eaglelandsonce commited on
Commit
703b8c7
1 Parent(s): fc63ff2

Update app.py

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Files changed (1) hide show
  1. app.py +34 -2
app.py CHANGED
@@ -73,7 +73,39 @@ def query_vectara(query: str, case_number: int, filter_str="", lambda_val=0.0) -
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  answer = result["responseSet"][0]["summary"][0]["text"]
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  return re.sub(r'\[\d+(,\d+){0,5}\]', '', answer)
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-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Streamlit UI
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  st.title('Vectara Query Interface')
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@@ -98,7 +130,7 @@ if st.button('Search'):
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  else:
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  st.error("Please enter a query to search.")
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-
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  # Initialize the HHEM model +++++++++++++++++++++++++++++++++++++++++++++++
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  model = CrossEncoder('vectara/hallucination_evaluation_model')
 
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  answer = result["responseSet"][0]["summary"][0]["text"]
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  return re.sub(r'\[\d+(,\d+){0,5}\]', '', answer)
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+
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+ # Title
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+ st.title('Vectara Query Interface')
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+
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+ # Combine case number selection with query input
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+ case_number = st.selectbox("Select Case Number:", range(2001, 2099), format_func=lambda x: f"Case Number = {x}")
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+
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+ # User input for query
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+ user_query = st.text_input("Enter your query:", "")
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+
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+ # Display case number with query input
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+ combined_query = f"{user_query} for {case_number}" if user_query else ""
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+
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+ # Advanced options
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+ st.sidebar.header("Advanced Options")
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+ filter_str = st.sidebar.text_input("Filter String:", "")
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+ lambda_val = st.sidebar.slider("Lambda Value:", min_value=0.0, max_value=1.0, value=0.025)
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+
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+ # Search button
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+ if st.button('Search'):
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+ if combined_query:
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+ with st.spinner('Querying Vectara...'):
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+ # Assuming `query_vectara` is a function that takes the combined query, filter string, and lambda value
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+ output = query_vectara(combined_query, filter_str, lambda_val)
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+ st.markdown("## Result")
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+ st.write(output)
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+ else:
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+ st.error("Please enter a query to search.")
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+
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+
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+
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+
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+ """
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  # Streamlit UI
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  st.title('Vectara Query Interface')
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  else:
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  st.error("Please enter a query to search.")
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+ """
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  # Initialize the HHEM model +++++++++++++++++++++++++++++++++++++++++++++++
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  model = CrossEncoder('vectara/hallucination_evaluation_model')