Update app.py

app.py

@@ -28,6 +28,7 @@ model = SentenceTransformer("all-MiniLM-L6-v2").to(device)
 with open("technical_interviewer_prompt.txt", "r") as file:
     technical_interviewer_prompt = file.read()
 
+# Load prompts from files
 with open("question_generation_prompt.txt", "r") as file:
     question_generation_prompt = file.read()
     
@@ -38,41 +39,56 @@ if "messages" not in st.session_state:
     st.session_state.messages = []
 
 if "follow_up_mode" not in st.session_state:
-    st.session_state.follow_up_mode = False
+    st.session_state.follow_up_mode = False  # Tracks whether we're in follow-up mode
 
 if "generated_question" not in st.session_state:
-    st.session_state.generated_question = None
+    st.session_state.generated_question = None  # Stores the generated question for persistence
 
 if "debug_logs" not in st.session_state:
-    st.session_state.debug_logs = []
+    st.session_state.debug_logs = []  # Stores debug logs for toggling
 
 # Function to find the top 1 most similar question based on user input
 def find_top_question(query):
+    # Generate embedding for the query
     query_embedding = model.encode(query, convert_to_tensor=True, device=device).cpu().numpy()
-    query_embedding = query_embedding.reshape(1, -1)
-    similarities = cosine_similarity(query_embedding, embeddings).flatten()
-    top_index = similarities.argsort()[-1]
+    
+    # Reshape query_embedding to ensure it is a 2D array
+    query_embedding = query_embedding.reshape(1, -1)  # Reshape to (1, n_features)
+
+    # Compute cosine similarity between query embedding and dataset embeddings
+    similarities = cosine_similarity(query_embedding, embeddings).flatten()  # Flatten to get a 1D array of similarities
+
+    # Get the index of the most similar result (top 1)
+    top_index = similarities.argsort()[-1]  # Index of highest similarity
+
+    # Retrieve metadata for the top result
     top_result = metadata.iloc[top_index].copy()
     top_result['similarity_score'] = similarities[top_index]
+
     return top_result
 
-# Function to generate response using OpenAI API
+# Function to generate response using OpenAI API with debugging logs
 def generate_response(messages):
+    debug_log_entry = {"messages": messages}
+    st.session_state.debug_logs.append(debug_log_entry)  # Store debug log
+    
     response = client.chat.completions.create(
         model="o1-mini",
         messages=messages,
     )
+    
     return response.choices[0].message.content
 
 # User input form for generating a new question
 with st.form(key="input_form"):
-    company = st.text_input("Company", value="Google")
-    difficulty = st.selectbox("Difficulty", ["Easy", "Medium", "Hard"], index=1)
-    topic = st.text_input("Topic (e.g., Backtracking)", value="Backtracking")
+    company = st.text_input("Company", value="Google")  # Default value: Google
+    difficulty = st.selectbox("Difficulty", ["Easy", "Medium", "Hard"], index=1)  # Default: Medium
+    topic = st.text_input("Topic (e.g., Backtracking)", value="Backtracking")  # Default: Backtracking
+    
     generate_button = st.form_submit_button(label="Generate")
 
 if generate_button:
-    # Clear session state and reset follow-up mode
+    # Clear session state and start fresh with follow-up mode disabled
     st.session_state.messages = []
     st.session_state.follow_up_mode = False
 
@@ -91,55 +107,61 @@ if generate_button:
         f"\nPlease create a real-world interview question based on this information."
     )
     
-    # Generate response using GPT-4
+    # Generate response using GPT with detailed prompt (question generation prompt excluded here)
     response = generate_response([{"role": "user", "content": detailed_prompt}])
-    
-    # Store the generated question for display but do not add the prompt to history
+
+    # Store generated question in session state for persistence in sidebar and follow-up conversation state
     st.session_state.generated_question = response
+
+    # Add the generated question to the conversation history as an assistant message (omit prompts in the chat)
     st.session_state.messages.append({"role": "assistant", "content": response})
 
-    # Enable follow-up mode
+    # Enable follow-up mode after generating the initial question
     st.session_state.follow_up_mode = True
 
-# Display the generated question and follow-up chat
+# Display chat messages from history on app rerun (for subsequent conversation)
 for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
+    if not message["content"].startswith("Please create a real-world interview question"):  # Exclude prompts
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
 
+# 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 follow-up response using the interviewer prompt but exclude it from history
-        follow_up_response = generate_response(
+        # Generate assistant's response based on follow-up input using technical_interviewer_prompt as system prompt
+        assistant_response = generate_response(
             [{"role": "user", "content": user_input}]
         )
-        
+
         with st.chat_message("assistant"):
-            st.markdown(follow_up_response)
+            st.markdown(assistant_response)
         
-        st.session_state.messages.append({"role": "assistant", "content": follow_up_response})
+        st.session_state.messages.append({"role": "assistant", "content": assistant_response})
+
+st.sidebar.markdown("""
+## About
+This is a Real-World Interview Question Generator powered by AI.
+Enter a company name, topic, and level of difficulty, and it will transform a relevant question into a real-world interview scenario!
+Continue chatting with the AI interviewer in the chatbox.
+""")
 
-# Sidebar content to display the generated question
+# Sidebar content to display persistent generated question (left sidebar)
 st.sidebar.markdown("## Generated Question")
 if st.session_state.generated_question:
     st.sidebar.markdown(st.session_state.generated_question)
 else:
     st.sidebar.markdown("_No question generated yet._")
 
-st.sidebar.markdown("""
-## About
-This is a Real-World Interview Question Generator powered by AI.
-Enter a company name, topic, and level of difficulty, and it will transform a relevant question into a real-world interview scenario.
-""")
-
-# Debug logs and code interpreter section
+# Right sidebar toggleable debug logs and code interpreter section
 with st.expander("Debug Logs (Toggle On/Off)", expanded=False):
     if len(st.session_state.debug_logs) > 0:
-        for log_entry in reversed(st.session_state.debug_logs):
+        for log_entry in reversed(st.session_state.debug_logs):  # Show most recent logs first
             st.write(log_entry)
 
 st.sidebar.markdown("---")
@@ -148,12 +170,13 @@ code_input = st.sidebar.text_area("Write your Python code here:")
 if st.sidebar.button("Run Code"):
     try:
         exec_globals = {}
-        exec(code_input, exec_globals)
+        exec(code_input, exec_globals)  # Execute user-provided code safely within its own scope.
         output_key = [k for k in exec_globals.keys() if k != "__builtins__"]
         if output_key:
             output_value = exec_globals[output_key[0]]
             st.sidebar.success(f"Output: {output_value}")
         else:
             st.sidebar.success("Code executed successfully!")
+            
     except Exception as e:
         st.sidebar.error(f"Error: {e}")