chore: update streamlit deprecation

app.py

@@ -49,8 +49,6 @@ else:
     device = "cpu"
 max_len = st.sidebar.slider("Max length", 32, 512, 256, 32)
 beam_size = st.sidebar.slider("Beam size", 1, 10, 3, 1)
-tokenizer = load_tokenizer(model_name)
-model = load_model(model_name, device)
 
 # create a text input box for each of the following item
 # CHỈ TIÊU	ĐƠN VỊ	ĐIỀU KIỆN	KPI mục tiêu tháng	Tháng 9.2022	Đánh giá	T8.2022	So sánh T8.2022 Tăng giảm	T9.2021	So sánh T9.2021 Tăng giảm
@@ -138,13 +136,20 @@ data = {
     "Previous year": previous_year,
 }
 
+tokenizer = load_tokenizer(model_name)
+model = load_model(model_name, device)
 
 if st.button("Generate"):
-    with st.spinner("Generating..."):
-        input_string = make_input_sentence_from_strings(data)
-        print(input_string)
-        descriptions = generate_description(
-            input_string, model, tokenizer, device, max_len, model_name, beam_size
-        )
-
-        st.success(descriptions)
+    if objective_name == "":
+        st.error("Please input objective name")
+    elif unit == "":
+        st.error("Please input unit")
+    else:
+        with st.spinner("Generating..."):
+            input_string = make_input_sentence_from_strings(data)
+            print(input_string)
+            descriptions = generate_description(
+                input_string, model, tokenizer, device, max_len, model_name, beam_size
+            )
+
+            st.success(descriptions)

utils.py

@@ -15,21 +15,7 @@ import streamlit as st
 from typing import Dict, List
 
 
-def get_model(args):
-    print(f"Using model {args.model_name}")
-    model = AutoModelForSeq2SeqLM.from_pretrained(args.model_name)
-    model.to(args.device)
-
-    if args.load_model_path:
-        print(f"Loading model from {args.load_model_path}")
-        model.load_state_dict(
-            torch.load(args.load_model_path, map_location=torch.device(args.device))
-        )
-
-    return model
-
-
-@st.cache(allow_output_mutation=True)
+@st.cache_resource
 def load_model(model_name, device):
     print(f"Using model {model_name}")
     os.makedirs("cache", exist_ok=True)
@@ -46,7 +32,7 @@ def load_model(model_name, device):
     return model
 
 
-@st.cache(allow_output_mutation=True)
+@st.cache_resource
 def load_tokenizer(model_name):
     print(f"Loading tokenizer {model_name}")
     if "mbart" in model_name.lower():