# LightEval_Mimir.py
'''This document outlines hte LightEval setu for tracking performance metrics of Mimir, to be sent to the trackio page for viszulization.'''

# Imports
from lighteval.metrics.metrics_sample import BertScore, ROUGE
from lighteval.tasks.requests import Doc

async def evaluate_educational_quality(user_query, response, thread_id):
    """Dynamic evaluation using LightEval metrics"""
    # Create ephemeral task for this turn
    doc = Doc(
        task_name=f"turn_{thread_id}",
        query=user_query,
        choices=[response],
        gold_index=-1,  # No ground truth initially
        specific_output=response
    )
    
    # Use BertScore for semantic quality
    bert_score = BertScore().compute(doc)
    
    # Custom educational coherence metric
    educational_indicators = {
        'has_examples': 'example' in response.lower(),
        'structured_explanation': '##' in response or '1.' in response,
        'appropriate_length': 100 < len(response) < 1500,
        'encourages_learning': any(phrase in response.lower() 
            for phrase in ['practice', 'try', 'consider', 'think about'])
    }
    
    return {
        'semantic_quality': bert_score,
        'educational_score': sum(educational_indicators.values()) / len(educational_indicators),
        'response_time': time.time() - start_time
    }

def track_rag_performance(query, retrieved_docs, used_in_response):
    """Evaluate RAG retrieval quality"""
    from lighteval.metrics.utils.metric_utils import SampleLevelMetric
    
    # Track retrieval-to-response alignment
    retrieval_relevance = calculate_relevance(query, retrieved_docs)
    retrieval_usage = len(used_in_response) / len(retrieved_docs) if retrieved_docs else 0
    
    # Log to trackio with LightEval structure
    metric_payload = {
        "evaluation_id": str(uuid.uuid4()),
        "task": "rag_retrieval",
        "metrics": {
            "retrieval_relevance": retrieval_relevance,
            "retrieval_usage_rate": retrieval_usage,
            "num_docs_retrieved": len(retrieved_docs)
        },
        "metadata": {
            "query": query[:100],
            "sources": [doc.metadata.get('source') for doc in retrieved_docs]
        }
    }
    
    send_evaluation_to_trackio(metric_payload)

def evaluate_prompt_classification(predicted_mode, actual_conversation_outcome, thread_id):
    """Track prompt classifier accuracy in production"""
    
    # Did the predicted mode lead to successful interaction?
    success_indicators = {
        'discovery_mode': lambda outcome: 'clarified_topic' in outcome,
        'teaching_mode': lambda outcome: outcome.get('quality_score', 0) > 3.5,
        'conversational': lambda outcome: outcome.get('user_satisfied', False)
    }
    
    mode_was_correct = success_indicators.get(
        predicted_mode, 
        lambda x: True
    )(actual_conversation_outcome)
    
    # Create LightEval-style evaluation
    from lighteval.metrics import Metrics
    accuracy_metric = Metrics.ACCURACY if mode_was_correct else 0
    
    return {
        "prompt_classifier_accuracy": accuracy_metric,
        "predicted_mode": predicted_mode,
        "conversation_length": len(conversation_state)
    }

def process_user_feedback(response_id, feedback_type, conversation_state):
    """Convert user feedback to LightEval ground truth"""
    
    last_exchange = {
        "query": conversation_state[-2]["content"],  # User's question
        "response": conversation_state[-1]["content"], # Agent's response
        "gold_index": 0 if feedback_type == "thumbs_up" else -1
    }
    
    # Create retrospective evaluation with ground truth
    from lighteval.tasks.requests import Doc
    doc = Doc(
        task_name="user_feedback_eval",
        query=last_exchange["query"],
        choices=[last_exchange["response"]],
        gold_index=last_exchange["gold_index"]
    )
    
    # Now you have ground truth for accuracy metrics!
    accuracy = 1.0 if feedback_type == "thumbs_up" else 0.0
    
    return {"user_feedback_accuracy": accuracy, "response_id": response_id}