import os
import re
import pandas as pd
import joblib
import json
from datetime import datetime
from typing import Dict, Any, Optional

# --- FINAL, STABLE IMPORTS ---
try:
    from newsapi import NewsApiClient
except ImportError:
    NewsApiClient = None
try:
    from serpapi import GoogleSearch
except ImportError:
    GoogleSearch = None
try:
    from core.utils import get_supabase_client
except ImportError:
    get_supabase_client = None

# --- CONFIGURATION ---
MODEL_PATH = os.path.join(os.path.dirname(__file__), '..', 'models', 'thunderbird_market_predictor_v1.joblib')
NEWS_API_KEY = os.environ.get("NEWS_API_KEY")
SERPAPI_KEY = os.environ.get("SERPAPI_KEY")

def get_platform_shifts() -> Optional[Dict[str, str]]:
    """Calculates REAL 7-day shift from Supabase. Returns None if it fails."""
    if not get_supabase_client: return None
    print("   - Calculating REAL platform shifts from DB...")
    try:
        supabase = get_supabase_client()
        response = supabase.rpc('get_platform_trend_data_last_14_days').execute()
        if not response.data or len(response.data) < 2: return None
        df = pd.DataFrame(response.data); df['date'] = pd.to_datetime(df['date'])
        seven_days_ago = datetime.now() - pd.Timedelta(days=7)
        recent = df[df['date'] >= seven_days_ago]; prev = df[df['date'] < seven_days_ago]
        if prev.empty or recent.empty: return None
        avg_recent = recent.groupby('platform')['usage_count'].mean()
        avg_prev = prev.groupby('platform')['usage_count'].mean()
        shifts = {}
        for p in ['instagram', 'tiktok', 'youtube']:
            if p in avg_recent and p in avg_prev and avg_prev[p] > 0:
                change = ((avg_recent[p] - avg_prev[p]) / avg_prev[p]) * 100
                shifts[f"{p}_shift"] = f"{'+' if change > 0 else ''}{round(change)}%"
        return shifts
    except Exception as e:
        print(f"   - ❌ DB Error calculating shifts: {e}")
        return None

def get_external_trends() -> dict:
    """Orchestrator function to fetch all real-world data."""
    print("🚀 [Thunderbird Engine] Fetching all external trends...")
    results = { "news_headlines": [], "breakout_keyword": None, "trending_audio": None, "platform_shifts": None }
    
    # 1. NewsAPI
    if NEWS_API_KEY and NewsApiClient:
        try:
            newsapi = NewsApiClient(api_key=NEWS_API_KEY)
            top_headlines = newsapi.get_everything(q='("influencer marketing")', language='en', sort_by='relevancy', page_size=5)
            if top_headlines.get('articles'): results["news_headlines"] = [{"title": a['title'], "url": a['url']} for a in top_headlines['articles']]
        except Exception as e: print(f"   - ⚠️ NewsAPI failed: {e}")

    # 2. SerpApi for Google Trends & TikTok
    if SERPAPI_KEY and GoogleSearch:
        try:
            # Google Trends
            params = {"engine": "google_trends_trending_now", "frequency": "daily", "api_key": SERPAPI_KEY}
            search = GoogleSearch(params)
            res = search.get_dict()
            if res.get("trending_searches"): results["breakout_keyword"] = res["trending_searches"][0]["title"]
            
            # TikTok Trends
            params = {"engine": "tiktok_trending", "api_key": SERPAPI_KEY}
            search = GoogleSearch(params)
            res = search.get_dict()
            if res.get("trending_videos"):
                for video in res["trending_videos"]:
                    if video.get("music"):
                        results["trending_audio"] = {"name": video["music"].get("title"), "coverArtUrl": video["music"].get("cover_thumb")}
                        break
        except Exception as e: print(f"   - ❌ SerpApi failed: {e}")
    
    # 3. Platform Shifts from DB
    results["platform_shifts"] = get_platform_shifts()
    
    return results

def predict_niche_trends() -> dict:
    """Uses the REAL trained ML pipeline to predict future interest."""
    print("\n🚀 [Thunderbird Engine] Using REAL ML pipeline for predictions...")
    try:
        pipeline = joblib.load(MODEL_PATH)
        encoder = pipeline.named_steps['preprocessor'].named_transformers_['cat']
        all_niches = [cat.replace('niche_', '') for cat in encoder.get_feature_names_out(['niche'])]
        future_dates = pd.to_datetime(pd.date_range(start=datetime.now(), periods=12, freq='ME'))
        predictions = {}
        for niche in all_niches:
            future_df = pd.DataFrame({'month_of_year': future_dates.month, 'niche': [niche] * 12, 'trend_score': 50})
            predicted_values = pipeline.predict(future_df[['niche', 'trend_score', 'month_of_year']])
            predictions[niche] = [{"date": dt.strftime('%Y-%m'), "value": max(0, int(val))} for dt, val in zip(future_dates, predicted_values)]
        return {"trend_predictions": predictions}
    except Exception as e:
        print(f"   - ❌ REAL Prediction Failed: {e}. Chart will be empty.")
        return {"trend_predictions": {}}

def decode_market_trend(topic: str, llm_instance) -> Dict[str, str]:
    """
    Decodes a keyword into a strategy using a simplified prompt for small models.
    """
    print(f"🧠 [Thunderbird] Decoding Trend with SIMPLE prompt: {topic}")
    
    offline_response = {"summary": "AI Analyst is offline.", "impact": "Please try again.", "strategy": "System is rebooting."}
    if not llm_instance: return offline_response

    # === THE FINAL, SIMPLEST PROMPT ===
    # We ask for a simple paragraph and let Python do the formatting.
    prompt = f"""[INST]
    You are an expert marketing strategist.
    Analyze this trend: "{topic}".
    Write a short, three-sentence briefing about it.
    [/INST]"""

    try:
        response = llm_instance(prompt, max_tokens=150, temperature=0.7, echo=False)
        full_text = response['choices'][0]['text'].strip()

        # Split the paragraph into three sentences
        sentences = re.split(r'(?<=\.)\s+', full_text)

        # Assign each sentence to a key, with fallbacks
        summary = sentences[0] if len(sentences) > 0 else "Analysis in progress."
        impact = sentences[1] if len(sentences) > 1 else "Impact is being evaluated."
        strategy = sentences[2] if len(sentences) > 2 else "Awaiting actionable strategy."
        
        return {
            "summary": summary,
            "impact": impact,
            "strategy": strategy
        }
        # === END OF FIX ===

    except Exception as e:
        print(f"   - ❌ LLM Error: {e}")
        return offline_response