Update app.py

app.py

@@ -13,33 +13,42 @@ import joblib
 
 from huggingface_hub import hf_hub_download
 
-encoder_text_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['ENCODER_TEXT'],
-                            use_auth_token=os.environ['TOKEN'])
-print("DEBUG ", encoder_text_path)                                
+#encoder_text_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['ENCODER_TEXT'],
+#                            use_auth_token=os.environ['TOKEN'])
+#print("DEBUG ", encoder_text_path)                                
 # NO GPU
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
 # Cacher le nom du repo
-python_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['MODEL_FILE'],
-                            use_auth_token=os.environ['TOKEN'])
-print(python_path)
-os.system('ls -la')
+#python_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['MODEL_FILE'],
+#                            use_auth_token=os.environ['TOKEN'])
+#print(python_path)
+
+#os.system('ls -la')
+
 sys.path.append(os.environ['PRIVATE_DIR'])
 from models import *
 preprocess_model, model = get_models()
-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 = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['INDEX_NAMES'],
-                            use_auth_token=os.environ['TOKEN']) #########
+
+#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()
+#catalog_path = get_catalog()
 
-url_dict=get_durl(catalog_path) ############
-audio_names = joblib.load(open(indexnames_path, 'rb')) ############
-index = faiss.read_index(index_path)
-encoder_text = tf.keras.models.load_model(encoder_text_path)
+#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)
+encoder_text = get_encoder_text()
 
 def process(prompt, lang):
     now = datetime.datetime.now()
@@ -52,12 +61,13 @@ def process(prompt, lang):
 
     # Embed text
     embed_query  = get_predict(encoder_text, prompt, preprocess_model, model)
-        
-    faiss.normalize_L2(embed_query) 
+
+    do_normalize(embed_query)
+    #faiss.normalize_L2(embed_query) 
     print("   text embed computed.")
 
     # distance computing
-    D, I = index.search(embed_query, TOP)
+    D, I = get_distance(index, embed_query, TOP) #index.search(embed_query, TOP)
 
     # output : top N audio file names
     print(I)