Ajouter le script Gradio et les dépendances

app.py

@@ -1,23 +1,29 @@
 import os
-
 import gradio as gr
 from qdrant_client import QdrantClient
 from transformers import ClapModel, ClapProcessor
 
-# Loading the Qdrant DB in local ###################################################################
-client = QdrantClient("https://ebe79742-e3ac-4d09-a2c6-63946024cc7a.us-east4-0.gcp.cloud.qdrant.io", api_key="_NnGLuSMH4Qwv-ancoFh88YvzuR7WbyidAorVOVQ_eMCbPhxTb2TSw")
+# Charger les variables d'environnement
+QDRANT_URL = os.getenv("QDRANT_URL")
+QDRANT_API_KEY = os.getenv("QDRANT_API_KEY")
+
+# Vérifiez si les variables d'environnement sont définies
+if not QDRANT_URL ou not QDRANT_API_KEY:
+    raise ValueError("Veuillez définir les variables d'environnement QDRANT_URL et QDRANT_API_KEY")
+
+# Chargement de la base de données Qdrant en local
+client = QdrantClient(QDRANT_URL, api_key=QDRANT_API_KEY)
 print("[INFO] Client created...")
 
-# loading the model
+# 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)
 
-# Gradio Interface #################################################################################
+# 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]
@@ -34,13 +40,12 @@ def sound_search(query):
         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
+    out = [gr.Audio(label=f"{x}") for x in range(max_results)]  # Nécessaire d'avoir des objets différents
     inp.change(sound_search, inp, out)
 
 demo.launch()