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| import os | |
| import numpy as np | |
| import tensorflow as tf | |
| import pandas as pd | |
| import time | |
| from recommenders.models.sasrec.model import SASREC | |
| from tabulate import tabulate | |
| import streamlit as st | |
| class SASREC_Vessl(SASREC): | |
| def __init__(self, **kwargs): | |
| super().__init__(**kwargs) | |
| def predict_next(self, input): | |
| # seq generation | |
| training = False | |
| seq = np.zeros([self.seq_max_len], dtype=np.int32) | |
| idx = self.seq_max_len - 1 | |
| idx -= 1 | |
| for i in input[::-1]: | |
| seq[idx] = i | |
| idx -= 1 | |
| if idx == -1: | |
| break | |
| input_seq = np.array([seq]) | |
| candidate = np.expand_dims(np.arange(1, self.item_num + 1, 1), axis=0) | |
| mask = tf.expand_dims(tf.cast(tf.not_equal(input_seq, 0), tf.float32), | |
| -1) | |
| seq_embeddings, positional_embeddings = self.embedding(input_seq) | |
| seq_embeddings += positional_embeddings | |
| seq_embeddings *= mask | |
| seq_attention = seq_embeddings | |
| seq_attention = self.encoder(seq_attention, training, mask) | |
| seq_attention = self.layer_normalization(seq_attention) # (b, s, d) | |
| seq_emb = tf.reshape( | |
| seq_attention, | |
| [tf.shape(input_seq)[0] * self.seq_max_len, self.embedding_dim], | |
| ) # (b*s, d) | |
| candidate_emb = self.item_embedding_layer(candidate) # (b, s, d) | |
| candidate_emb = tf.transpose(candidate_emb, perm=[0, 2, 1]) # (b, d, s) | |
| test_logits = tf.matmul(seq_emb, candidate_emb) | |
| test_logits = tf.reshape( | |
| test_logits, | |
| [tf.shape(input_seq)[0], self.seq_max_len, self.item_num], | |
| ) | |
| test_logits = test_logits[:, -1, :] # (1, 101) | |
| predictions = np.array(test_logits)[0] | |
| return predictions | |
| def load_model(): | |
| model_config = { | |
| "MAXLEN": 50, | |
| "NUM_BLOCKS": 2, # NUMBER OF TRANSFORMER BLOCKS | |
| "HIDDEN_UNITS": 100, # NUMBER OF UNITS IN THE ATTENTION CALCULATION | |
| "NUM_HEADS": 1, # NUMBER OF ATTENTION HEADS | |
| "DROPOUT_RATE": 0.2, # DROPOUT RATE | |
| "L2_EMB": 0.0, # L2 REGULARIZATION COEFFICIENT | |
| "NUM_NEG_TEST": 100, | |
| # NUMBER OF NEGATIVE EXAMPLES PER POSITIVE EXAMPLE | |
| } | |
| model = SASREC_Vessl( | |
| item_num=12101, # should be changed according to dataset | |
| seq_max_len=model_config.get("MAXLEN"), | |
| num_blocks=model_config.get("NUM_BLOCKS"), | |
| embedding_dim=model_config.get("HIDDEN_UNITS"), | |
| attention_dim=model_config.get("HIDDEN_UNITS"), | |
| attention_num_heads=model_config.get("NUM_HEADS"), | |
| dropout_rate=model_config.get("DROPOUT_RATE"), | |
| conv_dims=[100, 100], | |
| l2_reg=model_config.get("L2_EMB"), | |
| num_neg_test=model_config.get("NUM_NEG_TEST"), | |
| ) | |
| if os.path.isfile('best.index') and os.path.isfile('best.data-00000-of-00001'): | |
| model.load_weights('best').expect_partial() | |
| return model | |
| def main(): | |
| st.title('Self-Attentive Sequential Recommendation(SASRec)') | |
| model = load_model() | |
| st.write(model) | |
| numbers = st.text_input | |
| st.write(numbers) | |
| if __name__ == '__main__': | |
| main() | |