entry_tutor_explain_entries.py

from __future__ import annotations

from .entry_common import (
    Any,
    Err,
    Ok,
    SdkError,
    _entry_exception_error,
    _normalize_submitted_image_payload,
    _validate_optional_vision_image_payload,
    _plugin_lock,
    plugin_entry,
    tr,
    LLM_OPERATION_CONCEPT_EXPLAIN,
    MODE_COMPANION,
    MODE_CONCEPT_EXPLAIN,
    handle_user_intent,
)


class _TutorExplainEntriesMixin:
    @plugin_entry(
        id="study_submit_image",
        name=tr("entries.submit_image.name", default="Submit Study Image"),
        description=tr(
            "entries.submit_image.description",
            default="Accept a user image and explain it with the configured vision model.",
        ),
        input_schema={
            "type": "object",
            "properties": {
                "image_base64": {"type": "string"},
                "text": {"type": "string", "default": ""},
            },
            "required": ["image_base64"],
        },
        timeout=60.0,
        llm_result_fields=["summary", "reply", "diagnostic"],
    )
    async def study_submit_image(self, image_base64: str, text: str = "", **_):
        try:
            image_payload = _normalize_submitted_image_payload(image_base64)
        except ValueError as exc:
            return _entry_exception_error(self, exc, operation="study_submit_image")
        if not bool(self._cfg.llm_vision_enabled):
            return Err(SdkError("llm_vision_enabled is not enabled"))
        normalized_text = str(text or "").strip()
        if normalized_text:
            async with _plugin_lock(self._lock):
                self._state.last_ocr_text = normalized_text
        source_text = normalized_text or "请查看这张图片的内容"
        return await self.study_explain_text(
            text=source_text,
            vision_image_base64=image_payload,
        )

    @plugin_entry(
        id="study_explain_text",
        name=tr("entries.explain_text.name", default="Explain Study Text"),
        description=tr(
            "entries.explain_text.description",
            default="Explain a concept from supplied text, or use the latest OCR text if text is omitted.",
        ),
        input_schema={
            "type": "object",
            "properties": {
                "text": {"type": "string", "default": ""},
                "vision_image_base64": {"type": "string", "default": ""},
            },
        },
        timeout=45.0,
        llm_result_fields=["summary", "reply", "diagnostic"],
    )
    async def study_explain_text(
        self, text: str = "", vision_image_base64: str = "", **_
    ):
        if self._agent is None:
            return Err(SdkError("study tutor agent is not initialized"))
        raw_text = str(text or "").strip()
        # Phase 1: detect an explicit mode intent and switch first when present.
        intent = (
            handle_user_intent(raw_text, language=self._cfg.language)
            if raw_text
            else {
                "matched": False,
                "pure_switch": False,
                "mode": "",
                "remaining_text": "",
            }
        )
        async with _plugin_lock(self._lock):
            active_mode = self._state.active_mode
        mode_switch: dict[str, Any] = {}
        if intent.get("matched") and intent.get("kind") == "mode_switch":
            try:
                mode_switch = await self._apply_mode_switch(
                    str(intent.get("mode") or MODE_COMPANION),
                    f"intent:{intent.get('keyword') or 'text'}",
                    language=self._cfg.language,
                )
                active_mode = str(mode_switch.get("new_mode") or active_mode)
            except ValueError as exc:
                return _entry_exception_error(self, exc, operation="study_explain_text")
            if intent.get("pure_switch"):
                transition_phrase = str(
                    mode_switch.get("transition_phrase")
                    or intent.get("transition_phrase")
                    or ""
                )
                return Ok(
                    {
                        **mode_switch,
                        "reply": transition_phrase,
                        "summary": transition_phrase,
                        "operation": MODE_CONCEPT_EXPLAIN,
                        "input_text": raw_text,
                        "degraded": False,
                    }
                )
        # Phase 2: resolve the text to explain.
        intent_kind = str(intent.get("kind") or "")
        source_text = str(intent.get("remaining_text") or "").strip()
        if not source_text and intent_kind != "concept_explain":
            source_text = raw_text
        used_ocr_fallback = False
        if not source_text:
            async with _plugin_lock(self._lock):
                source_text = self._state.last_ocr_text
            used_ocr_fallback = bool(source_text.strip())
        source_text = source_text.strip()
        vision_image_payload = str(vision_image_base64 or "").strip()
        if not source_text and not vision_image_payload:
            return Err(
                SdkError(
                    "study tutor requires text or a non-empty OCR snapshot",
                    code="MISSING_TEXT",
                )
            )
        # Phase 3: explain with the active mode selected above.
        try:
            extra_context: dict[str, Any] = {
                "source": "ocr_snapshot"
                if used_ocr_fallback or not raw_text
                else "manual",
                "mode": active_mode,
                "mode_switch": bool(mode_switch.get("changed")),
                "source_text": source_text,
            }
            if vision_image_payload:
                validated_vision_image = _validate_optional_vision_image_payload(
                    self, vision_image_payload, operation="study_explain_text"
                )
                if isinstance(validated_vision_image, Err):
                    return validated_vision_image
                vision_image_payload = validated_vision_image
                extra_context["vision_enabled"] = True
                extra_context["vision_image_base64"] = vision_image_payload
            tutor_context = await self._build_learning_context(
                LLM_OPERATION_CONCEPT_EXPLAIN,
                input_text=source_text,
                extra=extra_context,
            )
            reply = await self._agent.concept_explain(
                source_text,
                mode=active_mode,
                context=tutor_context,
            )
            payload = await self._finalize_tutor_call(
                LLM_OPERATION_CONCEPT_EXPLAIN,
                reply,
                history_kind=MODE_CONCEPT_EXPLAIN,
                metadata={
                    "degraded": reply.degraded,
                    "diagnostic": reply.diagnostic,
                    "mode": active_mode,
                    "mode_switch": mode_switch,
                    "intent": intent,
                    "screen_classification": tutor_context.get("screen_classification")
                    or {},
                },
                extra_context=tutor_context,
            )
            if mode_switch:
                payload["mode_switch"] = mode_switch
            if intent.get("matched"):
                payload["intent"] = intent
                if intent.get("pure_switch"):
                    payload["transition_phrase"] = str(
                        mode_switch.get("transition_phrase")
                        or intent.get("transition_phrase")
                        or ""
                    )
            return Ok(payload)
        except Exception as exc:
            return _entry_exception_error(self, exc, operation="study_explain_text")