Update app.py

app.py

@@ -8,12 +8,12 @@ import torch
 
 #STEP 2 FROM SEMANTIC SEARCH
 # Open the water_cycle.txt file in read mode with UTF-8 encoding
-with open("water_cycle.txt", "r", encoding="utf-8") as file:
+with open("quentins_knowledge.txt", "r", encoding="utf-8") as file:
   # Read the entire contents of the file and store it in a variable
-  water_cycle_text = file.read()
+  quentins_knowledge = file.read()
 
 # Print the text below
-print(water_cycle_text)
+print(quentins_knowledge)
 
 #STEP 3 FROM SEMANTIC SEARCH
 def preprocess_text(text):
@@ -42,7 +42,7 @@ def preprocess_text(text):
   return cleaned_chunks
 
 # Call the preprocess_text function and store the result in a cleaned_chunks variable
-cleaned_chunks = preprocess_text(water_cycle_text)
+cleaned_chunks = preprocess_text(quentins_knowledge)
 
 #STEP 4 FROM SEMANTIC SEARCH
 # Load the pre-trained embedding model that converts text to vectors