Update app.py

app.py

@@ -11,7 +11,7 @@ with open("cool_mom_phrases.txt", "r", encoding="utf-8") as file:
   # Read the entire contents of the file and store it in a variable
   cool_mom_text = file.read()
 
-'''with open("tutor_mom_phrases.txt", "r", encoding="utf-8") as file:
+with open("tutor_mom_phrases.txt", "r", encoding="utf-8") as file:
   # Read the entire contents of the file and store it in a variable
   tutor_mom_text = file.read()
 
@@ -22,7 +22,7 @@ with open("strict_mom_phrases.txt", "r", encoding="utf-8") as file:
 with open("study_techniques.txt", "r", encoding="utf-8") as file:
   # Read the entire contents of the file and store it in a variable
   study_techniques_text = file.read()
-'''
+
 
 # STEP 3 FROM SEMANTIC SEARCH
 def preprocess_text(text):
@@ -46,8 +46,8 @@ def preprocess_text(text):
 
 # Call the preprocess_text function and store the result in a cleaned_chunks variable
 cleaned_cool_chunks = preprocess_text(cool_mom_text) # Complete this line
-'''cleaned_tutor_chunks = preprocess_text(tutor_mom_text)
-cleaned_strict_chunks = preprocess_text(strict_mom_text)'''
+cleaned_tutor_chunks = preprocess_text(tutor_mom_text)
+cleaned_strict_chunks = preprocess_text(strict_mom_text)
 
 #STEP 4 FROM SEMANTIC SEARCH
 # Load the pre-trained embedding model that converts text to vectors
@@ -62,8 +62,8 @@ def create_embeddings(text_chunks):
 
 # Call the create_embeddings function and store the result in a new chunk_embeddings variable
 cool_chunk_embeddings = create_embeddings(cleaned_cool_chunks) # Complete this line
-'''tutor_chunk_embeddings = create_embeddings(cleaned_tutor_chunks)
-strict_chunk_embeddings = create_embeddings(cleaned_strict_chunks)'''
+tutor_chunk_embeddings = create_embeddings(cleaned_tutor_chunks)
+strict_chunk_embeddings = create_embeddings(cleaned_strict_chunks)
 
 #STEP 5 FROM SEMANTIC SEARCH
 # Define a function to find the most relevant text chunks for a given query, chunk_embeddings, and text_chunks
@@ -116,7 +116,13 @@ def respond(messages, history, mom_type) :
         top_results = get_top_chunks(message, tutor_chunk_embeddings, cleaned_tutor_chunks)
     elif mom_type == "Strict Mom":
         top_results = get_top_chunks(message, strict_chunk_embeddings, cleaned_strict_chunks)
-        
+
+    messages = [{"role": "system", "content": f"You are chatbot that plays the role of the user's extremely studious, tutor-like mom. Respond in full sentences, don't cut yourself off. Base your response on the provided context: {mom_type}"}, 
+               {"role": "user", 
+               "content": (
+                   f"Context:\n{top_tutor_results}\n\n"
+                   f"Question{message}"
+               )}]
     if history:
         messages.extend(history)