Kevin Louis commited on
Commit
d636407
1 Parent(s): 2c8f0e3

updated app.py

Browse files

Removed share=True from ChatToSequence.launch(). Share=True parameter shouldn't be active when app is ran in spaces. It caused an runtime error

Files changed (1) hide show
  1. app.py +6 -9
app.py CHANGED
@@ -9,10 +9,7 @@ from helper import list_at_index_0, list_at_index_1, logger
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  def chat_to_sequence(sequence, user_query):
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- if sequence is None:
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- gr.Warning("Sequence Is Empty. Please Input A Sequence")
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- if user_query is None:
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- gr.Warning("Query Is Empty. Please Input A Query")
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  # Log information to a CSV file
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  log_filename = "CTS_user_log.csv"
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@@ -66,8 +63,7 @@ def chat_to_sequence(sequence, user_query):
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  # Semantic similarity search user query against sample queries
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  index_result = ref_query_ds.get_nearest_examples("all-mpnet-base-v2_embeddings", query_embedding, k=3)
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- print(index_result)
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-
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  # Retrieve results from dataset object
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  scores, examples = index_result
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@@ -93,8 +89,7 @@ def chat_to_sequence(sequence, user_query):
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  # Description of query code to be executed
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  query_code_description = code_function_mapping[code_function_mapping['code'] == query_code]['description'].values[0]
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- # Print the query with the highest score
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- print(ref_question, query_code, query_score)
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  similarity_metric = "k nearest neighbours"
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  ref_question_2 = sorted_df.iloc[1]['question']
@@ -102,6 +97,7 @@ def chat_to_sequence(sequence, user_query):
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  query_score_2 = sorted_df.iloc[1]['score']
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  query_score_3 = sorted_df.iloc[1]['score']
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  log_data = [
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  user_query,
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  ref_question,
@@ -116,6 +112,7 @@ def chat_to_sequence(sequence, user_query):
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  proximal_lower_threshold,
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  proximal_upper_threshold,
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  ]
 
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  # Check the query score against threshold values
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  if query_score >= proximal_upper_threshold:
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  response = threshold_exceeded_message
@@ -172,4 +169,4 @@ ChatToSequence = gr.Interface(
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  ],
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  ).queue()
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- ChatToSequence.launch(share=True)
 
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  def chat_to_sequence(sequence, user_query):
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+
 
 
 
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  # Log information to a CSV file
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  log_filename = "CTS_user_log.csv"
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  # Semantic similarity search user query against sample queries
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  index_result = ref_query_ds.get_nearest_examples("all-mpnet-base-v2_embeddings", query_embedding, k=3)
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+
 
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  # Retrieve results from dataset object
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  scores, examples = index_result
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  # Description of query code to be executed
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  query_code_description = code_function_mapping[code_function_mapping['code'] == query_code]['description'].values[0]
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+ # Extra log entities
 
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  similarity_metric = "k nearest neighbours"
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  ref_question_2 = sorted_df.iloc[1]['question']
 
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  query_score_2 = sorted_df.iloc[1]['score']
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  query_score_3 = sorted_df.iloc[1]['score']
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+ # logger function log_data parameter input
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  log_data = [
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  user_query,
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  ref_question,
 
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  proximal_lower_threshold,
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  proximal_upper_threshold,
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  ]
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+
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  # Check the query score against threshold values
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  if query_score >= proximal_upper_threshold:
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  response = threshold_exceeded_message
 
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  ],
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  ).queue()
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+ ChatToSequence.launch()