divide prompt

app.py

@@ -24,15 +24,21 @@ embeddings = np.load(embeddings_path)
 # Load the SentenceTransformer model
 model = SentenceTransformer("all-MiniLM-L6-v2").to(device)
 
-# Load the system prompt from the file
-with open("prompt.txt", "r") as file:
-    system_prompt = file.read()
+# Load prompts from files
+with open("question_generation_prompt.txt", "r") as file:
+    question_generation_prompt = file.read()
+
+with open("technical_interviewer_prompt.txt", "r") as file:
+    technical_interviewer_prompt = file.read()
 
 st.title("Real-World Programming Question Mock Interview")
 
 # Initialize chat history in session state
 if "messages" not in st.session_state:
-    st.session_state.messages = [{"role": "assistant", "content": system_prompt}]
+    st.session_state.messages = []
+
+if "follow_up_mode" not in st.session_state:
+    st.session_state.follow_up_mode = False  # Tracks whether we're in follow-up mode
 
 # Function to find the top 1 most similar question based on user input
 def find_top_question(query):
@@ -55,13 +61,13 @@ def find_top_question(query):
     return top_result
 
 # Function to generate response using OpenAI API with debugging logs
-def generate_response(prompt):
+def generate_response(messages):
     st.write("### Debugging Log: Data Sent to GPT")
-    st.write(prompt)  # Log the prompt being sent to GPT for debugging
+    st.write(messages)  # Log the messages being sent to GPT for debugging
     
     response = client.chat.completions.create(
         model="o1-mini",
-        messages=st.session_state.messages + [{"role": "assistant", "content": prompt}]
+        messages=messages,
     )
     return response.choices[0].message.content
 
@@ -74,8 +80,9 @@ with st.form(key="input_form"):
     generate_button = st.form_submit_button(label="Generate")
 
 if generate_button:
-    # Clear session state and start a new conversation history with system prompt
-    st.session_state.messages = [{"role": "assistant", "content": system_prompt}]
+    # Clear session state and start fresh with follow-up mode disabled
+    st.session_state.messages = []
+    st.session_state.follow_up_mode = False
     
     # Create a query from user inputs and find the most relevant question
     query = f"{company} {difficulty} {topic}"
@@ -93,34 +100,40 @@ if generate_button:
     )
     
     # Generate response using GPT-4 with detailed prompt and debugging logs
-    response = generate_response(detailed_prompt)
+    response = generate_response([{"role": "system", "content": question_generation_prompt}, {"role": "user", "content": detailed_prompt}])
 
     # Display assistant response in chat message container and add to session history
     with st.chat_message("assistant"):
         st.markdown(response)
     
     st.session_state.messages.append({"role": "assistant", "content": response})
+    
+    # Enable follow-up mode after generating the initial question
+    st.session_state.follow_up_mode = True
 
 # Display chat messages from history on app rerun (for subsequent conversation)
-for message in st.session_state.messages[1:]:  # Skip the system message
+for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
-# Chatbox for subsequent conversations with assistant
-if user_input := st.chat_input("Continue your conversation or ask follow-up questions here:"):
-    # Display user message in chat message container and add to session history
-    with st.chat_message("user"):
-        st.markdown(user_input)
-    
-    st.session_state.messages.append({"role": "user", "content": user_input})
-
-    # Generate assistant's response based on follow-up input
-    assistant_response = generate_response(user_input)
-
-    with st.chat_message("assistant"):
-        st.markdown(assistant_response)
-    
-    st.session_state.messages.append({"role": "assistant", "content": assistant_response})
+# Chatbox for subsequent conversations with assistant (follow-up mode)
+if st.session_state.follow_up_mode:
+    if user_input := st.chat_input("Continue your conversation or ask follow-up questions here:"):
+        # Display user message in chat message container and add to session history
+        with st.chat_message("user"):
+            st.markdown(user_input)
+        
+        st.session_state.messages.append({"role": "user", "content": user_input})
+
+        # Generate assistant's response based on follow-up input using technical_interviewer_prompt as system prompt
+        assistant_response = generate_response(
+            [{"role": "system", "content": technical_interviewer_prompt}] + st.session_state.messages
+        )
+
+        with st.chat_message("assistant"):
+            st.markdown(assistant_response)
+        
+        st.session_state.messages.append({"role": "assistant", "content": assistant_response})
 
 st.sidebar.markdown("""
 ## About