Update app.py

app.py

@@ -13,7 +13,7 @@ model = AutoModel.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-a
 # Step 1: Read CSV and extract relevant rows for the first ten data points
 csv_filename = "new.csv"
 grants_data = []
-
+i =0 
 with open(csv_filename, "r", encoding="utf-8") as csv_file:
     csv_reader = csv.DictReader(csv_file)
     for i, row in enumerate(csv_reader):
@@ -33,6 +33,9 @@ if st.button("Calculate Similarity"):
     similarity_scores = []
 
     for grant_data in grants_data:
+        i =i+1
+        print(i)
+        
         grant_description = grant_data["opportunitytitle"][:3000]  # Truncate the description to reduce tokens
 
         # Tokenize the reference text and grant description