import os
import sys
from dotenv import load_dotenv
from openai import OpenAI

# Add project root to path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from services.llm_advisor import generate_explanation, LLM_MODELS, SYSTEM_PROMPT

load_dotenv()

def test_single_model(model_name, packet):
    print(f"\n--- Testing Model: {model_name} ---")
    api_key = os.getenv("NVIDIA_API_KEY")
    client = OpenAI(base_url="https://integrate.api.nvidia.com/v1", api_key=api_key)
    
    user_content = f"Result: {packet['prediction']}, Confidence: {packet['confidence']}, DTI: {packet['dti_ratio']}"
    
    try:
        completion = client.chat.completions.create(
            model=model_name,
            messages=[
                {"role": "system", "content": SYSTEM_PROMPT},
                {"role": "user", "content": user_content}
            ],
            temperature=0.1,
            max_tokens=512
        )
        content = completion.choices[0].message.content
        print(f"Response received ({len(content)} chars)")
        if "@@POINT@@" in content:
            print("SUCCESS: Output contains required @@POINT@@ delimiters.")
        else:
            print("FAILURE: Output missing @@POINT@@ delimiters.")
        return True
    except Exception as e:
        print(f"ERROR: Model {model_name} failed: {e}")
        return False

def test_fallback_logic(packet):
    print("\n--- Testing Full Fallback Workflow ---")
    # Forcing a failure by temporarily corrupting the model list in memory
    original_models = list(LLM_MODELS)
    # Inject a fake non-existent model at the start
    import services.llm_advisor as advisor
    advisor.LLM_MODELS = ["invalid/non-existent-model-1234"] + original_models
    
    print("Injected invalid model. Expecting fail-over to the first valid model...")
    response = advisor.generate_explanation(packet)
    
    if "@@POINT@@" in response:
        print("SUCCESS: Fallback logic correctly skipped the invalid model and used a secondary model.")
    else:
        print("FAILURE: Fallback logic did not return a valid AI summary.")
    
    # Reset models
    advisor.LLM_MODELS = original_models

if __name__ == "__main__":
    sample_packet = {
        "prediction": "Y",
        "confidence": 0.88,
        "dti_ratio": 32.5,
        "optimized_suggestion": "Excellent profile."
    }
    
    print("STARTING COMPREHENSIVE LLM VERIFICATION")
    
    # 1. Test every model in the list
    results = []
    for m in LLM_MODELS:
        results.append(test_single_model(m, sample_packet))
        
    # 2. Test the fallback mechanism
    test_fallback_logic(sample_packet)
    
    print("\nVERIFICATION COMPLETE")