"""
Criteria Evaluation Example

This example demonstrates how to evaluate agent outputs
against custom criteria using LLM-as-judge.
"""

import os
from praisonaiagents import Agent
from praisonaiagents.eval import CriteriaEvaluator

# Check if we have an API key
has_api_key = os.getenv("OPENAI_API_KEY") is not None

if has_api_key:
    print("--- Testing Criteria Evaluation with Agent ---")
    # Create a customer service agent
    agent = Agent(
        instructions="You are a friendly customer service agent. Be helpful and empathetic."
    )

    # Create criteria evaluator with numeric scoring
    evaluator = CriteriaEvaluator(
        criteria="Response is helpful, empathetic, and provides a clear solution",
        agent=agent,
        input_text="I'm frustrated because my order hasn't arrived yet.",
        scoring_type="numeric",  # Score 1-10
        threshold=7.0,           # Pass if score >= 7
        num_iterations=2,
        verbose=True
    )

    # Run evaluation
    result = evaluator.run(print_summary=True)

    print("\nNumeric Scoring Results:")
    print(f"  Average Score: {result.avg_score}/10")
    print(f"  Pass Rate: {result.pass_rate:.1%}")
    print(f"  Passed: {result.passed}")

    # Binary scoring example (pass/fail)
    print("\n--- Testing Binary Scoring ---")
    binary_evaluator = CriteriaEvaluator(
        criteria="Response does not contain any offensive language",
        agent=agent,
        input_text="Tell me a joke",
        scoring_type="binary",  # Pass or Fail
        verbose=True
    )

    binary_result = binary_evaluator.run(print_summary=True)
    
    # With on_fail callback
    print("\n--- Testing Failure Callback ---")
    def handle_failure(score):
        print(f"ALERT: Evaluation failed with score {score.score}")
        print(f"Reasoning: {score.reasoning}")

    callback_evaluator = CriteriaEvaluator(
        criteria="Response is professional and helpful",
        agent=agent,
        input_text="Help me",
        on_fail=handle_failure,
        threshold=8.0
    )

    callback_evaluator.run()
else:
    print("⚠️  No OPENAI_API_KEY found. Skipping agent-based criteria evaluation...")

# Test pre-generated output evaluation (doesn't need agent)
print("\n--- Testing Pre-generated Output Evaluation ---")
if has_api_key:
    output = "I understand your frustration. Let me check on your order right away."
    try:
        result2 = evaluator.evaluate_output(output, print_summary=True)
        print(f"Pre-generated output score: {result2.avg_score}/10")
    except Exception as e:
        print(f"Pre-generated evaluation failed: {e}")
else:
    print("⚠️  Skipping pre-generated evaluation (no API key)")
    print("To run full evaluation, set OPENAI_API_KEY environment variable")