Update app.py

app.py

@@ -1,12 +1,9 @@
 import tempfile
 import gradio as gr
 import os
-import tensorflow_hub as hub
 import tensorflow as tf
-import tensorflow_text as text
 import sys
 import numpy as np
-import faiss
 import csv
 import datetime
 import joblib
@@ -25,24 +22,9 @@ print(python_path)
 sys.path.append(os.environ['PRIVATE_DIR'])
 from models import *
 preprocess_model, model = get_models()
-
-#index_path = get_index_path()
-#hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['INDEX'],
-#                            use_auth_token=os.environ['TOKEN'])
-
-#indexnames_path = get_indexnames_path()
-#hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['INDEX_NAMES'],
-#                            use_auth_token=os.environ['TOKEN']) #########
-#catalog_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['CATALOG'],
-#                            use_auth_token=os.environ['TOKEN']) ###############
-#catalog_path = get_catalog()
-
-#url_dict=get_durl(catalog_path) ############
 url_dict = get_durl()
-audio_names = get_audio_names() #joblib.load(open(indexnames_path, 'rb')) ############
-index = get_index() #faiss.read_index(index_path)
-
-#encoder_text = tf.keras.models.load_model(encoder_text_path)
+audio_names = get_audio_names()
+index = get_index()
 encoder_text = get_encoder_text()
 
 def process(prompt, lang):
@@ -58,21 +40,20 @@ def process(prompt, lang):
     embed_query  = get_predict(encoder_text, prompt, preprocess_model, model)
 
     do_normalize(embed_query)
-    #faiss.normalize_L2(embed_query) 
     print("   text embed computed.")
-
-    # distance computing
-    D, I = get_distance(index, embed_query, TOP) #index.search(embed_query, TOP)
-
-    # output : top N audio file names
-    print(I)
-    print(D)
+    D, I = get_distance(index, embed_query, TOP)
+    #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 : ", get_url(I[0][i], audio_names, url_dict))
         
-    return [get_url(I[0][0], audio_names, url_dict), get_url(I[0][1], audio_names, url_dict), get_url(I[0][2], audio_names, url_dict), get_url(I[0][3], audio_names, url_dict), get_url(I[0][4], audio_names, url_dict)] #######
+    return [get_url(I[0][0], audio_names, url_dict), 
+            get_url(I[0][1], audio_names, url_dict), 
+            get_url(I[0][2], audio_names, url_dict), 
+            get_url(I[0][3], audio_names, url_dict), 
+            get_url(I[0][4], audio_names, url_dict)]
 
 inputs = [gr.Textbox(label="Input", value="type your description", max_lines=2), 
             gr.Radio(label="Language", choices=["en"], value="en")]
@@ -96,10 +77,8 @@ poc_examples = [#[["I love learning machine learning"],["autre"]]
           ["90 eurodance beat","en"],
 ]
 
-
 outputs = [gr.Audio(label="Track 1"), gr.Audio(label="Track 2"), gr.Audio(label="Track 3"), gr.Audio(label="Track 4"), gr.Audio(label="Track 5")]
 demo1 = gr.Interface(fn=process, inputs=inputs, outputs=outputs, examples=poc_examples, cache_examples=False)
 
 demo1.launch(debug=True)
 #demo1.launch(debug=True, enable_queue = False, auth=(os.environ['DEMO_LOGIN'], os.environ['DEMO_PWD']),auth_message = "Contact Simbals to get login/pwd")#, share=True)
-