Upload 17 files

#5
by awinml - opened
app.py CHANGED
@@ -74,7 +74,7 @@ with st.sidebar:
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  )
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  )
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- use_bm25 = st.checkbox("Use 2-Stage Retrieval (BM25)")
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  num_candidates = int(
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  st.number_input(
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  "Number of Candidates to Generate:",
@@ -252,7 +252,7 @@ with tab2:
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  for ticker, quarter, year in ticker_year_quarter_tuples_list:
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  file_text = retrieve_transcript(data, year, quarter, ticker)
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  with st.expander(f"See Transcript - {quarter} {year}"):
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- st.subheader("Earnings Call Transcript - {quarter} {year}:")
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  stx.scrollableTextbox(
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  file_text,
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  height=700,
@@ -262,5 +262,5 @@ with tab2:
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  else:
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  st.write(
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- "No specific document chosen. Please mention Ticker and Duration in Question."
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  )
 
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  )
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  )
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+ use_bm25 = st.checkbox("Use 2-Stage Retrieval (BM25)", value=True)
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  num_candidates = int(
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  st.number_input(
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  "Number of Candidates to Generate:",
 
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  for ticker, quarter, year in ticker_year_quarter_tuples_list:
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  file_text = retrieve_transcript(data, year, quarter, ticker)
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  with st.expander(f"See Transcript - {quarter} {year}"):
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+ st.subheader(f"Earnings Call Transcript - {quarter} {year}:")
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  stx.scrollableTextbox(
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  file_text,
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  height=700,
 
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  else:
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  st.write(
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+ "No specific document chosen. Please mention Ticker and Duration in the Question."
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  )
utils/__pycache__/prompts.cpython-38.pyc CHANGED
Binary files a/utils/__pycache__/prompts.cpython-38.pyc and b/utils/__pycache__/prompts.cpython-38.pyc differ
 
utils/__pycache__/retriever.cpython-38.pyc CHANGED
Binary files a/utils/__pycache__/retriever.cpython-38.pyc and b/utils/__pycache__/retriever.cpython-38.pyc differ
 
utils/__pycache__/vector_index.cpython-38.pyc CHANGED
Binary files a/utils/__pycache__/vector_index.cpython-38.pyc and b/utils/__pycache__/vector_index.cpython-38.pyc differ
 
utils/models.py CHANGED
@@ -105,7 +105,6 @@ def get_vicuna_text_gen_model():
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  return client
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- @st.cache_resource
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  def get_bm25_model(data):
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  corpus = data.Text.tolist()
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  corpus_clean = [preprocess_text(x) for x in corpus]
 
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  return client
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  def get_bm25_model(data):
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  corpus = data.Text.tolist()
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  corpus_clean = [preprocess_text(x) for x in corpus]