diff --git a/augur/tasks/data_analysis/message_insights/message_novelty.py b/augur/tasks/data_analysis/message_insights/message_novelty.py index 7821cbf278..6a4a9e8ef7 100644 --- a/augur/tasks/data_analysis/message_insights/message_novelty.py +++ b/augur/tasks/data_analysis/message_insights/message_novelty.py @@ -8,8 +8,8 @@ import numpy as np import pandas as pd from gensim.models.doc2vec import Doc2Vec, TaggedDocument -from keras.layers import Dense, Input -from keras.models import Model, load_model +#from keras.layers import Dense, Input +#from keras.models import Model, load_model from skimage.filters import threshold_otsu from sklearn import utils as skl_utils @@ -42,10 +42,13 @@ def build_model(max_epochs, vec_size, alpha, tag_data): # ''' def autoencoder(vec_input, train): + from keras.layers import Dense, Input input_dim = Input(shape = (vec_input, )) encoded1 = Dense(vec_input//2, activation='sigmoid')(input_dim) encoded2 = Dense(1, activation='sigmoid')(encoded1) + from keras.models import Model + # Decoder Layers decoded1 = Dense(vec_input//2, activation='tanh')(encoded2) decoded2 = Dense(vec_input, activation='tanh')(decoded1) @@ -170,6 +173,7 @@ def novelty_analysis(df_message, r_id, models_dir, full_train=True): # Pretrained AE model already exists, directly predict else: + from keras.models import load_model threshold = 0 ae = load_model(f'{models_dir}/{r_id}_uniq.h5') #logger.info('Loaded pretrained AE model for repo') diff --git a/pyproject.toml b/pyproject.toml index 1929ca83b0..beb1d0490f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -64,7 +64,9 @@ dependencies = [ "slack==0.0.2", "SQLAlchemy==2.0.22", "tenacity==8.3.0", - "tensorflow==2.15.0", + "tensorflow==2.15.0; sys_platform == 'linux'", + "tensorflow==2.15.0; sys_platform == 'darwin' and platform_machine == 'x86_64'", + "tensorflow-macos==2.15.0; sys_platform == 'darwin' and platform_machine == 'arm64'", "textblob==0.15.3", "textual>=0.73.0", "tomli>=2.2.1 ; python_full_version < 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