Update app.py

app.py

@@ -27,7 +27,7 @@ rerank_models = [
 ]
 
 collection_name = "trec_covid"
-qdrant_url = os.getenv("https://5cd56757-1989-4ce6-b7b6-97f6e13f9e89.us-east4-0.gcp.cloud.qdrant.io:6333", "http://localhost:6333")k_values = [1, 3, 5, 10, 20]
+qdrant_url = os.getenv("QDRANT_URL", "http://localhost:6333")k_values = [1, 3, 5, 10, 20]
 
 # =====================
 # LOAD DATA
@@ -163,7 +163,7 @@ def evaluate_metrics(results_data, qrels_dict, k_values):
 # =====================
 
 def encode_and_upload():
-    client = QdrantClient(url=qdrant_url, api_key=os.getenv("eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJhY2Nlc3MiOiJtIn0.4a-XqSJIvuhW6_IO8kKpRir9k6NfWH7yY3NcZciHx-4"))
+    client = QdrantClient(url=qdrant_url, api_key=os.getenv("QDRANT_API_KEY"))
 
     for embedding_model in embedding_models:
         print(f"Encoding corpus with embedding model {embedding_model} ...")
@@ -346,7 +346,7 @@ print(f"Average relevant docs per query: {avg_relevant_docs:.2f}")
 # --------------------
 # CONFIG
 # --------------------
-QDRANT_URL = os.getenv("https://5cd56757-1989-4ce6-b7b6-97f6e13f9e89.us-east4-0.gcp.cloud.qdrant.io:6333", "http://localhost:6333")COLLECTION_NAME = "trec_covid"
+QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6333")COLLECTION_NAME = "trec_covid"
 EMBEDDING_MODEL = "all-MiniLM-L6-v2"
 MAPPING_FILE = "int_to_doc_id.pkl"
 # --------------------