fix indentation

app.py

@@ -19,9 +19,9 @@ cleaned_chunks = []
 
 #putting text in chunks
 for chunk in chunks:
-  stripped_chunk = chunk.strip()
-  if stripped_chunk:
-    cleaned_chunks.append(stripped_chunk)
+    stripped_chunk = chunk.strip()
+    if stripped_chunk:
+        cleaned_chunks.append(stripped_chunk)
 
 #import model for embeddings
 model = SentenceTransformer('all-MiniLM-L6-v2')
@@ -30,38 +30,40 @@ chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
 
 def get_top_chunks(query):
   # creating a function taking query as my parameter
-  query_embedding = model.encode(query, convert_to_tensor=True)
+    query_embedding = model.encode(query, convert_to_tensor=True)
   # encode query to vector embedding for comparison
-  query_embedding_normalized = query_embedding / query_embedding.norm()
+    query_embedding_normalized = query_embedding / query_embedding.norm()
   # normalize query to 1: allows for comparison of meaning
-  chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
+    chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
   # normalizing chunks for comparison of meaning
- similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
-  print(similarities)
+    similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
   # using matmul (matrix multiplication) method to compare query to chunks
-
-  top_indices = torch.topk(similarities, k=3).indices
-  print(top_indices)
+    top_indices = torch.topk(similarities, k=3).indices
   # get the indices of the chunks thart are most similar to my query
 
-  top_chunks = []
+    top_chunks = []
 
-  for i in top_indices:
-    chunk = chunks[i]
+    for i in top_indices:
+        chunk = chunks[i]
     # for each index number in top_indices, get back the text
-    top_chunks.append(chunk)
+        top_chunks.append(chunk)
+
+    return top_chunks
 
-  return top_chunks
 
 def respond(message, history):
     messages = [{"role": "system", "content": "You are a big sister chatbot named, Nessie. You help people feel better about their bodies and self-image."}] 
     # change the personality
     context = get_top_chunks(message)
+    
    if history:
         messages.extend(history)
     messages.append({"role": "user", "content": message})
 
-response = ""
+    user_context = f"{message}\nInformation: {context}"
+    messages.append({"role": "user", "content": user_context})
+
+    response = ""
     for messages in client.chat_completion(
         messages,
         max_tokens = 500,