Update models.py

models.py

@@ -296,3 +296,87 @@ def create_encoder_model_mlp(input_shape, size1, final_activ=None):
 
     return model
 
+def make_bert_preprocess_model(sentence_features, tfhub_handle_preprocess, seq_length=128):
+  """Returns Model mapping string features to BERT inputs.
+  """
+
+  input_segments = [
+      tf.keras.layers.Input(shape=(), dtype=tf.string, name=ft)
+      for ft in sentence_features]
+
+  bert_preprocess = hub.load(tfhub_handle_preprocess)
+  tokenizer = hub.KerasLayer(bert_preprocess.tokenize, name='tokenizer')
+  segments = [tokenizer(s) for s in input_segments]
+
+  truncated_segments = segments
+  packer = hub.KerasLayer(bert_preprocess.bert_pack_inputs,
+                          arguments=dict(seq_length=seq_length),
+                          name='packer')
+  model_inputs = packer(truncated_segments)
+  return tf.keras.Model(input_segments, model_inputs)
+
+def process(prompt, lang):
+
+    # Getting prompt user
+    #prompt = input("Audio Search - enter text : ")
+    #print(prompt)
+
+    # prompt embedding
+    bert_model_name = 'small_bert/bert_en_uncased_L-4_H-512_A-8' 
+    tfhub_handle_encoder = 'https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1'
+    tfhub_handle_preprocess = 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3'
+
+    MAX_LENGTH = 130 # MAX de 512 !!! TENSORFLOW !!!
+    TOP = 10
+
+
+    bert_preprocess_model = make_bert_preprocess_model(['my_input'], tfhub_handle_preprocess, seq_length = MAX_LENGTH)
+    bert_model = hub.KerasLayer(tfhub_handle_encoder)
+
+    now = datetime.datetime.now()
+    print()
+    print('*************')
+    print("Current Time: ", str(now))
+    print("Text input : ", prompt)
+    print('*************')
+    print()
+    prompt=[prompt]
+    text_preprocessed = bert_preprocess_model([np.array(prompt)])
+    embed_prompt = bert_model(text_preprocessed)
+    print("   text representation computed.")
+
+    # Embed text
+    #from models import *
+    encoder_text = tf.keras.models.load_model(encoder_text_path)
+    embed_query  = encoder_text.predict(embed_prompt["pooled_output"])
+    faiss.normalize_L2(embed_query)
+    print("   text embed computed.")
+
+    # load embed audio catalog
+    index = faiss.read_index("BMG_221022.index")
+
+    # distance computing
+    D, I = index.search(embed_query, TOP)
+
+    # names index
+    import joblib
+    audio_names = joblib.load(open('BMG_221022_names.index', 'rb'))
+
+    #url
+    url_dict={}
+    with open("bmg_clean.csv") as csv_file:
+        csv_reader = csv.reader(csv_file, delimiter=';')
+        for row in csv_reader:
+          f = row[2].split('/')[-1]
+          url_dict[f.split('/')[-1][:-4]] = row[2]
+
+    # output : top N audio file names
+    print(I)
+    print(D)
+    print("----")
+    for i in range(len(I[0])):
+        print(audio_names[I[0][i]], " with distance ", D[0][i])
+        print("    url : ", url_dict[audio_names[I[0][i]]])
+
+
+    return [url_dict[audio_names[I[0][0]]], url_dict[audio_names[I[0][1]]], url_dict[audio_names[I[0][2]]], url_dict[audio_names[I[0][3]]], url_dict[audio_names[I[0][4]]]]