Update fun_advaitbert.py
Browse files- fun_advaitbert.py +9 -0
fun_advaitbert.py
CHANGED
@@ -12,6 +12,7 @@ tf.get_logger().setLevel('ERROR')
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from huggingface_hub import InferenceClient
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import math
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import gradio as gr
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num_warmup_steps=1
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num_train_steps=1
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@@ -163,6 +164,8 @@ additional_inputs=[
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def predict_CTH(txt):
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print('Desc: ',txt)
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if (txt!='') and len(txt)>=3 and (count_special_character(txt)):
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valid_data = tf.data.Dataset.from_tensor_slices(([txt] , [1])) # 1 refers to 'entertainment' and 2 refers to 'sport'
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valid_data = (valid_data.map(to_feature_map).batch(1))
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@@ -201,8 +204,14 @@ def predict_CTH(txt):
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try:
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pred_duty=df_report_DUTY[df_report_DUTY['CTH']==str(pred_CTH)]['DUTY_RATE'].iloc[0]
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pred_desc=df_report[df_report['CTH Code']==str(pred_CTH)]['Concat Description'].iloc[0]
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except:
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pass
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Var_CTH.append(pred_CTH)
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from huggingface_hub import InferenceClient
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import math
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import gradio as gr
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from datetime import datetime
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num_warmup_steps=1
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num_train_steps=1
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def predict_CTH(txt):
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print('Desc: ',txt)
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now = datetime.now()
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print("Time =", now)
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if (txt!='') and len(txt)>=3 and (count_special_character(txt)):
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valid_data = tf.data.Dataset.from_tensor_slices(([txt] , [1])) # 1 refers to 'entertainment' and 2 refers to 'sport'
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valid_data = (valid_data.map(to_feature_map).batch(1))
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try:
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pred_duty=df_report_DUTY[df_report_DUTY['CTH']==str(pred_CTH)]['DUTY_RATE'].iloc[0]
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except:
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pred_duty=''
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pass
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try:
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pred_desc=df_report[df_report['CTH Code']==str(pred_CTH)]['Concat Description'].iloc[0]
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except:
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pred_desc=''
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pass
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Var_CTH.append(pred_CTH)
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