feat: file attachment support in chat (images + text/code files)

Text files (.md, .py, .js, .json, etc.): read client-side and injected
into the message body as a fenced code block — works with all backends
with zero model capability requirements.

Images (PNG/JPG/WebP/GIF, max 5 MB): encoded as base64 data URL on the
client and sent as a separate attachment field. Backend formats them as
OpenAI multimodal content (text + image_url) for local_openai backends.
Claude CLI and Gemini CLI see the text message with a "📎 filename.png"
note; image data is never written to session history.

- index.html: 📎 button + hidden file input in mode-select row;
  attachment-row preview area with thumbnail (images) or filename chip
- app.js: _resolveAttachment(), file reader, clearAttachment();
  sendMessage/sendOrchestrate updated to allow no-text sends when a
  file is pending; attachment spread into chat payload for images
- chat.py: Attachment model; attachment field on ChatRequest;
  llm_attachment extracted in _stream_chat and passed to complete()
- llm_client.py: attachment param through complete()/_dispatch()/_local();
  _local() builds multimodal content array for vision calls
- style.css: #attach-btn, #attachment-row, #attachment-preview, thumb

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

cortex/llm_client.py

@@ -51,6 +51,7 @@ async def complete(
     role: str = "chat",
     slot: str | None = None,
     max_tokens: int = 2048,
+    attachment: dict | None = None,
 ) -> tuple[str, str]:
     """
     Returns (response_text, actual_backend_used).
@@ -96,7 +97,7 @@ async def complete(
     fallback = _FALLBACK.get(primary, "claude")
 
     try:
-        response = await _dispatch(primary, system_prompt, messages, resolved_cfg)
+        response = await _dispatch(primary, system_prompt, messages, resolved_cfg, attachment=attachment)
         return response, primary
     except Exception as e:
         err_str = str(e)
@@ -116,11 +117,12 @@ async def _dispatch(
     system_prompt: str,
     messages: list[dict],
     model_cfg: dict | None,
+    attachment: dict | None = None,
 ) -> str:
     if backend == "gemini":
         return await _gemini(system_prompt, messages)
     if backend == "local":
-        return await _local(system_prompt, messages, model_cfg)
+        return await _local(system_prompt, messages, model_cfg, attachment=attachment)
     return await _claude(system_prompt, messages, model_cfg)
 
 
@@ -166,11 +168,17 @@ async def _claude(system_prompt: str, messages: list[dict], model_cfg: dict | No
     return await _run(cmd, timeout=settings.timeout_claude, env=env)
 
 
-async def _local(system_prompt: str, messages: list[dict], model_cfg: dict | None = None) -> str:
+async def _local(
+    system_prompt: str,
+    messages: list[dict],
+    model_cfg: dict | None = None,
+    attachment: dict | None = None,
+) -> str:
     """OpenAI-compatible backend — Open WebUI / Ollama.
 
     model_cfg is pre-resolved by complete() via model_registry.
     Falls back to registry lookup if not provided.
+    attachment: optional image dict {filename, mime_type, data} for vision calls.
     """
     import httpx
 
@@ -200,8 +208,20 @@ async def _local(system_prompt: str, messages: list[dict], model_cfg: dict | Non
     msgs: list[dict] = []
     if system_prompt:
         msgs.append({"role": "system", "content": system_prompt})
-    # Strip any non-standard metadata fields before sending to the API
-    msgs.extend({"role": m["role"], "content": m["content"]} for m in messages)
+
+    # Build message list; inject image into the last user message when present.
+    for i, m in enumerate(messages):
+        is_last = (i == len(messages) - 1)
+        if is_last and m["role"] == "user" and attachment:
+            content: list[dict] = [{"type": "text", "text": m["content"]}]
+            content.append({
+                "type": "image_url",
+                "image_url": {"url": attachment["data"]},
+            })
+            msgs.append({"role": "user", "content": content})
+        else:
+            # Strip non-standard metadata fields before sending to the API
+            msgs.append({"role": m["role"], "content": m["content"]})
 
     url = api_url.rstrip("/") + chat_path
     headers: dict[str, str] = {}