import tree_sitter
import faiss
from openai import OpenAI

from tree_sitter import Language, Parser
import tree_sitter_c
import tree_sitter_cpp
import os
import json
import requests
import chardet

# Load C language
CPP_LANGUAGE = Language(tree_sitter_cpp.language())
parser = Parser()
parser.language = CPP_LANGUAGE
CPP_EXTENSIONS = {'.c', '.cpp', '.h', '.hpp', '.tcc'}
IGNORE_DIRS = {'build', 'cmake-build', '.git', 'vendor', 'lib', 'external', 'Debug'}
from tree_sitter import Language, Parser


def read_gbk_file(filepath):
    with open(filepath, 'rb') as f:
        raw_data = f.read()
        encoding = chardet.detect(raw_data)['encoding']
        if encoding.lower() in ('gbk', 'gb2312', 'cp936'):
            return raw_data.decode('gbk')
        else:
            return raw_data.decode('utf-8')  # fallback


def find_c_files(project_path):
    """
    递归查找项目文件夹中的所有 C/C++ 文件
    """
    c_files = []
    for root, dirs, files in os.walk(project_path):
        # 过滤需要忽略的目录
        dirs[:] = [d for d in dirs if d not in IGNORE_DIRS]

        for file in files:
            if any(file.endswith(ext) for ext in CPP_EXTENSIONS):
                full_path = os.path.join(root, file)
                c_files.append(full_path)
    return c_files


def extract_functions_from_file(file_path):
    """
    从单个文件中提取所有函数
    """
    try:
        # 检测文件编码并读取
        with open(file_path, 'rb') as f:
            raw_data = f.read()
            encoding = chardet.detect(raw_data)['encoding']
            if encoding.lower() in ('gbk', 'gb2312', 'cp936'):
                text_str = raw_data.decode('gbk')
            else:
                text_str = raw_data.decode('utf-8')

        code_bytes = text_str.encode('utf-8')
        functions = extract_functions(code_bytes)

        # 为每个函数添加文件路径信息
        for func in functions:
            func['file_path'] = file_path
            func['file_name'] = os.path.basename(file_path)

        return functions
    except Exception as e:
        print(f"处理文件 {file_path} 时出错: {e}")
        return []


def extract_functions(code_bytes: bytes):
    tree = parser.parse(code_bytes)
    root_node = tree.root_node
    functions = []

    def traverse(node):
        if node.type == 'function_definition':
            start = node.start_byte
            end = node.end_byte
            func_code = code_bytes[start:end].decode('utf-8')
            func_name = get_function_name(node, code_bytes)
            functions.append({
                'name': func_name,
                'code': func_code,
                'start_line': node.start_point[0] + 1,
                'end_line': node.end_point[0] + 1
            })
        else:
            for child in node.children:
                traverse(child)

    traverse(root_node)
    return functions


def find_declarator(node):
    """
    递归查找 declarator 节点。
    在 C 语言中，函数名位于 declarator -> ... -> identifier 中。
    我们的目标是找到最内层的 declarator（通常是 function_declarator）。
    """
    # 如果当前节点本身就是 declarator 类型，直接返回
    if node.type in ('declarator', 'function_declarator', 'pointer_declarator'):
        return node

    # 否则在其子节点中递归查找
    for child in node.children:
        # declarator 通常出现在 declaration 的第2个或之后的子节点中
        # 常见结构: [storage_class?, type, declarator, ...]
        result = find_declarator(child)
        if result is not None:
            return result

    return None


def get_function_name(func_node, code_bytes: bytes):
    """
    从 function_definition 节点中提取函数名。
    code_bytes: 原始源码的 bytes 对象（UTF-8 编码）
    """
    declarator = None

    for child in func_node.children:
        if child.type == 'function_declarator':
            declarator = child
            break
        elif child.type == 'declaration':
            declarator = find_declarator(child)
            if declarator:
                break

    if declarator is None:
        return "<unknown>"

    def find_identifier(node):
        if node.type == 'identifier':
            return node
        for child in node.children:
            found = find_identifier(child)
            if found:
                return found
        return None

    ident_node = find_identifier(declarator)
    if ident_node:
        name_bytes = code_bytes[ident_node.start_byte:ident_node.end_byte]
        return name_bytes.decode('utf-8')
    else:
        return "<unnamed>"


MAX_CHAR_LENGTH = 60000  # ～15k tokens
import pandas as pd


def load_review_rules(excel_path: str):
    """
    从 Excel 加载评审规则。
    返回列表：[{"id": 1, "description": "..."}, ...]
    仅包含第3列为"是"的规则。
    """
    df = pd.read_excel(excel_path, header=None, engine='openpyxl')

    # 假设：第0列=序号（可选），第1列=规则描述（B列），第2列=是否启用（C列）
    rules = []
    for idx, row in df.iterrows():
        # 兼容不同长度行
        if len(row) < 3:
            continue
        desc = str(row[1]).strip() if pd.notna(row[1]) else ""
        enabled = str(row[2]).strip() if pd.notna(row[2]) else ""
        if desc and enabled == "是":
            rule_id = str(row[0]).strip() if pd.notna(row[0]) else f"R{idx + 1}"
            rules.append({
                'id': rule_id,
                'description': desc
            })
    return rules


def save_results_to_excel(results, output_path):
    """
    将分析结果保存为 Excel 文件。

    :param results: List[Dict], 每个元素包含 keys:
                    'function_name', 'rule_name', 'has_issue', 'explanation'
    :param output_path: str, 输出 Excel 路径，如 "audit_report.xlsx"
    """
    df = pd.DataFrame(results, columns=[
        "文件名",
        "函数名",
        "规则名",
        "是否缺陷",
        "解释规则"
    ])
    # 自动调整列宽（可选，提升可读性）
    with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
        df.to_excel(writer, index=False, sheet_name='静态分析结果')
        worksheet = writer.sheets['静态分析结果']
        for column in worksheet.columns:
            max_length = 0
            column_letter = column[0].column_letter
            for cell in column:
                try:
                    if len(str(cell.value)) > max_length:
                        max_length = len(str(cell.value))
                except:
                    pass
            adjusted_width = min(max_length + 2, 80)  # 最大列宽限制为80
            worksheet.column_dimensions[column_letter].width = adjusted_width
    print(f"分析结果已保存至: {output_path}")


def split_long_function(func_code: str) -> list[str]:
    if len(func_code) <= MAX_CHAR_LENGTH:
        return [func_code]
    # 简单按行分片（更优方式：按语句块分割）
    lines = func_code.splitlines(keepends=True)
    chunks = []
    current = []
    current_len = 0
    for line in lines:
        if current_len + len(line) > MAX_CHAR_LENGTH and current:
            chunks.append(''.join(current))
            current = [line]
            current_len = len(line)
        else:
            current.append(line)
            current_len += len(line)
    if current:
        chunks.append(''.join(current))
    return chunks


import os
from openai import OpenAI

client = OpenAI(
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
    api_key="sk-b51bf9fea0b3447896ce6d3a150f4a67"
)

RULE_BASED_PROMPT = """你是一名 C 语言安全审计专家。请根据以下特定编码或安全规则，严格审查提供的函数代码片段：

【审查规则】
{rule_description}

【代码上下文】
- 当前为函数 "{function_name}" 的第 {chunk_index}/{total_chunks} 片段
- 若函数被分片，请综合所有片段判断；若仅提供一片，则基于此片判断

【审查要求】
- 如果此代码片段**明确违反**上述规则，请指出：违规位置、风险说明、修复建议；
- 如果**未发现违规**，请回答："本片段未发现违反该规则的问题"；
- 如果因**代码不完整无法判断**（如只看到函数开头），请回答："需结合完整函数判断"；
- 回答必须简洁、基于事实，用中文，不超过50字。
【回答要求】
-请严格按以下 JSON 格式回答，不要包含任何额外文本、注释或 Markdown：
{{
"has_issue": true 或 false,
"explanation": "简明解释为何存在或不存在该问题"
}}
【函数代码片段】
c
{code_chunk}
"""
def analyze_function_chunk_against_rule(
    client,
    func_name: str,
    code_chunk: str,
    rule_desc: str,
    chunk_index: int,
    total_chunks: int,
    model: str = "qwen-max"
) -> str:
    prompt = RULE_BASED_PROMPT.format(
        rule_description=rule_desc,
        function_name=func_name,
        chunk_index=chunk_index,
        total_chunks=total_chunks,
        code_chunk=code_chunk
    )
    try:
        response = client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1,
            max_tokens=500
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"[API Error] {str(e)}"
def analyze_function_against_rules(func_info: dict, rules: list, client, model: str = "qwen-max") -> dict:
    """
    对单个函数应用所有启用的规则。
    若函数过长则分片，每片都送入同一条规则分析，最后合并结果。
    """
    func_name = func_info['name']
    full_code = func_info['code']

    # 分片（复用之前的 split_long_function，但保留行信息更好）
    chunks = split_long_function(full_code)  # 返回 list[str]
    total = len(chunks)

    rule_results = {}

    import json

    # 假设 analyze_function_chunk_against_rule 已按新 prompt 返回纯 JSON 字符串
    # 且 LLM 被强制要求只输出 JSON（无多余文本）

    for rule in rules:
        rule_id = rule['id']
        rule_desc = rule['description']
        chunk_results = []  # 存储每个 chunk 的结构化结果：{has_issue: bool, explanation: str}

        if total == 1:
            # 不分片
            raw_response = analyze_function_chunk_against_rule(
                client, func_name, chunks[0], rule_desc,
                chunk_index=1, total_chunks=1, model=model
            )
            try:
                res = json.loads(raw_response.strip())
                chunk_results.append({
                    'has_issue': bool(res.get('has_issue', False)),
                    'explanation': str(res.get('explanation', ''))
                })
            except (json.JSONDecodeError, AttributeError):
                # 解析失败时降级处理
                chunk_results.append({
                    'has_issue': False,
                    'explanation': f"[解析失败] 原始响应: {raw_response[:200]}..."
                })
        else:
            # 分片分析
            for i, chunk in enumerate(chunks):
                raw_response = analyze_function_chunk_against_rule(
                    client, func_name, chunk, rule_desc,
                    chunk_index=i + 1, total_chunks=total, model=model
                )
                try:
                    res = json.loads(raw_response.strip())
                    chunk_results.append({
                        'has_issue': bool(res.get('has_issue', False)),
                        'explanation': f"【片段{i + 1}】{res.get('explanation', '')}"
                    })
                except (json.JSONDecodeError, AttributeError):
                    chunk_results.append({
                        'has_issue': False,
                        'explanation': f"【片段{i + 1}】[解析失败] 原始响应: {raw_response[:200]}..."
                    })

        # 合并分片结果：只要有一个片段存在缺陷，整体视为有缺陷
        final_has_issue = any(item['has_issue'] for item in chunk_results)
        final_explanation = "\n".join(item['explanation'] for item in chunk_results)

        rule_results[rule_id] = {
            'rule_description': rule_desc,
            'has_issue': final_has_issue,  # 新增结构化字段
            'analysis': final_explanation  # 保留原始解释用于报告
        }

    return rule_results
def split_long_function(func_code: str, max_lines: int = 1600) -> list[str]:
    """
    按行分片，尽量避免在 {} 中间切断（简单启发式）。
    """
    lines = func_code.splitlines(keepends=True)
    if len(lines) <= max_lines:
        return [func_code]

    chunks = []
    current = []
    brace_depth = 0

    for line in lines:
        current.append(line)
        # 粗略跟踪花括号深度
        brace_depth += line.count('{') - line.count('}')

        if len(current) >= max_lines and brace_depth <= 0:
            # 在块结束处分片
            chunks.append(''.join(current))
            current = []
            brace_depth = 0

    if current:
        chunks.append(''.join(current))

    # 防御：万一 brace_depth 始终 >0，强制分片
    if not chunks:
        # fallback: 每 max_lines 行一切
        for i in range(0, len(lines), max_lines):
            chunks.append(''.join(lines[i:i + max_lines]))

    return chunks
def main(project_path: str, rules_excel: str, output_report: str = "rule_based_report.md"):
    print("正在加载评审规则...")
    rules = load_review_rules(rules_excel)
    print(f"共加载 {len(rules)} 条启用的规则")
    print("正在读取 GBK 编码源文件...")
    print(f"正在扫描项目文件夹: {project_path}")
    c_files = find_c_files(project_path)
    print(f"找到 {len(c_files)} 个 C/C++ 文件")

    if not c_files:
        print("未找到任何 C/C++ 文件，请检查项目路径")
        return

    # 提取所有文件中的函数
    all_functions = []
    for file_path in c_files:
        print(f"正在处理文件: {file_path}")
        functions = extract_functions_from_file(file_path)
        all_functions.extend(functions)
        print(f"  找到 {len(functions)} 个函数")

    print(f"共找到 {len(all_functions)} 个函数")

    if not all_functions:
        print("未找到任何函数，分析结束")
        return

    # 初始化 OpenAI/Qwen 客户端
    client = OpenAI(
        base_url=os.getenv("QWEN_API_BASE", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
        api_key=os.getenv("QWEN_API_KEY", "sk-b51bf9fea0b3447896ce6d3a150f4a67")
    )

    all_results = []

    for i, func in enumerate(all_functions, 1):
        print(f"\n分析函数 {i}/{len(all_functions)}: {func['name']} (来自 {func['file_name']})")
        rule_results = analyze_function_against_rules(func, rules, client)
        print(rule_results)
        all_results.append({
            'function': func,
            'rule_results': rule_results
        })

    # 生成 Excel 报告
    excel_rows = []
    for item in all_results:
        func_info = item['function']
        func_name = func_info['name']
        file_name = func_info['file_name']

        for rule_id, res in item['rule_results'].items():
            issue_flag = "是" if res.get('has_issue', False) else "否"

            excel_rows.append({
                "文件名": file_name,
                "函数名": func_name,
                "规则名": f"[{rule_id}] {res['rule_description']}",
                "是否缺陷": issue_flag,
                "解释规则": res['analysis']
            })

    # 调用保存 Excel 的函数
    excel_output_path = output_report.replace('.md', '.xlsx')
    save_results_to_excel(excel_rows, excel_output_path)

    # 生成 Markdown 报告
    with open(output_report, 'w', encoding='utf-8') as f:
        f.write("# 基于规则的 C 函数静态分析报告\n\n")
        f.write(f"**项目路径**: `{project_path}`\n\n")
        f.write(f"**规则来源**: `{rules_excel}`\n\n")
        f.write(f"**分析文件数**: {len(c_files)} 个\n\n")
        f.write(f"**共分析函数**: {len(all_functions)} 个\n\n")
        f.write(f"**启用规则数**: {len(rules)} 条\n\n---\n\n")

        # 按文件分组显示结果
        files_dict = {}
        for item in all_results:
            func = item['function']
            file_path = func['file_path']
            if file_path not in files_dict:
                files_dict[file_path] = []
            files_dict[file_path].append(item)

        for file_path, items in files_dict.items():
            f.write(f"## 文件: `{os.path.basename(file_path)}`\n\n")
            f.write(f"**完整路径**: {file_path}\n\n")

            for item in items:
                func = item['function']
                f.write(f"### 函数: `{func['name']}` (L{func['start_line']}-L{func['end_line']})\n\n")
                f.write("```c\n")
                snippet = func['code'][:1500] + ("..." if len(func['code']) > 1500 else "")
                f.write(snippet)
                f.write("\n```\n\n")

                for rule_id, res in item['rule_results'].items():
                    f.write(f"#### 规则 [{rule_id}] {res['rule_description']}\n\n")
                    f.write(f"{res['analysis']}\n\n")
                f.write("---\n\n")

    print(f"\n分析完成！")
    print(f"Excel 报告已保存至: {excel_output_path}")
    print(f"Markdown 报告已保存至: {output_report}")
if __name__ == "__main__":
    import os
    os.environ["QWEN_API_BASE"] = "https://dashscope.aliyuncs.com/compatible-mode/v1"
    os.environ["QWEN_API_KEY"] = ""

    main(
        project_path="",
        rules_excel="审查规则.xlsx",
        output_report="audit_report.md"
    )