_audit.run_audit()
-
-    print("\n=== SEMANTIC ACCESSIBILITY AUDIT RESULTS ===\n")
-
-    semantic_audit.parse_results(results)
-
-    sys.exit(2)
\ No newline at end of file