m7mdal7aj commited on
Commit
6b5fbd5
1 Parent(s): 1d9397e

Update my_model/results/demo.py

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Files changed (1) hide show
  1. my_model/results/demo.py +3 -7
my_model/results/demo.py CHANGED
@@ -1,5 +1,4 @@
1
  import os
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- from my_model.utilities.gen_utilities import log_function_call
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  import altair as alt
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  from my_model.config import evaluation_config as config
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  import streamlit as st
@@ -41,7 +40,6 @@ class ResultDemonstrator:
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  """
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  st.dataframe(data)
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- @log_function_call
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  def calculate_and_append_data(self, data_list: list, score_column: str, model_config: str) -> None:
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  """
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  Calculates mean scores by category and appends them to the data list.
@@ -59,8 +57,7 @@ class ResultDemonstrator:
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  "Configuration": model_config,
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  "Mean Value": round(mean_value * 100, 2)
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  })
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-
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- @log_function_call
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  def display_ablation_results_per_question_category(self) -> None:
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  """Displays ablation results per question category for each model configuration."""
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@@ -90,7 +87,7 @@ class ResultDemonstrator:
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  with st.expander(f"{score_type.upper()} Scores per Question Category and Model Configuration"):
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  self.display_table(results_df)
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- @log_function_call
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  def display_main_results(self) -> None:
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  """Displays the main model results from the Scores sheet, these are displayed from the file directly."""
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  main_scores = pd.read_excel(config.EVALUATION_DATA_PATH, sheet_name="Scores", index_col=0)
@@ -98,7 +95,7 @@ class ResultDemonstrator:
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  main_scores.reset_index()
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  self.display_table(main_scores)
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- @log_function_call
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  def plot_token_count_vs_scores(self, conf: str, model_name: str, score_name: str = 'VQA Score') -> None:
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  """
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  Plots an interactive scatter plot comparing token count to VQA or EM scores using Altair.
@@ -194,7 +191,6 @@ class ResultDemonstrator:
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  return 'color: black;'
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- @log_function_call
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  def show_samples(self, num_samples: int = 3) -> None:
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  """
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  Displays random sample images and their associated models answers and evaluations.
 
1
  import os
 
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  import altair as alt
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  from my_model.config import evaluation_config as config
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  import streamlit as st
 
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  """
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  st.dataframe(data)
42
 
 
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  def calculate_and_append_data(self, data_list: list, score_column: str, model_config: str) -> None:
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  """
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  Calculates mean scores by category and appends them to the data list.
 
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  "Configuration": model_config,
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  "Mean Value": round(mean_value * 100, 2)
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  })
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+
 
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  def display_ablation_results_per_question_category(self) -> None:
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  """Displays ablation results per question category for each model configuration."""
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  with st.expander(f"{score_type.upper()} Scores per Question Category and Model Configuration"):
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  self.display_table(results_df)
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+
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  def display_main_results(self) -> None:
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  """Displays the main model results from the Scores sheet, these are displayed from the file directly."""
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  main_scores = pd.read_excel(config.EVALUATION_DATA_PATH, sheet_name="Scores", index_col=0)
 
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  main_scores.reset_index()
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  self.display_table(main_scores)
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+
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  def plot_token_count_vs_scores(self, conf: str, model_name: str, score_name: str = 'VQA Score') -> None:
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  """
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  Plots an interactive scatter plot comparing token count to VQA or EM scores using Altair.
 
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  return 'color: black;'
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193
 
 
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  def show_samples(self, num_samples: int = 3) -> None:
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  """
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  Displays random sample images and their associated models answers and evaluations.