Update app.py

app.py

@@ -1,5 +1,3 @@
-
-
 import streamlit as st
 import pandas as pd
 from transformers import pipeline
@@ -34,7 +32,7 @@ model_classes ={
     13: "WiFi",
 }
 
-@st.cache(suppress_st_warning=True,suppress_st_warning=False)
+@st.cache(allow_output_mutation=False=True,suppress_st_warning=False)
 def load_t5():
     model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
 
@@ -42,23 +40,23 @@ def load_t5():
     return model, tokenizer
 
 
-@st.cache(suppress_st_warning=True,suppress_st_warning=False)
+@st.cache(allow_output_mutation=False=True,suppress_st_warning=False)
 def custom_model():
     return pipeline("summarization", model="my_awesome_sum/")
 
 
-@st.cache(suppress_st_warning=True,suppress_st_warning=False)
+@st.cache(allow_output_mutation=False=True,suppress_st_warning=False)
 def convert_df(df):
     # IMPORTANT: Cache the conversion to prevent computation on every rerun
     return df.to_csv(index=False).encode("utf-8")
 
 
-@st.cache(suppress_st_warning=True,suppress_st_warning=False)
+@st.cache(allow_output_mutation=False=True,suppress_st_warning=False)
 def load_one_line_summarizer(model):
     return model.load_model("t5", "snrspeaks/t5-one-line-summary")
 
 
-@st.cache(suppress_st_warning=True,suppress_st_warning=False)
+@st.cache(allow_output_mutation=False=True,suppress_st_warning=False)
 def classify_category():
     tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
     new_model = load_model("model")
@@ -76,14 +74,6 @@ summarizer_option = st.selectbox(
 classification = st.checkbox("Classify Category", value=True)
 
 ps = st.empty()
-cache_button=st.empty()
-msg=st.empty()
-mutable_object = get_mutable()
-
-if cache_button.button("Clear"):
-    mutable_object.clear()
-    st.balloons()
-    msg.error("Cache is cleared, please reload to scrape new values")
     
 if st.button("Process",type="primary"):
     cancel_button=st.empty()