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7d73e2a
1
Parent(s):
5e99f3f
Update app.py
Browse files
app.py
CHANGED
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@@ -10,10 +10,7 @@ from tensorflow.nn import softmax
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import numpy as np
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from datetime import datetime
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import logging
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import
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-
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import gc
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date = datetime.now().strftime(r"%Y-%m-%d")
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model_classes = {
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@@ -171,7 +168,7 @@ if st.button("Process", type="primary"):
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for x in stqdm(range(len(text))):
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tf_o = softmax(tf_outputs["logits"][x], axis=-1)
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label = np.argmax(tf_o, axis=0)
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keys =
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classes.append(keys.get(label))
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output["sub theme"] = classes
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del classification_token, classification_model
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@@ -248,7 +245,7 @@ if st.button("Process", type="primary"):
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for x in stqdm(range(len(text))):
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tf_o = softmax(tf_outputs["logits"][x], axis=-1)
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label = np.argmax(tf_o, axis=0)
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keys =
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classes.append(keys.get(label))
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output["sub theme"] = classes
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del classification_token, classification_model
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@@ -296,7 +293,7 @@ if st.button("Process", type="primary"):
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keys = model_classes
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classes.append(keys.get(label))
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output["category"] = classes
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del classification_token,classification_model
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if sub_theme:
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classification_token, classification_model = classify_sub_theme()
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tf_batch = classification_token(
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@@ -313,7 +310,7 @@ if st.button("Process", type="primary"):
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for x in stqdm(range(len(text))):
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tf_o = softmax(tf_outputs["logits"][x], axis=-1)
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label = np.argmax(tf_o, axis=0)
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keys =
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classes.append(keys.get(label))
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output["sub theme"] = classes
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del classification_token, classification_model
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import numpy as np
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from datetime import datetime
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import logging
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from constants import sub_themes_dict
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date = datetime.now().strftime(r"%Y-%m-%d")
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model_classes = {
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for x in stqdm(range(len(text))):
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tf_o = softmax(tf_outputs["logits"][x], axis=-1)
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label = np.argmax(tf_o, axis=0)
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keys = sub_themes_dict
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classes.append(keys.get(label))
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output["sub theme"] = classes
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del classification_token, classification_model
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for x in stqdm(range(len(text))):
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tf_o = softmax(tf_outputs["logits"][x], axis=-1)
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label = np.argmax(tf_o, axis=0)
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keys = sub_themes_dict
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classes.append(keys.get(label))
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output["sub theme"] = classes
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del classification_token, classification_model
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keys = model_classes
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classes.append(keys.get(label))
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output["category"] = classes
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del classification_token, classification_model
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if sub_theme:
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classification_token, classification_model = classify_sub_theme()
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tf_batch = classification_token(
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for x in stqdm(range(len(text))):
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tf_o = softmax(tf_outputs["logits"][x], axis=-1)
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label = np.argmax(tf_o, axis=0)
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keys = sub_themes_dict
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classes.append(keys.get(label))
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output["sub theme"] = classes
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del classification_token, classification_model
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