feat(ai): stream explanations live (Ollama NDJSON + Claude SSE) — 0.33.0

ai.explain_stream(findings_text, on_chunk) streams token deltas and returns
(ok, full_text). Ollama: stream=True NDJSON; Claude: stream=True SSE (parse
content_block_delta text deltas). The diagnostic dialog opens an explanation
window immediately and fills it token-by-token via a _chunk signal, then
re-renders the finished answer as Markdown — no more multi-second freeze on a
local model. Non-streaming explain() kept for the CLI. Tests for both parsers;
verified live against qwen2.5:7b.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/rigdoctor/__init__.py

@@ -1,3 +1,3 @@
 """RigDoctor — modular hardware monitoring & crash diagnostics for Linux gamers."""
 
-__version__ = "0.32.0"
+__version__ = "0.33.0"

src/rigdoctor/core/ai.py

@@ -150,6 +150,24 @@ def explain(findings_text: str, timeout: float = 120.0) -> tuple[bool, str]:
         return False, f"Unexpected response from the AI provider: {exc}"
 
 
+def explain_stream(findings_text: str, on_chunk, timeout: float = 180.0) -> tuple[bool, str]:
+    """Like :func:`explain`, but calls ``on_chunk(text_delta)`` as tokens arrive and returns
+    ``(ok, full_text)`` at the end. Caller MUST be a direct user action (D24)."""
+    content = build_prompt(findings_text)
+    try:
+        if provider() == "claude":
+            return _claude_stream(content, on_chunk, timeout)
+        if provider() == "ollama":
+            return _ollama_stream(content, on_chunk, timeout)
+        return False, "No AI provider is configured (Settings → AI assistant)."
+    except urllib.error.HTTPError as exc:
+        return False, _http_error(exc)
+    except (urllib.error.URLError, OSError, TimeoutError) as exc:
+        return False, f"Couldn't reach the AI provider: {exc}"
+    except (ValueError, KeyError, IndexError) as exc:
+        return False, f"Unexpected response from the AI provider: {exc}"
+
+
 def _post(url: str, payload: dict, headers: dict, timeout: float) -> dict:
     req = urllib.request.Request(
         url, data=json.dumps(payload).encode("utf-8"),
@@ -185,6 +203,65 @@ def _claude(content: str, timeout: float) -> tuple[bool, str]:
     return True, text.strip() or "(the model returned no text)"
 
 
+def _stream_request(url: str, payload: dict, headers: dict, timeout: float):
+    req = urllib.request.Request(
+        url, data=json.dumps(payload).encode("utf-8"),
+        headers={"Content-Type": "application/json", **headers})
+    return urllib.request.urlopen(req, timeout=timeout)
+
+
+def _ollama_stream(content: str, on_chunk, timeout: float) -> tuple[bool, str]:
+    if not model():
+        return False, "No Ollama model is set (Settings → AI assistant)."
+    payload = {"model": model(), "system": SYSTEM_PROMPT, "prompt": content, "stream": True}
+    parts: list[str] = []
+    with _stream_request(endpoint().rstrip("/") + "/api/generate", payload, {}, timeout) as resp:
+        for raw in resp:  # newline-delimited JSON objects
+            line = raw.decode("utf-8", "replace").strip()
+            if not line:
+                continue
+            obj = json.loads(line)
+            chunk = obj.get("response", "")
+            if chunk:
+                parts.append(chunk)
+                on_chunk(chunk)
+            if obj.get("done"):
+                break
+    return True, "".join(parts).strip() or "(the model returned an empty response)"
+
+
+def _claude_stream(content: str, on_chunk, timeout: float) -> tuple[bool, str]:
+    key = config.load_ai_key()
+    if not key:
+        return False, "No Claude API key is set (Settings → AI assistant)."
+    payload = {
+        "model": model(), "max_tokens": CLAUDE_MAX_TOKENS, "system": SYSTEM_PROMPT,
+        "messages": [{"role": "user", "content": content}], "stream": True,
+    }
+    headers = {"x-api-key": key, "anthropic-version": ANTHROPIC_VERSION}
+    parts: list[str] = []
+    with _stream_request(CLAUDE_ENDPOINT, payload, headers, timeout) as resp:
+        for raw in resp:  # SSE: parse `data:` lines, accumulate text deltas
+            line = raw.decode("utf-8", "replace").strip()
+            if not line.startswith("data:"):
+                continue
+            try:
+                event = json.loads(line[5:].strip())
+            except ValueError:
+                continue
+            etype = event.get("type")
+            if etype == "content_block_delta" and event.get("delta", {}).get("type") == "text_delta":
+                chunk = event["delta"].get("text", "")
+                if chunk:
+                    parts.append(chunk)
+                    on_chunk(chunk)
+            elif etype == "error":
+                return False, event.get("error", {}).get("message", "stream error")
+            elif etype == "message_stop":
+                break
+    return True, "".join(parts).strip() or "(the model returned no text)"
+
+
 def _http_error(exc: urllib.error.HTTPError) -> str:
     detail = ""
     try:

src/rigdoctor/gui/diagnostic_dialog.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 import threading
 
 from PySide6.QtCore import Qt, Signal
-from PySide6.QtGui import QFont
+from PySide6.QtGui import QFont, QTextCursor
 from PySide6.QtWidgets import (
     QDialog,
     QFrame,
@@ -24,11 +24,15 @@ from .widgets import finding_card
 
 
 class DiagnosticDialog(QDialog):
-    _explained = Signal(object)  # (ok, text) from a user-triggered AI explanation
+    _chunk = Signal(str)         # streamed token delta (worker thread -> GUI)
+    _explained = Signal(object)  # (ok, full_text) when the AI stream finishes
 
     def __init__(self, result, parent=None) -> None:
         super().__init__(parent)
         self._result = result
+        self._stream_view = None
+        self._stream_status = None
+        self._chunk.connect(self._on_chunk)
         self._explained.connect(self._on_explained)
         self.setWindowTitle(f"Diagnostic — {result.game}" if result.game else "Diagnostic")
         self.resize(660, 680)
@@ -97,7 +101,7 @@ class DiagnosticDialog(QDialog):
         buttons.addWidget(close)
         root.addLayout(buttons)
 
-    # --- AI explanation (M14, D24) — runs only on this button press ----------------
+    # --- AI explanation (M14, D24) — streamed; runs only on this button press ----------
     def _explain_with_ai(self) -> None:
         from ..core import ai
 
@@ -111,8 +115,11 @@ class DiagnosticDialog(QDialog):
             if confirm != QMessageBox.StandardButton.Yes:
                 return
         self._explain_btn.setEnabled(False)
-        self._explain_btn.setText("Asking the AI…")
+        dialog = self._open_stream_dialog()
         threading.Thread(target=self._work_explain, daemon=True).start()
+        dialog.exec()  # streaming fills the view live via signals during this nested loop
+        self._stream_view = self._stream_status = None
+        self._explain_btn.setEnabled(True)
 
     def _work_explain(self) -> None:
         from ..core import ai, gamelogs, syslogs
@@ -143,7 +150,8 @@ class DiagnosticDialog(QDialog):
         if sys_logs:
             lines.append("\nSystem logs for this session (kernel + crashed processes):\n" + sys_logs)
         text = "\n".join(lines)
-        ok, reply = ai.explain(text)
+
+        ok, reply = ai.explain_stream(text, on_chunk=lambda d: self._chunk.emit(d))
         if result.dir:  # record exactly what was sent, the model, and the reply (M15)
             from ..core import diagstore
             diagstore.record_ai(
@@ -152,11 +160,24 @@ class DiagnosticDialog(QDialog):
                 response=reply if ok else f"[error] {reply}")
         self._explained.emit((ok, reply))
 
+    def _on_chunk(self, delta: str) -> None:
+        if self._stream_view is None:
+            return
+        self._stream_view.moveCursor(QTextCursor.MoveOperation.End)
+        self._stream_view.insertPlainText(delta)  # live plain text as tokens arrive
+        self._stream_view.ensureCursorVisible()
+
     def _on_explained(self, result) -> None:
         ok, text = result
-        self._explain_btn.setEnabled(True)
-        self._explain_btn.setText("Explain with AI")
-        self._show_explanation(text if ok else f"AI explanation failed:\n\n{text}")
+        if self._stream_view is not None:
+            if ok:
+                self._stream_view.setMarkdown(text)  # re-render the finished answer as Markdown
+            else:
+                self._stream_view.setPlainText(f"AI explanation failed:\n\n{text}")
+        if self._stream_status is not None:
+            self._stream_status.setText(
+                "AI-generated suggestions — verify before acting, especially anything that changes "
+                "settings or data." if ok else "The request failed.")
 
     # --- Report bundle (M15) ------------------------------------------------------
     def _make_report(self) -> None:
@@ -183,7 +204,8 @@ class DiagnosticDialog(QDialog):
         if box.clickedButton() is open_btn:
             QDesktopServices.openUrl(QUrl.fromLocalFile(str(out.parent)))
 
-    def _show_explanation(self, text: str) -> None:
+    def _open_stream_dialog(self) -> QDialog:
+        """A live dialog the AI streams into; finalized to rendered Markdown when done."""
         from ..core import ai
 
         dlg = QDialog(self)
@@ -193,14 +215,15 @@ class DiagnosticDialog(QDialog):
         view = QTextEdit()
         view.setObjectName("Report")
         view.setReadOnly(True)
-        view.setMarkdown(text)  # the model replies in Markdown — render it
         lay.addWidget(view)
-        note = QLabel("AI-generated suggestions — verify before acting, especially anything that changes settings or data.")
-        note.setObjectName("Muted")
-        note.setWordWrap(True)
-        lay.addWidget(note)
+        status = QLabel("Streaming from the model…")
+        status.setObjectName("Muted")
+        status.setWordWrap(True)
+        lay.addWidget(status)
         close = QPushButton("Close")
         close.setObjectName("PrimaryButton")
         close.clicked.connect(dlg.accept)
         lay.addWidget(close, alignment=Qt.AlignmentFlag.AlignRight)
-        dlg.exec()
+        self._stream_view = view
+        self._stream_status = status
+        return dlg