kajila commited on
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c64fa1c
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1 Parent(s): d477bd2

Update app.py

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  1. app.py +2 -5
app.py CHANGED
@@ -143,14 +143,14 @@ Here are some documents that are relevant to the question.
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  {question}
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  ```
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  """
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- # Define the predict function
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  def predict(user_input, company):
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  filter = f"dataset/{company}-10-k-2023.pdf"
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  relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source": filter})
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  # Create context for query
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  context_list = [d.page_content for d in relevant_document_chunks]
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- context_for_query = ".".join(context_list)
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  # Create messages
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  prompt = [
@@ -172,9 +172,6 @@ def predict(user_input, company):
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  return prediction
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-
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-
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-
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  examples = [
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  ["What are the company's policies and frameworks regarding AI ethics, governance, and responsible AI use as detailed in their 10-K reports?", "AWS"],
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  ["What are the primary business segments of the company, and how does each segment contribute to the overall revenue and profitability?", "AWS"],
 
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  {question}
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  ```
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  """
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+ # Define the predict function
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  def predict(user_input, company):
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  filter = f"dataset/{company}-10-k-2023.pdf"
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  relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source": filter})
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  # Create context for query
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  context_list = [d.page_content for d in relevant_document_chunks]
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+ context_for_query = ".".join(context_list) # Ensure this is inside the function
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  # Create messages
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  prompt = [
 
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  return prediction
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  examples = [
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  ["What are the company's policies and frameworks regarding AI ethics, governance, and responsible AI use as detailed in their 10-K reports?", "AWS"],
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  ["What are the primary business segments of the company, and how does each segment contribute to the overall revenue and profitability?", "AWS"],