Refactor

app.py

@@ -4,36 +4,12 @@ from logging import Logger
 from logging.handlers import SysLogHandler
 
 import streamlit as st
-import tokenizers
-import torch
-from transformers import Pipeline, pipeline
-
-from utils import get_answer, get_context
-
-
-@st.cache(
-    hash_funcs={
-        torch.nn.parameter.Parameter: lambda _: None,
-        tokenizers.Tokenizer: lambda _: None,
-        tokenizers.AddedToken: lambda _: None,
-    },
-    allow_output_mutation=True,
-    show_spinner=False,
-)
-def load_engine() -> Pipeline:
-
-    nlp_qa = pipeline(
-        "question-answering",
-        model="mrm8488/bert-italian-finedtuned-squadv1-it-alfa",
-        tokenizer="mrm8488/bert-italian-finedtuned-squadv1-it-alfa",
-    )
-
-    return nlp_qa
 
+from utils import get_answer, get_context, load_engine
 
 if ("syslog" not in st.session_state) and ("logger" not in st.session_state):
     syslog = SysLogHandler(
-        address=(st.secrets["logging_address"], int(st.secrets["logging_port"]))
+        address=("logs4.papertrailapp.com", 49071)
     )
     logger = logging.getLogger()
     logger.setLevel(logging.INFO)
@@ -46,6 +22,7 @@ with st.spinner(
 ):
 
     engine = load_engine()
+    context = get_context()
 
 st.title("Le risposte alle tue domande personali")
 
@@ -53,10 +30,9 @@ input = st.text_input("Scrivi una domanda in italiano e comparirà la risposta!"
 
 if input:
     try:
-        context = get_context()
-        st.session_state["logger"].info(input)
         answer = get_answer(input, context, engine)
         st.subheader(answer)
+        st.session_state["logger"].info(f"{input} {answer}")
     except:
 
         st.error(

utils.py

@@ -1,5 +1,9 @@
 import datetime
 import json
+import streamlit as st
+import tokenizers
+import torch
+from transformers import Pipeline, pipeline
 
 
 def get_answer(input, context, engine):
@@ -9,6 +13,7 @@ def get_answer(input, context, engine):
     return answer["answer"]
 
 
+@st.cache
 def get_context():
 
     BIRTHYEAR = 1952
@@ -25,3 +30,23 @@ def get_context():
         )
 
     return context
+
+
+@st.cache(
+    hash_funcs={
+        torch.nn.parameter.Parameter: lambda _: None,
+        tokenizers.Tokenizer: lambda _: None,
+        tokenizers.AddedToken: lambda _: None,
+    },
+    allow_output_mutation=True,
+    show_spinner=False,
+)
+def load_engine() -> Pipeline:
+
+    nlp_qa = pipeline(
+        "question-answering",
+        model="mrm8488/bert-italian-finedtuned-squadv1-it-alfa",
+        tokenizer="mrm8488/bert-italian-finedtuned-squadv1-it-alfa",
+    )
+
+    return nlp_qa