Update app.py

app.py

@@ -6,12 +6,13 @@ import os
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 # Initialize paths and model identifiers for easy configuration and maintenance
-filename = "output_topic_details.txt"  # Path to the file storing chess-specific details
-retrieval_model_name = 'output/sentence-transformer-finetuned/'
+filename = "output_topic_details.txt"  # Path to the file storing to-do list examples
+retrieval_model_name = 'all-MiniLM-L6-v2'  # Using a pre-trained model from Hugging Face
 
 openai.api_key = os.environ["OPENAI_API_KEY"]
 
-system_message = "You are an assistant specialized in distilling the user input into to do list of specific items. You will then output those items in a numbered list."
+system_message = "You are an assistant specialized in distilling the user input into a to-do list of specific items. You will then output those items in a numbered list."
+
 # Initial system message to set the behavior of the assistant
 messages = [{"role": "system", "content": system_message}]
 
@@ -67,13 +68,14 @@ def generate_response(user_query, relevant_segment):
     Generate a response emphasizing the bot's capability in providing scheduling information.
     """
     try:
+        # Use relevant segment in the message to the model
         user_message = f"Here's a to do list based on what you said: {relevant_segment}"
 
         # Append user's message to messages list
         messages.append({"role": "user", "content": user_message})
         
         response = openai.ChatCompletion.create(
-            model="gpt-4o",
+            model="gpt-4",
             messages=messages,
             max_tokens=200,
             temperature=0.2,
@@ -100,21 +102,24 @@ def query_model(question):
     """
     if question == "":
         return "Hello! I am your time manager Timify! Please enter what you need to do today."
+    
+    # Find the most relevant example segment
     relevant_segment = find_relevant_segment(question, segments)
     if not relevant_segment:
         return "Could not find specific information. Please refine your requirements."
+    
+    # Generate a response using the relevant example
     response = generate_response(question, relevant_segment)
     return response
 
 # Define the welcome message and specific topics the chatbot can provide information about
 welcome_message = """
  ★Welcome to Timify!★
-
 ## I am your AI chatbot driven to help you with all your scheduling needs!
 """
 
 topics = """
-### Feel free to ask about of the questions below:
+### Feel free to ask about the questions below:
 - How does Timify work?
 - Create me a to-do list
 - Ask me to create a daily schedule
@@ -123,7 +128,7 @@ topics = """
 
 # Setup the Gradio Blocks interface with custom layout components
 with gr.Blocks(theme='freddyaboulton/test-blue') as demo:
-    gr.Image("Timify background.png", show_label = False, show_share_button = False, show_download_button = False) 
+    gr.Image("Timify background.png", show_label=False, show_share_button=False, show_download_button=False) 
     gr.Markdown(welcome_message)  # Display the formatted welcome message
     with gr.Row():
         with gr.Column():