fix: pre-process CLI clipboard images through vision tool instead of raw embedding

Images pasted in the CLI were embedded as raw base64 image_url content
parts in the conversation history, which only works with vision-capable
models. If the main model (e.g. Nous API) doesn't support vision, this
breaks the request and poisons all subsequent messages.

Now the CLI uses the same approach as the messaging gateway: images are
pre-processed through the auxiliary vision model (Gemini Flash via
OpenRouter or Nous Portal) and converted to text descriptions. The
local file path is included so the agent can re-examine via
vision_analyze if needed. Works with any model.

Fixes #638.

cli.py

@@ -1319,32 +1319,68 @@ class HermesCLI:
         else:
             _cprint(f"  {_DIM}(._.) No image found in clipboard{_RST}")
 
-    def _build_multimodal_content(self, text: str, images: list) -> list:
-        """Convert text + image paths into OpenAI vision multimodal content.
+    def _preprocess_images_with_vision(self, text: str, images: list) -> str:
+        """Analyze attached images via the vision tool and return enriched text.
 
-        Returns a list of content parts suitable for the ``content`` field
-        of a ``user`` message.
+        Instead of embedding raw base64 ``image_url`` content parts in the
+        conversation (which only works with vision-capable models), this
+        pre-processes each image through the auxiliary vision model (Gemini
+        Flash) and prepends the descriptions to the user's message — the
+        same approach the messaging gateway uses.
+
+        The local file path is included so the agent can re-examine the
+        image later with ``vision_analyze`` if needed.
         """
-        import base64 as _b64
+        import asyncio as _asyncio
+        import json as _json
+        from tools.vision_tools import vision_analyze_tool
 
-        content_parts = []
-        text_part = text if isinstance(text, str) and text else "What do you see in this image?"
-        content_parts.append({"type": "text", "text": text_part})
+        analysis_prompt = (
+            "Describe everything visible in this image in thorough detail. "
+            "Include any text, code, data, objects, people, layout, colors, "
+            "and any other notable visual information."
+        )
 
-        _MIME = {
-            "png": "image/png", "jpg": "image/jpeg", "jpeg": "image/jpeg",
-            "gif": "image/gif", "webp": "image/webp",
-        }
+        enriched_parts = []
         for img_path in images:
-            if img_path.exists():
-                data = _b64.b64encode(img_path.read_bytes()).decode()
-                ext = img_path.suffix.lower().lstrip(".")
-                mime = _MIME.get(ext, "image/png")
-                content_parts.append({
-                    "type": "image_url",
-                    "image_url": {"url": f"data:{mime};base64,{data}"}
-                })
-        return content_parts
+            if not img_path.exists():
+                continue
+            size_kb = img_path.stat().st_size // 1024
+            _cprint(f"  {_DIM}👁️  analyzing {img_path.name} ({size_kb}KB)...{_RST}")
+            try:
+                result_json = _asyncio.run(
+                    vision_analyze_tool(image_url=str(img_path), user_prompt=analysis_prompt)
+                )
+                result = _json.loads(result_json)
+                if result.get("success"):
+                    description = result.get("analysis", "")
+                    enriched_parts.append(
+                        f"[The user attached an image. Here's what it contains:\n{description}]\n"
+                        f"[If you need a closer look, use vision_analyze with "
+                        f"image_url: {img_path}]"
+                    )
+                    _cprint(f"  {_DIM}✓ image analyzed{_RST}")
+                else:
+                    enriched_parts.append(
+                        f"[The user attached an image but it couldn't be analyzed. "
+                        f"You can try examining it with vision_analyze using "
+                        f"image_url: {img_path}]"
+                    )
+                    _cprint(f"  {_DIM}⚠ vision analysis failed — path included for retry{_RST}")
+            except Exception as e:
+                enriched_parts.append(
+                    f"[The user attached an image but analysis failed ({e}). "
+                    f"You can try examining it with vision_analyze using "
+                    f"image_url: {img_path}]"
+                )
+                _cprint(f"  {_DIM}⚠ vision analysis error — path included for retry{_RST}")
+
+        # Combine: vision descriptions first, then the user's original text
+        user_text = text if isinstance(text, str) and text else ""
+        if enriched_parts:
+            prefix = "\n\n".join(enriched_parts)
+            return f"{prefix}\n\n{user_text}" if user_text else prefix
+        return user_text or "What do you see in this image?"
 
     def _show_tool_availability_warnings(self):
         """Show warnings about disabled tools due to missing API keys."""
@@ -2627,14 +2663,13 @@ class HermesCLI:
         if not self._init_agent():
             return None
         
-        # Convert attached images to OpenAI vision multimodal content
+        # Pre-process images through the vision tool (Gemini Flash) so the
+        # main model receives text descriptions instead of raw base64 image
+        # content — works with any model, not just vision-capable ones.
         if images:
-            message = self._build_multimodal_content(
+            message = self._preprocess_images_with_vision(
                 message if isinstance(message, str) else "", images
             )
-            for img_path in images:
-                if img_path.exists():
-                    _cprint(f"  {_DIM}📎 attached {img_path.name} ({img_path.stat().st_size // 1024}KB){_RST}")
 
         # Add user message to history
         self.conversation_history.append({"role": "user", "content": message})