"""LLM client for generating documentation. Supports Ollama and OpenAI-compatible APIs."""

import requests
import os
import concurrent.futures
import time
from pathlib import Path

# Load .env if present
_env_file = Path(__file__).parent / ".env"
if _env_file.exists():
    for line in _env_file.read_text().splitlines():
        line = line.strip()
        if line and not line.startswith("#") and "=" in line:
            key, _, val = line.partition("=")
            os.environ.setdefault(key.strip(), val.strip())

# Backend: "ollama" or "openai"
LLM_BACKEND = os.environ.get("LLM_BACKEND", "ollama")

# Ollama settings
OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://192.168.86.172:11434")
OLLAMA_MODEL = os.environ.get("OLLAMA_MODEL", "qwen2.5:7b")

# OpenAI-compatible settings (works with Kiro gateway, OpenRouter, etc.)
OPENAI_URL = os.environ.get("OPENAI_URL", "http://192.168.86.11:8000")
OPENAI_MODEL = os.environ.get("OPENAI_MODEL", "claude-haiku-4")
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "not-needed")

MAX_CONCURRENT = int(os.environ.get("MAX_CONCURRENT", "4"))


def generate_file_doc(filepath: str, content: str) -> str:
    """Generate documentation for a single file."""
    if len(content) > 8000:
        content = content[:8000] + "\n\n... [truncated]"

    prompt = f"""You are a senior software engineer documenting a Go codebase.

Describe what this file does in 2-4 sentences. Be specific about:
- The domain logic and purpose (not just "this file contains functions")
- Key types, interfaces, or structs defined
- How it fits into the larger system (if apparent from imports/naming)

Do NOT describe Go syntax or language mechanics. Describe WHAT the code does and WHY.

File: {filepath}

```go
{content}
```

Documentation:"""

    return _call_llm(prompt)


def generate_relationship_doc(file_a: str, content_a: str, file_b: str, content_b: str) -> str:
    """Generate documentation for a relationship between two files."""
    if len(content_a) > 4000:
        content_a = content_a[:4000] + "\n... [truncated]"
    if len(content_b) > 4000:
        content_b = content_b[:4000] + "\n... [truncated]"

    prompt = f"""You are a senior software engineer documenting how two files in a Go codebase interact.

Describe in 1-2 sentences how File A uses or depends on File B. Be specific about which types, functions, or interfaces are shared.

File A: {file_a}
```go
{content_a}
```

File B: {file_b}
```go
{content_b}
```

Relationship:"""

    return _call_llm(prompt)


def generate_repo_doc(readme: str, entry_files: list[tuple[str, str]]) -> str:
    """Generate repo-level documentation from README and key entry points."""
    files_section = ""
    for path, content in entry_files[:5]:
        snippet = content[:2000] if len(content) > 2000 else content
        files_section += f"\n--- {path} ---\n{snippet}\n"

    readme_section = readme[:3000] if len(readme) > 3000 else readme

    prompt = f"""You are a senior software engineer writing a project overview.

Based on the README and key source files below, write a 4-6 sentence summary of this project. Cover:
- What the project does (its purpose)
- Key architectural patterns (routing, middleware, etc.)
- The main abstractions and how they fit together

README:
{readme_section}

Key source files:
{files_section}

Project Overview:"""

    return _call_llm(prompt)


def generate_docs_batch(items: list[tuple[str, str]], doc_fn) -> list[str]:
    """Generate docs for multiple items concurrently."""
    results = [None] * len(items)

    with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_CONCURRENT) as executor:
        future_to_idx = {}
        for i, (filepath, content) in enumerate(items):
            future = executor.submit(doc_fn, filepath, content)
            future_to_idx[future] = i

        done = 0
        total = len(items)
        for future in concurrent.futures.as_completed(future_to_idx):
            idx = future_to_idx[future]
            try:
                results[idx] = future.result()
            except Exception as e:
                results[idx] = f"[doc generation failed: {e}]"
            done += 1
            if done % 10 == 0 or done == total:
                print(f"  Generated {done}/{total} docs")

    return results


def _call_ollama(prompt: str, retries: int = 3) -> str:
    """Call Ollama API with retries."""
    for attempt in range(retries):
        try:
            resp = requests.post(
                f"{OLLAMA_URL}/api/generate",
                json={
                    "model": OLLAMA_MODEL,
                    "prompt": prompt,
                    "stream": False,
                    "options": {
                        "temperature": 0.3,
                        "num_predict": 256,
                    },
                },
                timeout=120,
            )
            resp.raise_for_status()
            return resp.json()["response"].strip()
        except Exception as e:
            if attempt < retries - 1:
                time.sleep(2 ** attempt)
                continue
            return f"[doc generation failed after {retries} attempts: {e}]"


def _call_openai(prompt: str, retries: int = 3) -> str:
    """Call OpenAI-compatible API (Kiro gateway, OpenRouter, etc.)."""
    for attempt in range(retries):
        try:
            resp = requests.post(
                f"{OPENAI_URL}/v1/chat/completions",
                headers={"Authorization": f"Bearer {OPENAI_API_KEY}"},
                json={
                    "model": OPENAI_MODEL,
                    "messages": [{"role": "user", "content": prompt}],
                    "temperature": 0.3,
                    "max_tokens": 256,
                },
                timeout=120,
            )
            resp.raise_for_status()
            return resp.json()["choices"][0]["message"]["content"].strip()
        except Exception as e:
            if attempt < retries - 1:
                time.sleep(2 ** attempt)
                continue
            return f"[doc generation failed after {retries} attempts: {e}]"


def _call_llm(prompt: str) -> str:
    """Route to the configured backend."""
    if LLM_BACKEND == "openai":
        return _call_openai(prompt)
    return _call_ollama(prompt)