Update app.py

app.py

@@ -39,11 +39,11 @@ theme = gr.themes.Monochrome(
     button_primary_background_fill_hover='*neutral_100'
 )
 
-#STEP 1 FROM SEMATIC SEARCH
+#STEP 1 FROM SEMANTIC SEARCH
 from sentence_transformers import SentenceTransformer
 import torch
 
-#STEP 2 FROM SEMATIC SEARCH
+#STEP 2 FROM SEMANTIC SEARCH
 # Open the weather.txt file in read mode with UTF-8 encoding
 with open("weather.txt", "r", encoding="utf-8") as file:
   # Read the entire contents of the file and store it in a variable
@@ -58,7 +58,7 @@ with open("food.txt", "r", encoding="utf-8") as file:
   # Read the entire contents of the file and store it in a variable
   food_text = file.read()
 
-#STEP 3 FROM SEMATIC SEARCH
+#STEP 3 FROM SEMANTIC SEARCH
 def preprocess_text(text):
   # Strip extra whitespace from the beginning and the end of the text
   cleaned_text = text.strip()
@@ -79,7 +79,7 @@ cleaned_chunks_luggage = preprocess_text(luggage_text)
 cleaned_chunks_attraction = preprocess_text(attraction_text)
 cleaned_chunks_food = preprocess_text(food_text)
 
-#STEP 4 FROM SEMATIC SEARCH
+#STEP 4 FROM SEMANTIC SEARCH
 # Load the pre-trained embedding model that converts text to vectors
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
@@ -94,7 +94,7 @@ chunk_embeddings_luggage = create_embeddings(cleaned_chunks_luggage)
 chunk_embeddings_attraction = create_embeddings(cleaned_chunks_attraction)
 chunk_embeddings_food = create_embeddings(cleaned_chunks_food)
 
-#STEP 5 FROM SEMATIC SEARCH
+#STEP 5 FROM SEMANTIC SEARCH
 # Define a function to find the most relevant text chunks for a given query, chunk_embeddings, and text_chunks
 def get_top_chunks(query, chunk_embeddings, text_chunks):
   # Convert the query text into a vector embedding