vertex ai minor bugs 9

app.py

@@ -7,57 +7,60 @@ from src.perplexity_detector import load_detector_model
 # Initialize the graph
 graph = build_graph()
 
-def run_graph_logic(message: str, history: list[list[str]]):
-    """Contains the core logic for running the LangGraph chain."""
+def run_graph_logic(history: list[dict[str, str]]):
+    """
+    This helper function now works directly with Gradio's "messages" format.
+    """
+    # 1. Convert Gradio's history (list of dicts) to our graph's internal format (list of tuples)
     internal_history = []
-    for user_msg, assistant_msg in history:
-        if user_msg:
-            internal_history.append(("user", user_msg))
-        if assistant_msg:
-            parts = assistant_msg.split("\n\n")
-            for part in parts:
-                internal_history.append(("ai", part))
+    for turn in history:
+        # The 'role' and 'content' keys are guaranteed by Gradio's new format
+        internal_history.append((turn["role"], turn["content"]))
     
     len_before = len(internal_history)
 
-    # --- THIS BLOCK CONTAINS THE CRITICAL FIX ---
-    
-    # 1. Count how many questions have already been asked.
+    # 2. Build the state dictionary with the correct index for the current operation
     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,
         "interview_history": internal_history,
         "questions": EXCEL_QUESTIONS,
-        "question_index": current_question_index, # Use the corrected index
+        "question_index": current_question_index,
         "evaluations": [], 
     }
 
     print(f"Invoking graph. Question count in history: {question_count}. Using index for evaluation: {current_question_index}.")
     new_state = graph.invoke(current_state)
     
+    # 3. Extract ONLY the new bot messages to append to the history
     new_messages = new_state["interview_history"][len_before:]
-    bot_responses = [content for role, content in new_messages if role == "ai"]
+    bot_responses = [content for role, content in new_messages if role in ["ai", "assistant"]]
     
     return "\n\n".join(bot_responses)
 
 
-def user_sends_message(user_message: str, history: list[list[str]]):
-    """Called when the user submits their message."""
-    history.append([user_message, None])
-    bot_response = run_graph_logic(user_message, history)
-    history[-1][1] = bot_response
-    return history, ""
+def user_sends_message(history: list[dict[str, str]]):
+    """
+    This function is now simpler and works with the correct data format.
+    Gradio automatically appends the user's message to the history.
+    """
+    # The user's message is the last item in the history list
+    user_message = history[-1]["content"]
+    
+    # Get the bot's response from our backend logic
+    bot_response = run_graph_logic(history)
+    
+    # Append the bot's response to the history
+    history.append({"role": "assistant", "content": bot_response})
+    
+    return history
 
 
 def clear_chat():
-    """Called when the 'Clear' button is pressed."""
-    return [], ""
+    """Called when the 'Clear' button is pressed. Returns an empty list."""
+    return []
 
 
 # --- MANUAL UI LAYOUT WITH GR.BLOCKS ---
@@ -70,7 +73,7 @@ with gr.Blocks(theme="soft", css=".gradio-container {max-width: 1200px; margin:
         """
     )
 
-    # Added type="messages" to fix the UserWarning from the logs
+    # This chatbot component now correctly receives the data format it expects.
     chatbot = gr.Chatbot(
         label="Interview Conversation",
         type="messages",
@@ -95,20 +98,23 @@ with gr.Blocks(theme="soft", css=".gradio-container {max-width: 1200px; margin:
             inputs=user_input
         )
 
+    # --- EVENT LISTENERS (Updated for simplicity) ---
+    # The submit function now only takes the chatbot history as input
+    # and returns the updated history. Gradio handles the user input automatically.
     submit_btn.click(
         fn=user_sends_message,
-        inputs=[user_input, chatbot],
-        outputs=[chatbot, user_input]
+        inputs=[chatbot],
+        outputs=[chatbot]
     )
     user_input.submit(
         fn=user_sends_message,
-        inputs=[user_input, chatbot],
-        outputs=[chatbot, user_input]
+        inputs=[chatbot],
+        outputs=[chatbot]
     )
     clear_btn.click(
         fn=clear_chat,
         inputs=None,
-        outputs=[chatbot, user_input],
+        outputs=[chatbot],
         queue=False
     )