update to travel_info.txt

app.py

@@ -6,8 +6,8 @@ from sentence_transformers import SentenceTransformer
 import torch
 
 #STEP 2 FROM SEMANTIC SEARCH
-with open("water_cycle.txt", "r", encoding="utf-8") as file: # Open the water_cycle.txt file in read mode with UTF-8 encoding
-    water_cycle_text = file.read() # Read file and store into variable
+with open("travel_info.txt", "r", encoding="utf-8") as file: # Open the travel_info.txt file in read mode with UTF-8 encoding
+    travel_text = file.read() # Read file and store into variable
 
 #STEP 3 FROM SEMANTIC SEARCH
 def preprocess_text(text):
@@ -25,7 +25,7 @@ def preprocess_text(text):
     
     return cleaned_chunks
 
-cleaned_chunks = preprocess_text(water_cycle_text) # Call preprocess_text and store result in cleaned_chunks
+cleaned_chunks = preprocess_text(travel_text) # Call preprocess_text and store result in cleaned_chunks
 
 #STEP 4 FROM SEMANTIC SEARCH
 model = SentenceTransformer('all-MiniLM-L6-v2') # Load pre-trained embedding model that converts text to vectors
@@ -62,7 +62,7 @@ def get_top_chunks(query, chunk_embeddings, text_chunks): #Finds most relevant t
     return top_chunks
 
 #STEP 6 FROM SEMANTIC SEARCH
-top_results = get_top_chunks("How does the water cycle work?", chunk_embeddings, cleaned_chunks) # Call get_top_chunks with query
+top_results = get_top_chunks("Why is it important to carry copies of your travel documents?", chunk_embeddings, cleaned_chunks) # Call get_top_chunks with query
 print(top_results)
 
 #SAMPLE HUGGING FACE PROJECT