import joblib
import pandas as pd
import numpy as np
from typing import List, Dict
import traceback

print(">>> Loading ai-service/core/predictor.py (Version: FINAL, SAFE LOAD)")

# --- SAFE MODEL LOADING ---
# Hum 'Exception' use kar rahe hain taaki version mismatch ya missing file 
# se server crash na ho.
influencer_pipeline = None
performance_pipeline = None

try:
    print("--- Loading Influencer Matcher Model... ---")
    influencer_pipeline = joblib.load('models/influencer_matcher_v1.joblib')
    print("✅ Influencer Matcher loaded successfully.")
except Exception as e:
    print(f"⚠️ Warning: Influencer Matcher model failed to load: {e}")
    # Continue running, don't crash

try:
    print("--- Loading Performance Predictor Model... ---")
    performance_pipeline = joblib.load('models/performance_predictor_v1.joblib')
    print("✅ Performance Predictor loaded successfully.")
except Exception as e:
    print(f"⚠️ Warning: Performance Predictor model failed to load: {e}")
    # Continue running

# --- LOGIC ---

def rank_influencers_by_match(influencers: List[Dict], campaign_details: Dict, top_n: int = 5) -> List[Dict]:
    """
    Influencers ko rank karta hai. Agar model nahi hai toh basic fallback use karega.
    """
    # 1. Agar model load nahi hua ya data khali hai, toh empty list bhejo (Crash mat karo)
    if not influencers or influencer_pipeline is None:
        print("ℹ️ Matcher skipped: No model or no influencers provided.")
        return influencers[:top_n] # Fallback: Return original list as is

    try:
        print(f"--- Predictor: Ranking {len(influencers)} influencers...")
        
        # DataFrame Prepare
        influencer_df = pd.DataFrame(influencers)
        
        # Safe Feature Extraction
        features = influencer_df[['category', 'bio']].copy() if 'bio' in influencer_df else pd.DataFrame()
        if features.empty: return influencers[:top_n]

        features['niche'] = campaign_details.get('category', 'General')
        features['country'] = campaign_details.get('location', 'USA')
        features['followers'] = campaign_details.get('followers', 10000)
        features['engagement_rate'] = campaign_details.get('engagement_rate', 0.03)
        
        # Prediction
        match_scores = influencer_pipeline.predict(features)
        influencer_df['match_score'] = match_scores
        
        # Sorting
        top_influencers_df = influencer_df.sort_values(by='match_score', ascending=False).head(top_n)

        # Cleanup Result
        result_cols = ['id', 'name', 'handle', 'followers', 'category', 'bio']
        final_cols = [col for col in result_cols if col in top_influencers_df.columns]
        
        results = top_influencers_df[final_cols].to_dict(orient='records')
        return results

    except Exception as e:
        print(f"❌ Ranking Error: {e}")
        traceback.print_exc()
        return influencers[:top_n] # Error aane par original list wapas kardo

def predict_performance(data: dict) -> int:
    """
    Campaign performance predict karta hai.
    """
    if performance_pipeline is None:
        return 50000 # Default/Safe Value

    try:
        df = pd.DataFrame(data, index=[0])
        prediction = performance_pipeline.predict(df)
        return int(prediction[0])
    except Exception as e:
        print(f"❌ Performance Prediction Error: {e}")
        return 50000 # Default/Safe Value