import requests
import json
import time

OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"

MODEL_OPTIONS = {
    "🟣 Claude 3.5 Haiku  (Fast & Cheap)":     "anthropic/claude-3-5-haiku",
    "🟣 Claude 3.5 Sonnet (Most Capable)":      "anthropic/claude-3-5-sonnet",
    "🔵 GPT-4o Mini       (OpenAI - Fast)":     "openai/gpt-4o-mini",
    "🔵 GPT-4o            (OpenAI - Powerful)": "openai/gpt-4o",
    "🔴 Gemini Flash 1.5  (Google - Fast)":     "google/gemini-flash-1.5",
    "🔴 Gemini Pro 1.5    (Google - Powerful)": "google/gemini-pro-1.5",
    "⚫ Grok 2            (xAI)":               "x-ai/grok-2-1212",
    "🟡 DeepSeek V3       (Fast & Cheap)":      "deepseek/deepseek-chat",
    "🟡 DeepSeek R1       (Reasoning Model)":   "deepseek/deepseek-r1",
}


def test_connection(api_key: str, model_id: str) -> tuple[bool, str]:
    """Ping the API with a minimal request. Returns (success, message)."""
    try:
        resp = requests.post(
            OPENROUTER_URL,
            headers=_headers(api_key),
            json={
                "model": model_id,
                "messages": [{"role": "user", "content": "Reply OK"}],
                "max_tokens": 5,
            },
            timeout=15,
        )
        if resp.status_code == 200:
            return True, "Connection successful."
        err = resp.json().get("error", {}).get("message", resp.text[:200])
        return False, f"API error {resp.status_code}: {err}"
    except Exception as e:
        return False, f"Connection failed: {str(e)}"


def enrich_merge_pairs(
    pairs: list[dict],
    api_key: str,
    model_id: str,
    batch_size: int = 10,
    progress_callback=None,
) -> list[dict]:
    """
    Send merge pairs to the LLM in batches.
    Fills in topic_cluster and merge_reason for each pair.
    progress_callback(current, total) is called after each batch.
    """
    total   = len(pairs)
    batches = [pairs[i: i + batch_size] for i in range(0, total, batch_size)]
    results = []

    for b_idx, batch in enumerate(batches):
        try:
            llm_data = _call_llm(batch, api_key, model_id)
            for item in llm_data:
                idx = item.get("pair", 1) - 1
                if 0 <= idx < len(batch):
                    batch[idx]["topic_cluster"] = item.get("topic_cluster", "General")
                    batch[idx]["merge_reason"]  = item.get("merge_reason", "Topically similar content detected.")
                    batch[idx]["should_merge"]  = item.get("should_merge", True)
        except Exception as e:
            # Graceful fallback — keep pairs with placeholder text
            for p in batch:
                p.setdefault("topic_cluster", "Review Manually")
                p.setdefault("merge_reason",  f"LLM error: {str(e)[:80]}. Review manually.")
                p.setdefault("should_merge",  True)

        results.extend(batch)

        if progress_callback:
            progress_callback(min((b_idx + 1) * batch_size, total), total)

        # Small delay to avoid rate limits
        if b_idx < len(batches) - 1:
            time.sleep(0.5)

    # Filter to only pairs the LLM recommends merging
    return [p for p in results if p.get("should_merge", True)]


def _call_llm(batch: list[dict], api_key: str, model_id: str) -> list[dict]:
    pairs_text = ""
    for i, p in enumerate(batch):
        pairs_text += f"""
Pair {i + 1}:
  WEAK   → Title: "{p['weak_title']}" | Clicks: {p['weak_clicks']}
  STRONG → Title: "{p['strong_title']}" | Clicks: {p['strong_clicks']}
  Similarity: {p['similarity']}
"""

    prompt = f"""You are an SEO content strategist auditing blog articles for a website.

I have identified blog pairs that may be topically overlapping based on semantic similarity.
The WEAK article has fewer clicks; the STRONG article has more.

For each pair:
1. Decide if the WEAK article should be merged INTO the STRONG article (should_merge: true/false)
2. Provide a topic cluster name (2-4 words)
3. Write a concise 1-2 sentence merge reason

Return ONLY a valid JSON array — no markdown, no extra text:
[
  {{
    "pair": 1,
    "should_merge": true,
    "topic_cluster": "Change Management",
    "merge_reason": "Both articles cover overlapping change management topics. Merge the weaker piece as a supporting section and implement a 301 redirect."
  }}
]

Pairs to analyze:
{pairs_text}"""

    resp = requests.post(
        OPENROUTER_URL,
        headers=_headers(api_key),
        json={
            "model": model_id,
            "messages": [{"role": "user", "content": prompt}],
            "max_tokens": 2500,
            "temperature": 0.2,
        },
        timeout=90,
    )

    if resp.status_code != 200:
        raise Exception(f"API {resp.status_code}: {resp.text[:200]}")

    raw = resp.json()["choices"][0]["message"]["content"].strip()
    raw = raw.replace("```json", "").replace("```", "").strip()
    return json.loads(raw)


def _headers(api_key: str) -> dict:
    return {
        "Authorization": f"Bearer {api_key}",
        "Content-Type":  "application/json",
        "HTTP-Referer":  "https://blog-audit.streamlit.app",
        "X-Title":       "Blog Audit Tool",
    }