Update app.py

app.py

@@ -6,9 +6,12 @@ import re
 from rank_bm25 import BM25Okapi
 import numpy as np
 # Load models
-model = SentenceTransformer("distilbert-base-multilingual-cased")
-modela = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
-modelb = SentenceTransformer("Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka")
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = SentenceTransformer("distilbert-base-multilingual-cased", device=device)
+modela = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2", device=device)
+modelb = SentenceTransformer("Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka", device=device)
 
 # Load data
 df = pd.read_csv("cleaned1.csv")
@@ -16,19 +19,19 @@ df2 = pd.read_csv("cleaned2.csv")
 df3 = pd.read_csv("cleaned3.csv")
 
 # Load pre-computed embeddings
-embeddings = torch.load("embeddings1_1.pt")
-embeddings2 = torch.load("embeddings2_1.pt")
-embeddings3 = torch.load("embeddings3_1.pt")
+embeddings = torch.load("embeddings1_1.pt", map_location=device)
+embeddings2 = torch.load("embeddings2_1.pt", map_location=device)
+embeddings3 = torch.load("embeddings3_1.pt", map_location=device)
 
 
-embeddingsa = torch.load("embeddings1.pt")
-embeddingsa2 = torch.load("embeddings2.pt")
-embeddingsa3 = torch.load("embeddings3.pt")
+embeddingsa = torch.load("embeddings1.pt", map_location=device)
+embeddingsa2 = torch.load("embeddings2.pt", map_location=device)
+embeddingsa3 = torch.load("embeddings3.pt", map_location=device)
 
 
-embeddingsb = torch.load("embeddingso1_3.pt")
-embeddingsb2 = torch.load("embeddingso2_3.pt")
-embeddingsb3 = torch.load("embeddingso3_3.pt")
+embeddingsb = torch.load("embeddingso1_3.pt", map_location=device)
+embeddingsb2 = torch.load("embeddingso2_3.pt", map_location=device)
+embeddingsb3 = torch.load("embeddingso3_3.pt", map_location=device)
 
 # Extract questions and links
 df_questions = df["question"].values