Update app.py

app.py

@@ -13,33 +13,31 @@ os.environ['AWS_ACCESS_KEY_ID']="AKIAZOU6TJIYU64BCGHE"
 os.environ['AWS_SECRET_ACCESS_KEY']="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"
 
 
+import boto3
+from cachetools.func import cached
+from cachetools import TTLCache
+from gpt2_vectors import GPTSimpleVectorIndex
+
 s3 = boto3.resource('s3')
 bucket_name = "notesinendocrinology"
-prefixes = ["comboindex.json", "osteoporosis.json", "nafld.json"]
+combo_index_path = "comboindex.json"
+nafld_path = "nafld.json"
+osteoporosis_path = "osteoporosis.json"
 
-for prefix in prefixes:
+for prefix in [combo_index_path, nafld_path, osteoporosis_path]:
     for obj in s3.Bucket(bucket_name).objects.filter(Prefix=prefix):
         file_path = obj.key
         s3.Bucket(bucket_name).download_file(file_path, file_path.split("/")[-1])
         print(f"Downloaded {file_path}")
 
-
 @cached(cache=TTLCache(maxsize=1, ttl=3600))  # cache for 1 hour
-
-
 def keywords(query2):
-	Bucket.download_file(combo_index_path, "comboindex.json")
-	index1 = GPTSimpleVectorIndex.load_from_disk('comboindex.json')
-	if "NASH" in query2 or "NAFLD" in query2 or "Non-alcoholic fatty live disease" in query2:
-		Bucket.download_file(combo_index_path, "nafld.json")
-		index1 = GPTSimpleVectorIndex.load_from_disk('nafld.json')
-	elif "osteoporosis" in query2 or 'osteopenia' in query2 or 'low bone mass' in query2 or 'DEXA-BMD' in query2 or 'BMD' in query2:
-		Bucket.download_file(combo_index_path, "osteoporosis.json")
-		index1 = GPTSimpleVectorIndex.load_from_disk('osteoporosis.json')
-	else:
-		Bucket.download_file(combo_index_path, "comboindex.json")
-		index1 = GPTSimpleVectorIndex.load_from_disk('comboindex.json')
-	return index1
+    index1 = GPTSimpleVectorIndex.load_from_disk(combo_index_path)
+    if "NASH" in query2 or "NAFLD" in query2 or "Non-alcoholic fatty live disease" in query2:
+        index1 = GPTSimpleVectorIndex.load_from_disk(nafld_path)
+    elif "osteoporosis" in query2 or 'osteopenia' in query2 or 'low bone mass' in query2 or 'DEXA-BMD' in query2 or 'BMD' in query2:
+        index1 = GPTSimpleVectorIndex.load_from_disk(osteoporosis_path)
+    return index1
 
 def send_message(message_log):
 	# Use OpenAI's ChatCompletion API to get the chatbot's response