Update app.py

app.py

@@ -21,7 +21,6 @@ for chunk in chunks:
   if stripped_chunk:
     #if the chunk is not empty then it is being appended to the cleaned chunk list.
     cleaned_chunks.append(stripped_chunk)
-print(cleaned_chunks)
 model = SentenceTransformer('all-MiniLM-L6-v2')
 chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
 # encode the model, pass through my clean chunks and convert to vector embeddings (not arrays)
@@ -38,11 +37,9 @@ def get_top_chunks(query): # store a function that gets the most relevant_info a
   # normalizing chunks for comparison of meaning
 
   similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
-  print(similarities)
 # using my matmul(matrix multiplication method to compare query to chunks)
 
   top_indices = torch.topk(similarities, k=3).indices
-  print(top_indices)
   # get the indices of the chunks that are most similar to my query
 
   top_chunks = []