Update app.py

app.py

@@ -6,8 +6,8 @@ from sentence_transformers.util import cos_sim
 from sentence_transformers import SentenceTransformer
 import gradio as gr
 #%%
-etalon = pd.read_csv("data/symptomps_specialist_mkb_issledovania/ranker/raw_data/etalon_prod.csv")
-df = pd.read_csv("data/symptomps_specialist_mkb_issledovania/ranker/raw_data/preprocessed_train_classify_rec_spec_filtered_by_etalon.csv")
+etalon = pd.read_csv("etalon_prod.csv")
+df = pd.read_csv("preprocessed_train_classify_rec_spec_filtered_by_etalon.csv")
 df = df[df['is_match'] == 1]
 
 model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2').to("cuda")
@@ -16,7 +16,7 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 unique_complaints = df['Жалобы'].values.tolist()
 
-with open("experiments/symptom_recommender/embeddings_scripts/app/embeddings.npy", 'rb') as f:
+with open("embeddings.npy", 'rb') as f:
     unique_complaints_embeddings_st = np.load(f)
 
 def get_recommend(user_input,