Upload app.py

app.py

@@ -29,7 +29,7 @@ st.write(
 
 query_text = st.text_input("Input Query", value="Who is the CEO of Apple?")
 
-years_choice = ["2016", "2017", "2018", "2019", "2020"]
+years_choice = ["2020", "2019", "2018", "2017", "2016"]
 
 year = st.selectbox("Year", years_choice)
 
@@ -50,7 +50,7 @@ ticker_choice = [
 
 ticker = st.selectbox("Company", ticker_choice)
 
-num_results = int(st.number_input("Number of Results to query", 1, 5, value=3))
+num_results = int(st.number_input("Number of Results to query", 1, 5, value=5))
 
 
 # Choose encoder model
@@ -82,11 +82,11 @@ elif encoder_model == "SGPT":
     retriever_model = get_sgpt_embedding_model()
 
 
-window = int(st.number_input("Sentence Window Size", 0, 3, value=0))
+window = int(st.number_input("Sentence Window Size", 0, 5, value=3))
 
 threshold = float(
     st.number_input(
-        label="Similarity Score Threshold", step=0.05, format="%.2f", value=0.55
+        label="Similarity Score Threshold", step=0.05, format="%.2f", value=0.35
     )
 )
 
@@ -103,7 +103,7 @@ query_results = query_pinecone(
     threshold,
 )
 
-if threshold <= 0.60:
+if threshold <= 0.90:
     context_list = sentence_id_combine(data, query_results, lag=window)
 else:
     context_list = format_query(query_results)