Restore MSR character analyze config and parsing

js/msr_character.js

@@ -92,6 +92,9 @@ function setWidgetValue(node, widget, value) {
 function syncFormFromWidgets(node) {
   const description = findWidget(node, "description")?.value || "";
   const alias = findWidget(node, "alias")?.value || "";
+  const provider = findWidget(node, "analyze_provider")?.value || "ollama";
+  const model = findWidget(node, "analyze_model")?.value || "";
+  const baseUrl = findWidget(node, "analyze_base_url")?.value || "";
 
   if (node._msrAliasInput && node._msrAliasInput.value !== alias) {
     node._msrAliasInput.value = alias;
@@ -104,6 +107,7 @@ function syncFormFromWidgets(node) {
       alias
         ? `Alias: <span style="color:#e8e8e8">@${String(alias).replace(/^@/, "")}</span>`
         : "Alias: <span style=\"color:#666\">none</span>",
+      `<div style="margin-top:4px;color:#888">Analyze: ${provider}${model ? ` / ${model}` : ""}${baseUrl ? ` / ${baseUrl}` : ""}</div>`,
       description
         ? `<div style="margin-top:4px;color:#d0d0d0">${description}</div>`
         : `<div style="margin-top:4px;color:#666">Description will be used for prompt replacement outside MSR prefix mode.</div>`,
@@ -189,6 +193,36 @@ function buildCharacterUi(node) {
     gap: "6px",
   });
 
+  const settingsButton = document.createElement("button");
+  settingsButton.type = "button";
+  settingsButton.textContent = "Analyze Settings";
+  Object.assign(settingsButton.style, {
+    alignSelf: "flex-start",
+    background: "#252525",
+    color: "#ddd",
+    border: "1px solid #444",
+    borderRadius: "6px",
+    padding: "6px 10px",
+    fontSize: "11px",
+    cursor: "pointer",
+  });
+  settingsButton.addEventListener("click", () => {
+    const providerWidget = findWidget(node, "analyze_provider");
+    const baseUrlWidget = findWidget(node, "analyze_base_url");
+    const modelWidget = findWidget(node, "analyze_model");
+    const provider = window.prompt("Analyze provider: ollama, lmstudio, custom, off", providerWidget?.value || "ollama");
+    if (provider == null) return;
+    const baseUrl = window.prompt("Analyze base URL (blank = default)", baseUrlWidget?.value || "");
+    if (baseUrl == null) return;
+    const model = window.prompt("Analyze model (blank = provider default)", modelWidget?.value || "");
+    if (model == null) return;
+    setWidgetValue(node, providerWidget, provider.trim() || "ollama");
+    setWidgetValue(node, baseUrlWidget, baseUrl.trim());
+    setWidgetValue(node, modelWidget, model.trim());
+    syncFormFromWidgets(node);
+    node.setDirtyCanvas?.(true, true);
+  });
+
   const aliasLabel = document.createElement("label");
   aliasLabel.textContent = "Alias";
   Object.assign(aliasLabel.style, {
@@ -245,6 +279,7 @@ function buildCharacterUi(node) {
   form.appendChild(aliasInput);
   form.appendChild(descLabel);
   form.appendChild(descInput);
+  form.appendChild(settingsButton);
 
   container.appendChild(previewRow);
   container.appendChild(meta);
@@ -261,6 +296,9 @@ function buildCharacterUi(node) {
 async function analyzeCharacterNode(node, buttonWidget) {
   const descriptionWidget = findWidget(node, "description");
   if (!descriptionWidget) return;
+  const providerWidget = findWidget(node, "analyze_provider");
+  const baseUrlWidget = findWidget(node, "analyze_base_url");
+  const modelWidget = findWidget(node, "analyze_model");
 
   buttonWidget.label = "Analyzing...";
   node.setDirtyCanvas?.(true, true);
@@ -280,7 +318,9 @@ async function analyzeCharacterNode(node, buttonWidget) {
     const response = await api.fetchApi("/ltx_director/analyze_character", {
       method: "POST",
       body: JSON.stringify({
-        provider: "ollama",
+        provider: providerWidget?.value || "ollama",
+        base_url: baseUrlWidget?.value || "",
+        model: modelWidget?.value || "",
         image_b64: images,
       }),
     });
@@ -332,6 +372,18 @@ app.registerExtension({
           buttonWidget.label = "Analyze with Ollama";
         }
 
+        if (!findWidget(this, "analyze_provider")) {
+          this.addWidget("combo", "analyze_provider", "ollama", null, {
+            values: ["ollama", "lmstudio", "custom", "off"],
+          });
+        }
+        if (!findWidget(this, "analyze_base_url")) {
+          this.addWidget("text", "analyze_base_url", "");
+        }
+        if (!findWidget(this, "analyze_model")) {
+          this.addWidget("text", "analyze_model", "");
+        }
+
         if (!this._msrPreviewWidget) {
           const previewContainer = buildCharacterUi(this);
           this._msrPreviewWidget = this.addDOMWidget("msr_character_ui", "msr_character_ui", previewContainer, {
@@ -358,7 +410,7 @@ app.registerExtension({
           return result;
         };
 
-        ["alias", "description"].forEach((widgetName) => {
+        ["alias", "description", "analyze_provider", "analyze_base_url", "analyze_model"].forEach((widgetName) => {
           hideNodeWidget(findWidget(this, widgetName));
         });
 

ltx_director.py

@@ -445,6 +445,27 @@ _ANALYZE_PROMPT = (
 )
 
 
+def _extract_ollama_generated_text(resp_json: dict) -> str:
+    if not isinstance(resp_json, dict):
+        return ""
+
+    candidates = [resp_json.get("response"), resp_json.get("content"), resp_json.get("thinking")]
+    message = resp_json.get("message")
+    if isinstance(message, dict):
+        candidates.extend([
+            message.get("content"),
+            message.get("reasoning_content"),
+            message.get("thinking"),
+        ])
+
+    for candidate in candidates:
+        if isinstance(candidate, str):
+            text = candidate.strip()
+            if text:
+                return text
+    return ""
+
+
 def _compress_analysis_image_b64(b64_payload: str, max_dim: int = 768, quality: int = 82) -> str:
     """Shrink analysis images so multimodal providers do not burn their full context on pixels."""
     try:
@@ -531,12 +552,12 @@ async def analyze_character_endpoint(request):
                                         retry_err = await retry_response.text()
                                         return web.json_response({"status": "error", "message": f"Ollama HTTP {retry_response.status}: {retry_err}"})
                                     resp_json = await retry_response.json()
-                                    generated_text = (resp_json.get("response") or "").strip()
+                                    generated_text = _extract_ollama_generated_text(resp_json)
                             else:
                                 return web.json_response({"status": "error", "message": f"Ollama HTTP {response.status}: {err_txt}"})
                         else:
                             resp_json = await response.json()
-                            generated_text = (resp_json.get("response") or "").strip()
+                            generated_text = _extract_ollama_generated_text(resp_json)
                 else:
                     # OpenAI-compatible vision chat (LM Studio / Custom).
                     content = [{"type": "text", "text": _ANALYZE_PROMPT}]