vertex ai minor bugs 8

app.py

@@ -2,70 +2,56 @@ import gradio as gr
 from src.graph import build_graph
 from src.interview_logic import EXCEL_QUESTIONS
 from src.local_llm_handler import load_llm_pipeline
-from src.perplexity_detector import load_detector_model # Import questions for state building
+from src.perplexity_detector import load_detector_model
 
-# Initialize the graph, which is stateless and operates on the state dict we provide.
+# Initialize the graph
 graph = build_graph()
 
 def run_graph_logic(message: str, history: list[list[str]]):
-    """
-    This helper function contains the core logic for running the LangGraph chain.
-    It's separated from the UI code for clarity.
-    """
-    # 1. Convert Gradio's history (list of lists) into our graph's internal format (list of tuples)
+    """Contains the core logic for running the LangGraph chain."""
     internal_history = []
     for user_msg, assistant_msg in history:
         if user_msg:
             internal_history.append(("user", user_msg))
         if assistant_msg:
-            # Split combined messages back into individual parts
             parts = assistant_msg.split("\n\n")
             for part in parts:
                 internal_history.append(("ai", part))
     
-    # NOTE: The user's new message is already in the history passed to this function.
-    # We find the length of the history *before* the bot adds its response.
     len_before = len(internal_history)
 
-    # 2. Build the state dictionary to pass to the graph on every turn.
+    # --- THIS BLOCK CONTAINS THE CRITICAL FIX ---
+    
+    # 1. Count how many questions have already been asked.
     question_count = sum(1 for role, content in internal_history if content in EXCEL_QUESTIONS)
     
+    # 2. Determine the index of the question that was JUST answered.
+    #    If 1 question is in the history, its index is 0. This is the correct index for evaluation.
+    current_question_index = question_count - 1 if question_count > 0 else 0
+    
+    # 3. Build the state dictionary with the CORRECT index.
     current_state = {
-        "interview_status": 0 if len(history) <= 1 else 1, # Status is 0 only for the very first message
+        "interview_status": 0 if len(history) <= 1 else 1,
         "interview_history": internal_history,
         "questions": EXCEL_QUESTIONS,
-        "question_index": question_count,
+        "question_index": current_question_index, # Use the corrected index
         "evaluations": [], 
     }
 
-    # 3. Invoke the graph with the current state.
-    print("Invoking graph with current state...")
+    print(f"Invoking graph. Question count in history: {question_count}. Using index for evaluation: {current_question_index}.")
     new_state = graph.invoke(current_state)
     
-    # 4. Extract ONLY the new messages generated by the bot in this turn.
     new_messages = new_state["interview_history"][len_before:]
     bot_responses = [content for role, content in new_messages if role == "ai"]
     
-    # 5. Return the bot's reply as a single string.
     return "\n\n".join(bot_responses)
 
 
 def user_sends_message(user_message: str, history: list[list[str]]):
-    """
-    This function is called when the user submits their message.
-    It provides instant UI feedback by adding the user's message to the chat,
-    then streams the bot's response.
-    """
-    # Append the user's message to the history for immediate display
+    """Called when the user submits their message."""
     history.append([user_message, None])
-    
-    # Get the bot's response from our backend logic
     bot_response = run_graph_logic(user_message, history)
-    
-    # Update the last message in the history with the bot's full response
     history[-1][1] = bot_response
-    
-    # Return the updated history for the chatbot and an empty string to clear the textbox
     return history, ""
 
 
@@ -84,24 +70,24 @@ with gr.Blocks(theme="soft", css=".gradio-container {max-width: 1200px; margin:
         """
     )
 
+    # Added type="messages" to fix the UserWarning from the logs
     chatbot = gr.Chatbot(
         label="Interview Conversation",
+        type="messages",
         height=600,
         show_copy_button=True,
         placeholder="The interview will begin after you send your first message.",
         avatar_images=(None, "https://upload.wikimedia.org/wikipedia/commons/1/1d/Microsoft_Excel_2013-2019_logo.svg")
     )
 
-    # Create a row for the textbox and submit button
     with gr.Row():
         user_input = gr.Textbox(
             show_label=False,
             placeholder="Type your answer here and press Enter...",
-            scale=5  # Make the textbox larger
+            scale=5
         )
         submit_btn = gr.Button("Submit", variant="primary", scale=1)
 
-    # Create a row for the examples and clear button
     with gr.Row():
         clear_btn = gr.Button("Clear and Restart Interview", variant="stop")
         gr.Examples(
@@ -109,24 +95,21 @@ with gr.Blocks(theme="soft", css=".gradio-container {max-width: 1200px; margin:
             inputs=user_input
         )
 
-    # --- EVENT LISTENERS ---
-    # Define what happens when the user clicks the submit button or presses Enter
     submit_btn.click(
         fn=user_sends_message,
         inputs=[user_input, chatbot],
-        outputs=[chatbot, user_input] # Update chatbot and clear user_input
+        outputs=[chatbot, user_input]
     )
     user_input.submit(
         fn=user_sends_message,
         inputs=[user_input, chatbot],
         outputs=[chatbot, user_input]
     )
-    # Define what happens when the user clicks the clear button
     clear_btn.click(
         fn=clear_chat,
         inputs=None,
         outputs=[chatbot, user_input],
-        queue=False # Clearing should be instant
+        queue=False
     )
 
 if __name__ == "__main__":