"""
FILE: intent_classifier.py (ENHANCED VERSION)

PURPOSE:
- Detect user intent from natural text using Semantic Similarity
- Route query into domains aligned with the expanded dataset: FOOD / HERITAGE / TRAVEL / CULTURAL / ARCHITECTURE / NATURE
- Uses SentenceTransformer for robust understanding beyond keywords
"""

from sentence_transformers import SentenceTransformer, util
import torch

# Define domain prototypes aligned with the dataset's actual domains
DOMAIN_PROTOTYPES = {
    "food": "I want to eat delicious food, snacks, dishes, sweets, desserts, breakfast, lunch, dinner, restaurants, street food, traditional cuisine, beverages.",
    
    "heritage": "I want to visit historical sites, ancient monuments, forts, palaces, museums, archaeological ruins, tombs, heritage buildings, UNESCO sites, historical architecture.",
    
    "travel": "I want to travel and explore places like hill stations, valleys, mountains, passes, trekking spots, adventure destinations, offbeat locations, hidden gems, scenic viewpoints, roads.",
    
    "nature": "I want to experience nature through waterfalls, lakes, rivers, forests, wildlife sanctuaries, national parks, caves, islands, beaches, natural landscapes, gardens.",
    
    "cultural": "I want to experience culture through festivals, traditional events, art forms, folk performances, local customs, tribal culture, villages, markets, handlooms, crafts.",
    
    "architecture": "I want to see beautiful architecture, design, structures, buildings, temples, churches, mosques, monasteries, modern architecture, engineering marvels."
}

class IntentClassifier:
    def __init__(self, model_name="all-MiniLM-L6-v2"):
        print(f"🧠 Loading Intent Classifier Model: {model_name}...")
        self.model = SentenceTransformer(model_name)
        
        # Pre-compute embeddings for domain prototypes
        self.domains = list(DOMAIN_PROTOTYPES.keys())
        self.prototypes = list(DOMAIN_PROTOTYPES.values())
        self.prototype_embeddings = self.model.encode(self.prototypes, convert_to_tensor=True)
        print("✅ Intent Classifier Ready")

    def predict_intent(self, query: str, threshold: float = 0.25) -> str:
        """
        Predicts the intent of the query based on semantic similarity to domain prototypes.
        Returns 'general' if the highest similarity score is below the threshold.
        """
        query_embedding = self.model.encode(query, convert_to_tensor=True)
        
        # Compute cosine similarity
        cosine_scores = util.cos_sim(query_embedding, self.prototype_embeddings)[0]
        
        # Find the best match
        best_score, best_index = torch.max(cosine_scores, dim=0)
        best_score = best_score.item()
        best_domain = self.domains[best_index]

        # Debug output (can be uncommented for testing)
        # print(f"DEBUG: Query='{query}' | Best Match='{best_domain}' ({best_score:.4f})")

        if best_score < threshold:
            return "general"
        
        return best_domain

# Backwards compatibility wrapper
_shared_classifier = None

def classify_intent(query: str):
    global _shared_classifier
    if _shared_classifier is None:
        _shared_classifier = IntentClassifier()
    return _shared_classifier.predict_intent(query)


# Enhanced test suite
if __name__ == "__main__":
    classifier = IntentClassifier()
    
    test_queries = [
        # Food queries
        ("spicy masala dosa bangalore", "food"),
        ("sweet Indian dessert", "food"),
        ("iconic restaurant butter chicken", "food"),
        
        # Heritage queries
        ("ancient fort in rajasthan", "heritage"),
        ("historical monuments", "heritage"),
        ("mughal palace", "heritage"),
        
        # Travel queries
        ("valley of flowers nagaland", "travel"),
        ("hill station honeymoon", "travel"),
        ("trekking adventure ladakh", "travel"),
        ("mountain pass", "travel"),
        
        # Nature queries
        ("hidden waterfall meghalaya", "nature"),
        ("wildlife sanctuary", "nature"),
        ("national park tigers", "nature"),
        ("beautiful lake", "nature"),
        
        # Cultural queries
        ("traditional festival", "cultural"),
        ("tribal village", "cultural"),
        ("folk art performance", "cultural"),
        
        # Architecture queries
        ("temple architecture", "architecture"),
        ("beautiful building design", "architecture"),
        
        # General/Ambiguous
        ("random gibberish text", "general"),
    ]

    print("\n" + "="*60)
    print("INTENT CLASSIFIER TEST RESULTS")
    print("="*60)
    
    correct = 0
    total = len(test_queries)
    
    for query, expected in test_queries:
        predicted = classifier.predict_intent(query)
        is_correct = predicted == expected
        correct += is_correct
        
        status = "✅" if is_correct else "❌"
        print(f"{status} Query: '{query}'")
        print(f"   Expected: {expected} | Predicted: {predicted}\n")
    
    print("="*60)
    print(f"ACCURACY: {correct}/{total} ({round(correct/total * 100, 1)}%)")
    print("="*60)