"""
Context Builder: assembles compact, high-signal repository context for IBM Bob.
"""

from __future__ import annotations

import re

MAX_CONTEXT_CHARS = 6000


def build_architecture_context(repo_data: dict) -> str:
    structure = repo_data["structure"]
    technologies = repo_data["technologies"]
    file_contents = repo_data.get("file_contents", {})
    important_files = _get_important_files(file_contents)

    ctx = f"""REPOSITORY: {repo_data['github_url']}
TOTAL FILES: {structure['total_files']} | TOTAL LINES: {structure['total_lines']}
LANGUAGES: {[l['name'] for l in technologies.get('languages', [])[:5]]}
FRAMEWORKS: {technologies.get('frameworks', [])}
DATABASES: {technologies.get('databases', [])}
DEVOPS: {technologies.get('devops', [])}
API STYLE: {technologies.get('apis', [])}

FILE TREE (key files):
{_format_file_tree(_rank_files(structure['files'])[:100])}

KEY FILE CONTENTS:
{important_files}"""
    return ctx[:MAX_CONTEXT_CHARS]


def build_qa_context(repo_data: dict, question: str) -> str:
    file_contents = repo_data.get("file_contents", {})
    technologies = repo_data["technologies"]
    relevant_files = _find_relevant_files(file_contents, question)

    ctx = f"""TECH STACK: {technologies.get('frameworks', [])} | {technologies.get('databases', [])}
LANGUAGES: {[l['name'] for l in technologies.get('languages', [])[:3]]}

RELEVANT CODE CONTEXT:
{relevant_files}"""
    return ctx[:MAX_CONTEXT_CHARS]


def build_impact_context(repo_data: dict, target_file: str) -> str:
    file_contents = repo_data.get("file_contents", {})
    structure = repo_data["structure"]
    target_content = file_contents.get(target_file, "File content not available")
    importers = _find_importers(file_contents, target_file)

    ctx = f"""REPOSITORY STATS: {structure['total_files']} files
TARGET FILE: {target_file}

TARGET FILE CONTENT:
```
{target_content[:2500]}
```

FILES THAT IMPORT/USE THIS:
{importers[:2200]}

ALL FILES IN REPO:
{_format_file_tree(_rank_files(structure['files'])[:80])}"""
    return ctx[:MAX_CONTEXT_CHARS]


def build_docs_context(repo_data: dict) -> str:
    """Build a compact docs context so live Bob generation returns quickly."""
    structure = repo_data["structure"]
    technologies = repo_data["technologies"]
    deps = repo_data.get("dependencies", {})
    ranked_files = _rank_files(structure["files"])
    ctx = f"""REPOSITORY: {repo_data['github_url']}
TOTAL FILES: {structure['total_files']} | TOTAL LINES: {structure['total_lines']}
LANGUAGES: {[l['name'] for l in technologies.get('languages', [])[:5]]}
FRAMEWORKS: {technologies.get('frameworks', [])}
DATABASES: {technologies.get('databases', [])}
DEVOPS: {technologies.get('devops', [])}
API STYLE: {technologies.get('apis', [])}

IMPORTANT FILES:
{_format_file_tree(ranked_files[:55])}

DEPENDENCIES:
NPM: {deps.get('npm', [])[:20]}
PIP: {deps.get('pip', [])[:20]}
GO: {deps.get('go', [])[:12]}
CARGO: {deps.get('cargo', [])[:12]}"""
    return ctx[:3500]


def _rank_files(files: list[dict]) -> list[dict]:
    important_tokens = ("main", "app", "server", "router", "route", "model", "schema", "auth", "security", "config", "package", "requirements")

    def score(file_info: dict) -> tuple[int, int]:
        path = file_info.get("path", "").lower()
        token_score = sum(5 for token in important_tokens if token in path)
        depth_score = max(0, 4 - path.count("/"))
        return (token_score + depth_score, -len(path))

    return sorted(files, key=score, reverse=True)


def _get_important_files(file_contents: dict) -> str:
    priority_patterns = [
        "main.py",
        "app.py",
        "index.js",
        "server.js",
        "app.js",
        "main.ts",
        "main.jsx",
        "index.ts",
        "package.json",
        "requirements.txt",
        "App.jsx",
        "App.tsx",
        "routes.py",
        "models.py",
        "schema.prisma",
        "pyproject.toml",
    ]
    result = ""
    for name in priority_patterns:
        for path, content in file_contents.items():
            if path.endswith(name) and len(result) < 3600:
                result += f"\n--- {path} ---\n{content[:700]}\n"
    return result


def _format_file_tree(files: list[dict]) -> str:
    return "\n".join(file_info.get("path", "") for file_info in files)


def _find_relevant_files(file_contents: dict, question: str) -> str:
    keywords = [token for token in re.split(r"\W+", question.lower()) if len(token) >= 3]
    scored = []
    for path, content in file_contents.items():
        combined = f"{path}\n{content}".lower()
        score = sum(1 for keyword in keywords if keyword in combined)
        if score:
            scored.append((score, path, content))
    scored.sort(key=lambda item: (item[0], -len(item[1])), reverse=True)
    result = ""
    for _, path, content in scored[:5]:
        result += f"\n--- {path} ---\n{content[:800]}\n"
    return result or _get_important_files(file_contents)


def _find_importers(file_contents: dict, target_file: str) -> str:
    filename = target_file.split("/")[-1]
    stem = filename.rsplit(".", 1)[0]
    module_guess = target_file.rsplit(".", 1)[0].replace("/", ".")
    result = ""
    for path, content in file_contents.items():
        if path == target_file:
            continue
        if target_file in content or filename in content or stem in content or module_guess in content:
            result += f"\n--- {path} (references target) ---\n{content[:450]}\n"
    return result or "No direct textual importers found in scanned content."