add code for building model and tokenizer

model.py

@@ -0,0 +1,34 @@
+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)