import io
import tempfile
import os
from typing import List, Dict, Any, Optional
from pathlib import Path

try:
    from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
    _vader = SentimentIntensityAnalyzer()
except ImportError:
    _vader = None

from app.services.scoring import detect_churn_keywords, CHURN_KEYWORDS


def analyze_sentiment(text: str) -> dict:
    """Run VADER sentiment analysis on transcript text."""
    if not text.strip():
        return {"compound": 0.0, "pos": 0.0, "neg": 0.0, "neu": 1.0, "label": "neutral"}

    if _vader is None:
        # fallback: simple heuristic
        positive = sum(1 for w in ["good", "great", "excellent", "happy", "love", "thanks", "amazing", "helpful"] if w in text.lower())
        negative = sum(1 for w in ["bad", "terrible", "awful", "hate", "frustrated", "angry", "broken", "useless"] if w in text.lower())
        compound = (positive - negative) / max(positive + negative + 1, 1)
        return {
            "compound": round(compound, 4),
            "pos": 0.0, "neg": 0.0, "neu": 0.0,
            "label": "positive" if compound > 0.1 else ("negative" if compound < -0.1 else "neutral")
        }

    scores = _vader.polarity_scores(text)
    compound = scores["compound"]
    label = "positive" if compound >= 0.05 else ("negative" if compound <= -0.05 else "neutral")

    return {
        "compound": round(compound, 4),
        "pos": round(scores["pos"], 4),
        "neg": round(scores["neg"], 4),
        "neu": round(scores["neu"], 4),
        "label": label,
    }


def transcribe_with_whisper(audio_bytes: bytes, filename: str, api_key: str) -> str:
    """Transcribe audio using OpenAI Whisper API."""
    import httpx

    # Determine content type from extension
    ext = Path(filename).suffix.lower()
    content_type_map = {
        ".mp3": "audio/mpeg",
        ".wav": "audio/wav",
        ".m4a": "audio/mp4",
        ".mp4": "video/mp4",
        ".ogg": "audio/ogg",
        ".webm": "audio/webm",
    }
    content_type = content_type_map.get(ext, "audio/mpeg")

    # Write bytes to temp file
    with tempfile.NamedTemporaryFile(suffix=ext, delete=False) as tmp:
        tmp.write(audio_bytes)
        tmp_path = tmp.name

    try:
        with open(tmp_path, "rb") as f:
            response = httpx.post(
                "https://api.openai.com/v1/audio/transcriptions",
                headers={"Authorization": f"Bearer {api_key}"},
                data={"model": "whisper-1", "response_format": "text"},
                files={"file": (filename, f, content_type)},
                timeout=60.0,
            )
        if response.status_code != 200:
            raise Exception(f"Whisper API error ({response.status_code}): {response.text[:200]}")

        return response.text
    finally:
        os.unlink(tmp_path)


def analyze_call(audio_bytes: bytes, filename: str, api_key: str) -> dict:
    """Full call analysis: transcript → sentiment → churn keywords."""
    transcript = transcribe_with_whisper(audio_bytes, filename, api_key)
    sentiment = analyze_sentiment(transcript)
    keywords = detect_churn_keywords(transcript)

    # Combined churn intent from transcript
    churn_score = keywords["churn_intent_score"]
    if sentiment["label"] == "negative":
        churn_score = min(churn_score + 20, 100)
    elif sentiment["label"] == "positive":
        churn_score = max(churn_score - 15, 0)

    return {
        "filename": filename,
        "transcript_snippet": transcript[:300] + ("..." if len(transcript) > 300 else ""),
        "transcript_full": transcript,
        "sentiment_score": sentiment["compound"],
        "sentiment_label": sentiment["label"],
        "churn_intent_score": churn_score,
        "flagged_keywords": keywords["flagged_keywords"],
        "flagged_keyword_count": keywords["flagged_keyword_count"],
    }