from models.model_seeds import seeds, seed2str

# ISSUES = ['Anxiety','Suicide']
ISSUES = [k for k,_ in seeds.items()]
SOURCES = [
    # "CTL_llama2",
    "CTL_llama3",
    # "CTL_mistral",
    'OA_rolemodel', 
        #    'OA_finetuned',
]
SOURCES_LAB = {"OA_rolemodel":'OpenAI GPT4o',
               "OA_finetuned":'Finetuned OpenAI',
            #    "CTL_llama2": "Llama 2",
               "CTL_llama3": "Llama 3",
               "CTL_mistral": "Mistral",
               }

ENDPOINT_NAMES = {
    # "CTL_llama2": "texter_simulator",
    "CTL_llama3": {
        "name": "texter_simulator_llm",
        "model_type": "text-generation"
    },
    # "CTL_llama3": {
    #     "name": "databricks-meta-llama-3-1-70b-instruct",
    #     "model_type": "text-generation"
    # },
    # 'CTL_llama2': "llama2_convo_sim",
    # "CTL_mistral": "convo_sim_mistral",
    "CPC": {
        "name": "phase_classifier",
        "model_type": "classificator"
    },
    "BadPractices": {
        "name": "training_adherence_bp",
        "model_type": "classificator"
    },
    "training_adherence": {
        "name": "training_adherence",
        "model_type": "text-completion"
    },
}

def source2label(source):
    return SOURCES_LAB[source]

def issue2label(issue):
    return seed2str.get(issue, "GCT")

ENVIRON = "prod"

DB_SCHEMA = 'prod_db' if ENVIRON == 'prod' else 'test_db' 
DB_CONVOS = 'conversations'
DB_COMPLETIONS = 'comparison_completions'
DB_BATTLES = 'battles'
DB_ERRORS = 'completion_errors'
DB_CPC = "cpc_comparison"
DB_BP = "bad_practices_comparison"
DB_TA = "convo_scoring_comparison"

MAX_MSG_COUNT = 60
WARN_MSG_COUT = int(MAX_MSG_COUNT*0.8)