Ajouter le script Gradio et les dépendances*

.env

@@ -1,2 +1,4 @@
 QDRANT_URL=https://ebe79742-e3ac-4d09-a2c6-63946024cc7a.us-east4-0.gcp.cloud.qdrant.io
-QDRANT_KEY=_NnGLuSMH4Qwv-ancoFh88YvzuR7WbyidAorVOVQ_eMCbPhxTb2TSw
+QDRANT_KEY=_NnGLuSMH4Qwv-ancoFh88YvzuR7WbyidAorVOVQ_eMCbPhxTb2TSw
+AWS_ACCESS_KEY_ID=AKIAWOUASMWP5DM6RZG2
+AWS_SECRET_ACCESS_KEY=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

app.py

@@ -1,58 +1,66 @@
 import os
-
 import gradio as gr
 from qdrant_client import QdrantClient
 from transformers import ClapModel, ClapProcessor
 from dotenv import load_dotenv
-import os
+import requests
 
 # Charger les variables d'environnement à partir du fichier .env
 load_dotenv()
 
-# Récupérer le mot de passe depuis les variables d'environnement
+# Récupérer les variables d'environnement
 QDRANT_URL = os.getenv('QDRANT_URL')
 QDRANT_KEY = os.getenv('QDRANT_KEY')
 
 # Vérifier les valeurs récupérées
 print(f"QDRANT_URL: {QDRANT_URL}")
 print(f"QDRANT_KEY: {QDRANT_KEY}")
-# Loading the Qdrant DB in local ###################################################################
-client = QdrantClient(QDRANT_URL, api_key=QDRANT_KEY)
-print("[INFO] Client created...")
-
-# loading the model
-print("[INFO] Loading the model...")
-model_name = "laion/larger_clap_general"
-model = ClapModel.from_pretrained(model_name)
-processor = ClapProcessor.from_pretrained(model_name)
-
-# Gradio Interface #################################################################################
-max_results = 10
-
-
-def sound_search(query):
-    text_inputs = processor(text=query, return_tensors="pt")
-    text_embed = model.get_text_features(**text_inputs)[0]
-
-    hits = client.search(
-        collection_name="demo_spaces_db",
-        query_vector=text_embed,
-        limit=max_results,
-    )
-    return [
-        gr.Audio(
-            hit.payload['audio_path'],
-            label=f"style: {hit.payload['style']} -- score: {hit.score}")
-        for hit in hits
-    ]
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(
-        """# Sound search database """
-    )
-    inp = gr.Textbox(placeholder="What sound are you looking for ?")
-    out = [gr.Audio(label=f"{x}") for x in range(max_results)]  # Necessary to have different objs
-    inp.change(sound_search, inp, out)
-
-demo.launch()
+
+try:
+    # Tester la connexion à l'URL de Qdrant
+    response = requests.get(QDRANT_URL)
+    print(f"Test de la connexion à Qdrant: {response.status_code}")
+
+    # Vérifier que les variables sont correctement récupérées
+    if not QDRANT_URL or not QDRANT_KEY:
+        raise ValueError("Les variables d'environnement QDRANT_URL ou QDRANT_KEY ne sont pas définies")
+
+    # Connexion au client Qdrant
+    client = QdrantClient(QDRANT_URL, api_key=QDRANT_KEY)
+    print("[INFO] Client created...")
+
+    # Chargement du modèle
+    print("[INFO] Loading the model...")
+    model_name = "laion/larger_clap_general"
+    model = ClapModel.from_pretrained(model_name)
+    processor = ClapProcessor.from_pretrained(model_name)
+
+    # Interface Gradio
+    max_results = 10
+
+    def sound_search(query):
+        text_inputs = processor(text=query, return_tensors="pt")
+        text_embed = model.get_text_features(**text_inputs)[0]
+
+        hits = client.search(
+            collection_name="demo_spaces_db",
+            query_vector=text_embed,
+            limit=max_results,
+        )
+        return [
+            gr.Audio(
+                hit.payload['audio_path'],
+                label=f"style: {hit.payload['style']} -- score: {hit.score}")
+            for hit in hits
+        ]
+
+    with gr.Blocks() as demo:
+        gr.Markdown("# Sound search database")
+        inp = gr.Textbox(placeholder="What sound are you looking for ?")
+        out = [gr.Audio(label=f"{x}") for x in range(max_results)]
+        inp.change(sound_search, inp, out)
+
+    demo.launch()
+
+except Exception as e:
+    print(f"[ERROR] Failed to create Qdrant client: {e}")

database.py

@@ -17,8 +17,8 @@ from dotenv import load_dotenv
 load_dotenv()
 
 # PARAMETERS #######################################################################################
-CACHE_FOLDER = '/home/arthur/data/music/demo_audio_search/audio_embeddings_cache_individual/'
-KAGGLE_DB_PATH = '/home/arthur/data/kaggle/park-spring-2023-music-genre-recognition/train/train'
+CACHE_FOLDER = '/home/nahia/audio'
+KAGGLE_DB_PATH = '/home/nahia/Documents/audio/actor/Actor_01'
 AWS_ACCESS_KEY_ID = os.environ['AWS_ACCESS_KEY_ID']
 AWS_SECRET_ACCESS_KEY = os.environ['AWS_SECRET_ACCESS_KEY']
 S3_BUCKET = "synthia-research"
@@ -77,7 +77,7 @@ os.makedirs(CACHE_FOLDER, exist_ok=True)
 cache = Cache(CACHE_FOLDER)
 
 # Creating a qdrant collection #####################################################################
-client = QdrantClient(QDRANT_URL,QDRANT_KEY)
+client = QdrantClient(QDRANT_URL,api_key=QDRANT_KEY)
 print("[INFO] Client created...")
 
 print("[INFO] Creating qdrant data collection...")