getting rid of sem search

app.py

@@ -1,121 +1,6 @@
 import gradio as gr
 import requests
 
-
-#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-# Step 1 - Semantic Search
-""from sentence_transformers import SentenceTransformer
-import torch
-
-
-
-# Step 2 - 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:
-      # Read the entire contents of the file and store it in a variable
-  water_cycle_text = file.read()
-    # Print the text below
-print(water_cycle_text)
-
-
-
-# Step 3 - Semantic Search
-def preprocess_text(text):
-  # Strip extra whitespace from the beginning and the end of the text
-  cleaned_text = text.strip()
-
-  # Split the cleaned_text by every newline character (\n)
-  chunks = cleaned_text.split("\n")
-
-  # Create an empty list to store cleaned chunks
-  cleaned_chunks = []
-
-  # Write your for-in loop below to clean each chunk and add it to the cleaned_chunks list
-  for chunk in chunks:
-    stripped_chunk = chunk.strip()
-    if len(stripped_chunk) > 0:
-      cleaned_chunks.append(stripped_chunk)
-
-  # Print cleaned_chunks
-  print(cleaned_chunks)
-
-  # Print the length of cleaned_chunks
-  print(len(cleaned_chunks))
-
-  # Return the cleaned_chunks
-  return cleaned_chunks
-
-
-
-# Step 4 - Semantic Search
-    # Load the pre-trained embedding model that converts text to vectors
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-def create_embeddings(text_chunks):
-      # Convert each text chunk into a vector embedding and store as a tensor
-  chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True) # Replace ... with the text_chunks list
-
-      # Print the chunk embeddings
-  print(chunk_embeddings)
-
-      # Print the shape of chunk_embeddings
-  print(chunk_embeddings.shape)
-
-      # Return the chunk_embeddings
-  return chunk_embeddings
-
-    # Call the create_embeddings function and store the result in a new chunk_embeddings variable
-chunk_embeddings = create_embeddings(cleaned_chunks) # Complete this line
-
-    # Call the preprocess_text function and store the result in a cleaned_chunks variable
-#cleaned_chunks = preprocess_text(water_cycle_text) # Complete this line
-
-
-
-# Step 5 - Semantic Search
-def get_top_chunks(query, chunk_embeddings, text_chunks):
-  # Convert the query text into a vector embedding
-  query_embedding = model.encode(query, convert_to_tensor = True) # Complete this line
-
-  # Normalize the query embedding to unit length for accurate similarity comparison
-  query_embedding_normalized = query_embedding / query_embedding.norm()
-
-  # Normalize all chunk embeddings to unit length for consistent comparison
-  chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
-
-  # Calculate cosine similarity between query and all chunks using matrix multiplication
-  similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized) # Complete this line
-
-  # Print the similarities
-  print(similarities)
-
-  # Find the indices of the 3 chunks with highest similarity scores
-  top_indices = torch.topk(similarities, k=3).indices
-
-  # Print the top indices
-  print(top_indices)
-
-  # Create an empty list to store the most relevant chunks
-  top_chunks = []
-
-  # Loop through the top indices and retrieve the corresponding text chunks
-  for i in top_indices:
-    chunk = text_chunks[i]
-    top_chunks.append(chunk)
-
-  # Return the list of most relevant chunks
-  return top_chunks
-
-
-
-# Step 6 - Semantic Search
-    # Call the get_top_chunks function with the original query
-top_results = get_top_chunks("How does water get into the sky", chunk_embeddings, cleaned_chunks) # Complete this line
-    # Print the top results
-print(top_results)""
-#------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-
-
 def response(message, history):
     bitebot_chunks = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
     print(bitebot)