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
@@ -149,13 +149,7 @@ chatbot = gr.ChatInterface(respond, type="messages")
 
 with gr.Blocks() as chatbot:
     with gr.Row():
-        cool_button = gr.Button("Cool Mom")
-        tutor_button = gr.Button("Tutor Mom")
-        strict_button = gr.Button("Strict Mom")
-
-    cool_button.click(fn=[], inputs=[], outputs=respond(message, history, "Cool Mom"))
-    tutor_button.click(fn=[], inputs=[], outputs=respond(message, history, "Tutor Mom"))
-    strict_button.click(fn=[], inputs=[], outputs=respond(message, history, "Strict Mom"))
+        mom_type = gr.CheckboxGroup(["Cool Mom", "Tutor Mom", "Strict Mom"],label = "Choose Your Mom")
     
     gr.ChatInterface(
         #fn=respond,