import mlflow
import torch
import streamlit as st
from transformers import T5ForConditionalGeneration, T5Tokenizer

class InferenceBuilder:

    def __init__(self):
        # Load the necessary configuration from yaml
        self.model_config = mlflow.models.ModelConfig(development_config="model_config.yaml")
        self.cybersolve_config = self.model_config.get("cybersolve_config")
    
    def load_tokenizer(self):
        tokenizer_name = self.cybersolve_config.get("tokenizer_name")
        # make sure we cache this so that it doesnt redownload each time
        # cannot directly use @st.cache_resource on a method (function within a class) that has a self argument
        @st.cache_resource # https://docs.streamlit.io/develop/concepts/architecture/caching
        def load_and_cache_tokenizer(tokenizer_name):
            tokenizer = T5Tokenizer.from_pretrained(tokenizer_name) # CyberSolve uses the same tokenizer as the base FLAN-T5 model
            return tokenizer
        
        return load_and_cache_tokenizer(tokenizer_name)
    
    def load_model(self):
        model_name = self.cybersolve_config.get("model_name")
        # make sure we cache this so that it doesnt redownload each time
        # cannot directly use @st.cache_resource on a method (function within a class) that has a self argument
        @st.cache_resource # https://docs.streamlit.io/develop/concepts/architecture/caching
        def load_and_cache_model(model_name):
            # model = T5ForConditionalGeneration.from_pretrained(model_name).to("cuda") # put the model on our Space's GPU
            model = T5ForConditionalGeneration.from_pretrained(model_name) # move to GPU eventually
            return model
        
        return load_and_cache_model(model_name)