Duplicate from sivakornchong/name_gender_BERT

Co-authored-by: SIVAKORN CHONGFEUNGPRINYA <sivakornchong@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+./h5

README.md

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+---
+title: Name Gender BERT
+emoji: 🏃
+colorFrom: red
+colorTo: blue
+sdk: gradio
+sdk_version: 3.23.0
+app_file: app.py
+pinned: false
+duplicated_from: sivakornchong/name_gender_BERT
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from main import main_fn
+
+
+iface = gr.Interface(
+    fn=main_fn, 
+    inputs= [
+            gr.inputs.Textbox(type="text", label='Name')
+        ], 
+    outputs= [
+            gr.outputs.Textbox(type="text", label='Gender'),
+            # gr.outputs.Number(label = 'Percentage confidence')
+        ]
+    )
+iface.launch()

gender_v1_freezebert.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:da62d65566a82c6b846e56852812f384a5682978a573c0c9470aaaec71be1efa
+size 438157232

main.py

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+import os
+import pandas as pd
+# import keras
+import tensorflow as tf
+import tensorflow_hub as hub
+import tensorflow_text
+### This is to predict gender
+
+def main_fn(Name_):
+##Input structure into model is##
+    filename = "gender_v1_freezebert.h5"
+    if os.path.exists(filename):
+      model = tf.keras.models.load_model((filename), custom_objects={'KerasLayer':hub.KerasLayer})
+      print('loaded model')
+    else:
+        print('failed loading model')
+
+    prob = model.predict([Name_])
+    female_prob = prob[0][0]
+    if female_prob>0.5:
+        gender = 'female'
+        prob = int(female_prob*100)
+    else:
+        gender = 'male'
+        prob = int((1-female_prob)*100)
+
+    final_text = f"{gender}_confidence at {prob}%"
+    return final_text
+#To start returning the probability confidence at later stage    
+
+if __name__ == "__main__":
+    Name_ = 'John'
+    final_text = main_fn(Name_)

requirements.txt

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+keras
+tensorflow
+pandas
+tensorflow-hub
+tensorflow-text
+# scikit-learn==1.0.2