updated to skin cancer harvard txt

app.py

@@ -8,8 +8,8 @@ import torch
 # SEMANTIC SEARCH STEP 2 --> EDIT WITH YOUR OWN KNOWLEDGE BASE WHEN READY
 with open("Skin_cancer_harvard.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()
-print(water_cycle_text)
+  Skin_cancer_harvard_text = file.read()
+print(Skin_cancer_harvard_text)
 
 # SEMANTIC SEARCH STEP 3
 def preprocess_text(text):
@@ -34,7 +34,7 @@ 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) # Complete this line; edit with my knowledgebase when ready
+cleaned_chunks = preprocess_text(Skin_cancer_harvard_text) # Complete this line; edit with my knowledgebase when ready
 
 # SEMANTIC SEARCH STEP 4
 model = SentenceTransformer('all-MiniLM-L6-v2')