Update app.py

app.py

@@ -12,6 +12,8 @@ import datetime
 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'],
                             use_auth_token=os.environ['TOKEN'])
 print("DEBUG ", encoder_text_path)                                
@@ -19,7 +21,8 @@ print("DEBUG ", encoder_text_path)
 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
 
-python_path = hf_hub_download(repo_id="PierreHanna/TextRetrieval", repo_type="space", filename="models.py",
+# 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 !
                             use_auth_token=os.environ['TOKEN'])
 print(python_path)
 os.system('ls -la')
@@ -29,11 +32,11 @@ preprocess_model, model = get_models()
 index_path = hf_hub_download(repo_id="PierreHanna/TextRetrieval", 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'],
-                            use_auth_token=os.environ['TOKEN'])
+                            use_auth_token=os.environ['TOKEN']) #########
 catalog_path = hf_hub_download(repo_id="PierreHanna/TextRetrieval", 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'))
+                            use_auth_token=os.environ['TOKEN']) ###############
+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)
 
@@ -51,8 +54,8 @@ def process(prompt, lang):
     print("   text representation computed.")
 
     # Embed text
-    embed_query  = encoder_text.predict(embed_prompt["pooled_output"])
-    faiss.normalize_L2(embed_query)
+    embed_query  = encoder_text.predict(embed_prompt["pooled_output"]) #######
+    faiss.normalize_L2(embed_query) 
     print("   text embed computed.")
 
     # distance computing
@@ -66,7 +69,7 @@ def process(prompt, lang):
         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]]]]
+    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]]]] #######
 
 inputs = [gr.Textbox(label="Input", value="type your description", max_lines=2), 
             gr.Radio(label="Language", choices=["en"], value="en")]
@@ -89,6 +92,8 @@ poc_examples = [#[["I love learning machine learning"],["autre"]]
           ["Big Band","en"],
           ["90 eurodance beat","en"],
 ]
+# cacher ces textes aussi pour pas que le user puisse afficher des choses....
+
 
 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)