AliMustapha commited on
Commit
9d378bb
·
1 Parent(s): a0b0a41

remove printing

Browse files
Files changed (2) hide show
  1. app.py +1 -1
  2. get_gender.py +0 -1
app.py CHANGED
@@ -51,7 +51,7 @@ class GenderPredictorApp:
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  Gender_Percentage=plot.get_gender_percentage(first_commit_dates)
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  Results=first_commit_dates[first_commit_dates["Predicted_Gender"]!="Unknown"]
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  Results=Region_predictor.get_region(Results)
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-
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  merged_df = df.merge(Results[["Author","sub-region-prediction","Predicted_Gender","Confidence"]], on=["Author"])
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  # Group by Year and Predicted_Gender, then count the occurrences
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  commit_per_gender_counts = merged_df.groupby(['Year', 'Predicted_Gender']).size().reset_index(name='Count')
 
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  Gender_Percentage=plot.get_gender_percentage(first_commit_dates)
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  Results=first_commit_dates[first_commit_dates["Predicted_Gender"]!="Unknown"]
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  Results=Region_predictor.get_region(Results)
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+
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  merged_df = df.merge(Results[["Author","sub-region-prediction","Predicted_Gender","Confidence"]], on=["Author"])
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  # Group by Year and Predicted_Gender, then count the occurrences
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  commit_per_gender_counts = merged_df.groupby(['Year', 'Predicted_Gender']).size().reset_index(name='Count')
get_gender.py CHANGED
@@ -30,7 +30,6 @@ class GenderPredictor:
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  proba=100
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  if EMAIL_re.match(name):
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- print("email")
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  prediction = 2
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  else:
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  proba=100
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  if EMAIL_re.match(name):
 
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  prediction = 2
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  else:
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