vertex ai minor bugs 2

app.py

@@ -1,70 +1,61 @@
 import gradio as gr
 from src.graph import build_graph
+from src.interview_logic import EXCEL_QUESTIONS # Import questions for state building
 
 # Initialize the graph, which is stateless and operates on the state dict we provide.
 graph = build_graph()
 
-def chat_fn(message: str, history: list[dict[str, str]]):
+def chat_fn(message: str, history: list[list[str]]):
     """
-    Main chat function for Gradio. It is now self-contained and uses the provided
-    history as the source of truth for the conversation state.
+    Main chat function for Gradio.
 
-    Args:
-        message: The new message from the user.
-        history: The entire conversation history in Gradio's format.
+    THE FIX: This function now returns a single string for the bot's reply.
+    Gradio's ChatInterface handles the history update automatically and reliably,
+    which prevents the submit button from disappearing.
     """
-    # 1. Convert Gradio's history (list of dicts) to the graph's expected
-    #    internal format (list of tuples).
+    # 1. Convert Gradio's history (list of lists) into our graph's internal format (list of tuples)
     internal_history = []
-    for turn in history:
-        internal_history.append((turn["role"], turn["content"]))
+    for user_msg, assistant_msg in history:
+        if user_msg:
+            internal_history.append(("user", user_msg))
+        if assistant_msg:
+            # Split assistant messages that might contain multiple parts from previous turns
+            # This is important for correctly reconstructing the state
+            parts = assistant_msg.split("\n\n")
+            for part in parts:
+                internal_history.append(("ai", part))
 
-    # 2. Determine the current state based on the history.
-    #    - How many questions have been asked by the assistant?
-    question_count = sum(1 for role, content in internal_history if content in EXCEL_QUESTIONS)
+    # Store the length of the history *before* adding the new message.
+    # We will use this to find out what the bot's new reply is.
+    len_before = len(internal_history)
     
-    #    - Is the interview over?
-    interview_status = 2 if any("interview will now be terminated" in msg[1] for msg in internal_history) or any("performance summary" in msg[1].lower() for msg in internal_history) else 1
+    # Add the user's new message to the history for the graph to process
+    internal_history.append(("user", message))
+
+    # 2. Build the state dictionary to pass to the graph on every turn.
+    question_count = sum(1 for role, content in internal_history if content in EXCEL_QUESTIONS)
     
-    # 3. Build the state dictionary to pass to the graph on every turn.
-    #    This makes our function stateless, avoiding session-related bugs.
     current_state = {
-        "interview_status": 0 if not internal_history else interview_status,
+        "interview_status": 0 if not history else 1, # Status is 0 only if history is empty
         "interview_history": internal_history,
-        "questions": EXCEL_QUESTIONS, # Assuming EXCEL_QUESTIONS is accessible
+        "questions": EXCEL_QUESTIONS,
         "question_index": question_count,
-        "evaluations": [], # Note: This simple state doesn't persist evaluations across turns. A more robust solution might need to parse them from history.
-        "final_feedback": "",
-        "warnings": []
+        "evaluations": [], # This remains stateless for simplicity
     }
 
-    # Add the user's new message to the history for the graph to process
-    current_state["interview_history"].append(("user", message))
-    
-    # 4. Invoke the graph with the current state.
-    print(f"Invoking graph with question index: {current_state['question_index']}")
+    # 3. Invoke the graph with the current state.
+    print(f"Invoking graph with current state...")
     new_state = graph.invoke(current_state)
     
-    # 5. Convert the graph's output history back to Gradio's format.
-    #    The graph returns the *complete* new history.
-    gradio_history = []
-    for role, content in new_state["interview_history"]:
-        if role in ["user", "assistant", "ai"]: # Handle 'ai' as 'assistant'
-            gradio_history.append({"role": "assistant" if role == "ai" else role, "content": content})
-            
-    return gradio_history
+    # 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)
 
-# This list must be accessible to the chat function.
-# It's better to define it here or import it from a shared config.
-EXCEL_QUESTIONS = [
-    "What is the difference between the VLOOKUP and HLOOKUP functions in Excel?",
-    "Explain how to use the INDEX and MATCH functions together, and why you might prefer them over VLOOKUP.",
-    "Describe what a Pivot Table is and give an example of a scenario where it would be useful.",
-    "What is Conditional Formatting in Excel? Can you provide an example?",
-]
 
-# Create the ChatInterface. No separate clear function is needed if we don't have a session.
-# The 'Clear' button is built-in and will correctly clear the UI.
+# Create the ChatInterface.
 demo = gr.ChatInterface(
     fn=chat_fn,
     title="🤖 AI-Powered Excel Interviewer (Phi-3 Mini)",
@@ -72,7 +63,8 @@ demo = gr.ChatInterface(
     chatbot=gr.Chatbot(
         show_copy_button=True,
         height=600,
-        placeholder="The interview will begin after you send your first message."
+        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")
     ),
     textbox=gr.Textbox(
         placeholder="Type your answer here and press Enter...", 
@@ -81,7 +73,7 @@ demo = gr.ChatInterface(
     ),
     theme="soft",
     submit_btn="Submit Answer",
-    examples=[ # These examples now work as intended.
+    examples=[
         "I'm ready to start the interview",
         "Let's begin",
         "Start the assessment"