Update app.py

app.py

@@ -13,8 +13,7 @@ import joblib
 
 from huggingface_hub import hf_hub_download
 
-# Cacher le nom du repo
-encoder_text_path = hf_hub_download(repo_id="PierreHanna/TextRetrieval", repo_type="space", filename=os.environ['ENCODER_TEXT'],
+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
@@ -22,18 +21,18 @@ 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="PierreHanna/TextRetrieval", repo_type="space", filename="models.py", # cacher le nom du fichier !
+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="PierreHanna/TextRetrieval", repo_type="space", filename=os.environ['INDEX'],
+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="PierreHanna/TextRetrieval", repo_type="space", filename=os.environ['INDEX_NAMES'],
+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="PierreHanna/TextRetrieval", repo_type="space", filename=os.environ['CATALOG'],
+catalog_path = hf_hub_download(repo_id=os.environ['REPO_ID'], repo_type="space", filename=os.environ['CATALOG'],
                             use_auth_token=os.environ['TOKEN']) ###############
 url_dict=get_durl(catalog_path) ############
 audio_names = joblib.load(open(indexnames_path, 'rb')) ############
@@ -48,13 +47,11 @@ def process(prompt, lang):
     print("Text input : ", prompt)
     print('*************')
     print()
-    prompt=[prompt]
-    text_preprocessed = preprocess_model([np.array(prompt)])
-    embed_prompt = model(text_preprocessed)
-    print("   text representation computed.")
 
     # Embed text
-    embed_query  = encoder_text.predict(embed_prompt["pooled_output"]) #######
+    #embed_query  = encoder_text.predict(embed_prompt["pooled_output"]) #######
+    embed_query  = get_predict(encoder_text, prompt, preprocess_model, model)
+        
     faiss.normalize_L2(embed_query) 
     print("   text embed computed.")