Update app.py

app.py

@@ -6,19 +6,19 @@ from pathlib import Path
 import time
 
 print("load model start")
-print(time.time())
+print(datetime.fromtimestamp(time.time()))
 model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
 print("load model end")
-print(time.time())
+print(datetime.fromtimestamp(time.time()))
 
 quran = pd.read_csv('quran-eng.csv', delimiter=",")
 print("load quran eng")
-print(time.time())
+print(datetime.fromtimestamp(time.time()))
 
 file = open('encoded_quran_text_split_multilingual-e5-large-instructs.sav','rb')
 document_embeddings = pickle.load(file)
 print("load quran embedding")
-print(time.time())
+print(datetime.fromtimestamp(time.time()))
 
 def make_clickable_both(val): 
     name, url = val.split('#')
@@ -28,7 +28,7 @@ def make_clickable_both(val):
 
 def find(query):
     print("start")
-    print(time.time())
+    print(datetime.fromtimestamp(time.time()))
     def get_detailed_instruct(task_description: str, query: str) -> str:
         return f'Instruct: {task_description}\nQuery: {query}'
     
@@ -41,7 +41,7 @@ def find(query):
     #file = open('quran-splitted.sav','rb')
     #quran_splitted = pickle.load(file)
     #print("load quran\n")
-    #print(time.time())
+    #print(datetime.fromtimestamp(time.time()))
     
     #documents = quran_splitted['text'].tolist()
     # document_embeddings = model.encode(documents, convert_to_tensor=True, normalize_embeddings=True)
@@ -50,20 +50,20 @@ def find(query):
     
     query_embeddings = model.encode(queries, convert_to_tensor=True, normalize_embeddings=True)
     print("embed query")
-    print(time.time())
+    print(datetime.fromtimestamp(time.time()))
     scores = (query_embeddings @ document_embeddings.T) * 100
     print("count similarities")
-    print(time.time())
+    print(datetime.fromtimestamp(time.time()))
 
     # insert the similarity value to dataframe & sort it
     file = open('quran-splitted.sav','rb')
     quran_splitted = pickle.load(file)
     print("load quran")
-    print(time.time())
+    print(datetime.fromtimestamp(time.time()))
     quran_splitted['similarity'] = scores.tolist()[0]
     sorted_quran = quran_splitted.sort_values(by='similarity', ascending=False)
     print("sort by similarity")
-    print(time.time())
+    print(datetime.fromtimestamp(time.time()))
     
     #results = ""
     results = pd.DataFrame()
@@ -75,7 +75,7 @@ def find(query):
         #results = results + result_quran['text'].item()+" (Q.S "+str(result['sura']).rstrip('.0')+":"+str(result['aya']).rstrip('.0')+")\n"
         i=i+1
     print("collect results")
-    print(time.time())
+    print(datetime.fromtimestamp(time.time()))
     
     url = 'https://quran.com/'+results['sura'].astype(str)+':'+results['aya'].astype(str)+'/tafsirs/en-tafisr-ibn-kathir'
     results['text'] = '<a href="'+url+'">'+results['text']+ '</a>' + ' (QS. ' + results['sura'].astype(str) + ':' + results['aya'].astype(str) + ')'