deploy

app.py

@@ -50,8 +50,8 @@ def selectbox_with_default(text, values, default=DEFAULT, sidebar=False):
     func = st.sidebar.selectbox if sidebar else st.selectbox
     return func(text, np.insert(np.array(values, object), 0, default))
 
-@st.cache(allow_output_mutation=True)
-def neuralqa():
+@st.cache_resource()
+def loadmodels():
     model = T5ForConditionalGeneration.from_pretrained("wanderer2k1/T5-LawsQA")
     tokenizer = T5TokenizerFast.from_pretrained("wanderer2k1/T5-LawsQA")
 
@@ -98,16 +98,11 @@ if 'bm25' not in st.session_state:
 
 #4: model
 if 'model' not in st.session_state:
-    st.session_state['tokenizer'], st.session_state['model'], st.session_state['bi_encoder'] = neuralqa()
+    st.session_state['tokenizer'], st.session_state['model'], st.session_state['bi_encoder'] = loadmodels()
 
 #%%
 
-
-#%%
-
-
 def deploy(question):
-    # tokenizer, model, bi_encoder = neuralqa()
     top_k = returns  # Number of passages we want to retrieve with the bi-encoder
 
     tokenized_query = preprocess(question).split()

requirements.txt

@@ -1,8 +1,8 @@
-streamlit==1.3.1
+streamlit==1.25.0
 sentence_transformers==2.2.2
 numpy==1.20.1
 transformers==4.28.0
-pandas==1.2.3
+pandas==1.3.0
 textwrap3==0.9.2
 torch==1.12.0
 joblib==1.0.1