from __future__ import annotations

import re
from pathlib import Path

from django.conf import settings

from review_agent.models import FileSummaryBatch, RegulatoryReviewBatch
from review_agent.regulatory_review.services.llm_review import review_condition_fields
from review_agent.regulatory_review.services.text_extract import extract_text


OPTION_FIELDS = {
    "product_category": ["体外诊断试剂", "医疗器械", "其他"],
    "registration_type": ["首次注册", "变更注册", "延续注册"],
    "clinical_evaluation_path": ["临床试验", "免临床", "同品种比对", "待确认"],
}


def ensure_regulatory_condition_candidates(batch: RegulatoryReviewBatch) -> dict[str, dict[str, object]]:
    condition_json = batch.condition_json or {}
    candidates = condition_json.get("candidates") or {}
    if batch.status != RegulatoryReviewBatch.Status.WAITING_USER or not _condition_candidates_incomplete(candidates):
        return candidates
    refreshed = detect_regulatory_condition_candidates(batch.source_summary_batch)
    refreshed = _merge_condition_candidates(candidates, refreshed)
    batch.condition_json = {**condition_json, "candidates": refreshed}
    batch.save(update_fields=["condition_json"])
    return refreshed


def detect_regulatory_condition_candidates(summary_batch: FileSummaryBatch) -> dict[str, dict[str, object]]:
    """Infers review-scope conditions from the summary batch and file names."""

    corpus_parts = [summary_batch.product_name or ""]
    field_candidates: dict[str, str] = {}
    field_sources: dict[str, str] = {}
    for item in summary_batch.items.order_by("file_index"):
        corpus_parts.extend([item.directory_level, item.file_name, item.relative_path])
        review = _extract_item_fields(item)
        extracted = review.get("selected_fields", {})
        sources = review.get("selected_sources", {})
        field_candidates.update({key: value for key, value in extracted.items() if value and key not in field_candidates})
        field_sources.update({key: value for key, value in sources.items() if value and key not in field_sources})
        corpus_parts.extend(extracted.values())
        if review.get("front_text"):
            corpus_parts.append(str(review["front_text"]))
    corpus = "\n".join(part for part in corpus_parts if part)
    product_name = field_candidates.get("产品名称") or _safe_summary_product_name(summary_batch.product_name)

    return {
        "product_category": {
            "label": "产品类别",
            "input_type": "select",
            "options": OPTION_FIELDS["product_category"],
            "suggested": _detect_product_category(corpus),
        },
        "registration_type": {
            "label": "注册类型",
            "input_type": "select",
            "options": OPTION_FIELDS["registration_type"],
            "suggested": _detect_registration_type(corpus),
        },
        "clinical_evaluation_path": {
            "label": "临床评价路径",
            "input_type": "select",
            "options": OPTION_FIELDS["clinical_evaluation_path"],
            "suggested": _detect_clinical_path(corpus),
        },
        "product_name": {
            "label": "产品名称",
            "input_type": "text",
            "suggested": product_name,
            "source": field_sources.get("产品名称", "summary" if product_name else ""),
        },
        "model_spec": {
            "label": "型号规格",
            "input_type": "text",
            "suggested": field_candidates.get("型号规格", ""),
            "source": field_sources.get("型号规格", ""),
        },
        "intended_use": {
            "label": "预期用途",
            "input_type": "text",
            "suggested": field_candidates.get("预期用途", ""),
            "source": field_sources.get("预期用途", ""),
        },
    }


def _extract_item_fields(item) -> dict[str, object]:
    path = Path(item.storage_path)
    if not path.is_absolute():
        path = Path(settings.MEDIA_ROOT) / item.storage_path
    if not path.exists():
        return {}
    result = extract_text(path)
    if result.status != "success" or not result.text:
        return {}
    inferred_fields = _infer_fields_from_text(result.front_text or result.text)
    rule_fields = {**inferred_fields, **(result.field_candidates or {})}
    review = review_condition_fields(
        text=result.front_text or result.text,
        rule_fields=rule_fields,
        file_context=f"{item.directory_level}\n{item.file_name}\n{item.relative_path}",
    )
    selected_sources = dict(review.get("selected_sources") or {})
    for key in inferred_fields:
        if selected_sources.get(key) == "rule" and key not in (result.field_candidates or {}):
            selected_sources[key] = "inferred"
    review["selected_sources"] = selected_sources
    review["front_text"] = result.front_text or result.text[:1200]
    return review


def _safe_summary_product_name(product_name: str) -> str:
    value = (product_name or "").strip()
    if not value:
        return ""
    if any(keyword in value for keyword in ["第1章", "第2章", "监管信息", "综述资料", "非临床资料", "章节目录"]):
        return ""
    return value


def _infer_fields_from_text(text: str) -> dict[str, str]:
    normalized = _normalize_text_for_inference(text)
    fields = {}
    product_name = _infer_product_name(normalized)
    if product_name:
        fields["产品名称"] = product_name
    model_spec = _infer_model_spec(normalized)
    if model_spec:
        fields["型号规格"] = model_spec
    return fields


def _normalize_text_for_inference(text: str) -> str:
    value = re.sub(r"\s+", "", text or "")
    value = value.replace("（", "(").replace("）", ")")
    return value


def _infer_product_name(text: str) -> str:
    patterns = [
        r"体外诊断试剂(?P<name>[^。；;，,]{4,120}?试剂盒\([^()]{2,30}\))产品注册",
        r"(?P<name>[^。；;，,]{4,120}?试剂盒\([^()]{2,30}\))",
    ]
    for pattern in patterns:
        match = re.search(pattern, text)
        if match:
            return _restore_chinese_parentheses(_trim_product_name(match.group("name")))
    return ""


def _trim_product_name(value: str) -> str:
    prefixes = ["申请境内第三类体外诊断试剂", "申请境内第二类体外诊断试剂", "境内第三类体外诊断试剂", "境内第二类体外诊断试剂"]
    result = value
    for prefix in prefixes:
        if prefix in result:
            result = result.split(prefix, 1)[-1]
    return result


def _infer_model_spec(text: str) -> str:
    specs = sorted(set(re.findall(r"规格[A-ZＡ-Ｚ]", text)))
    if specs:
        return "、".join(specs)
    match = re.search(r"产品的包装规格(?P<spec>.{1,80}?(?:人份/盒|测试/盒|反应/盒)(?:[、,，].{1,30}?(?:人份/盒|测试/盒|反应/盒))*)", text)
    if not match:
        return ""
    return _restore_chinese_parentheses(match.group("spec").strip("：:，,。；;"))


def _restore_chinese_parentheses(value: str) -> str:
    return value.replace("(", "（").replace(")", "）")


def _condition_candidates_incomplete(candidates: dict[str, dict[str, object]]) -> bool:
    if not candidates:
        return True
    product_name = str((candidates.get("product_name") or {}).get("suggested") or "").strip()
    product_category = str((candidates.get("product_category") or {}).get("suggested") or "").strip()
    return not product_name or "�" in product_name or product_category == "其他"


def _merge_condition_candidates(
    current: dict[str, dict[str, object]],
    refreshed: dict[str, dict[str, object]],
) -> dict[str, dict[str, object]]:
    merged = {**(current or {})}
    for field, config in (refreshed or {}).items():
        current_config = merged.get(field) or {}
        current_value = str(current_config.get("suggested") or "").strip()
        refreshed_value = str((config or {}).get("suggested") or "").strip()
        if _is_better_condition_value(current_value, refreshed_value):
            merged[field] = config
        elif field not in merged:
            merged[field] = config
    return merged


def _is_better_condition_value(current_value: str, refreshed_value: str) -> bool:
    if not refreshed_value:
        return False
    if "�" in refreshed_value:
        return False
    if "�" in current_value:
        return True
    if not current_value:
        return True
    if current_value == "其他" and refreshed_value != "其他":
        return True
    if current_value == "待确认" and refreshed_value != "待确认":
        return True
    return len(refreshed_value) > len(current_value) and current_value in refreshed_value


def _detect_product_category(corpus: str) -> str:
    if any(keyword in corpus for keyword in ["体外诊断", "检测试剂", "试剂盒", "IVD"]):
        return "体外诊断试剂"
    if "医疗器械" in corpus:
        return "医疗器械"
    return "其他"


def _detect_registration_type(corpus: str) -> str:
    if "延续" in corpus:
        return "延续注册"
    if "变更" in corpus:
        return "变更注册"
    return "首次注册"


def _detect_clinical_path(corpus: str) -> str:
    if "免临床" in corpus or "免于临床" in corpus:
        return "免临床"
    if "同品种" in corpus or "同类" in corpus:
        return "同品种比对"
    if "临床试验" in corpus:
        return "临床试验"
    return "待确认"