Update app.py

app.py

@@ -8,36 +8,46 @@ client = Together(api_key=get_together_api_key())
 
 # Gradio interface functions
 def run_action(message, history):
-    """
-    Generate a response based on the user's input and validate it against guardrails.
-    """
+    global game_state, game_running  # Access the global game state and game status
+
+    if not game_running:
+        return "The game has ended. Type 'restart the game' to play again."
+
+    if message.lower() == "start game":
+        return game_state["start"]
+
+    if message.lower() == "restart the game":
+        game_state = initialize_game_state()
+        return "Game restarted! " + game_state["start"]
+
     if message.lower() == "exit":
-        return "Thank you for using the Finance Chatbot. Goodbye!"
+        game_running = False
+        return "The game has ended. Type 'restart the game' to play again."
 
-    # Construct the prompt for the assistant
     system_prompt = """You are a financial assistant. You can only answer finance-related queries.
     - Do not answer non-finance questions.
+    - Answer in 50 words
     - Ensure responses adhere to the safety policy."""
 
     messages = [{"role": "system", "content": system_prompt}]
 
-    for user_message, bot_response in history:
-        messages.append({"role": "user", "content": user_message})
-        messages.append({"role": "assistant", "content": bot_response})
+    # Convert history into the appropriate format
+    for entry in history:
+        if entry["role"] == "user":
+            messages.append({"role": "user", "content": entry["content"]})
+        elif entry["role"] == "assistant":
+            messages.append({"role": "assistant", "content": entry["content"]})
 
+    # Add the user's current action
     messages.append({"role": "user", "content": message})
 
-    # Generate response using LLM
-    response = client.chat.completions.create(
+    # Get the model's response
+    model_output = client.chat.completions.create(
         model="meta-llama/Llama-3-70b-chat-hf",
         messages=messages,
-    ).choices[0].message.content
-
-    # Check if the response is safe
-    if not is_safe(response):
-        return "Sorry, I cannot provide a safe response to that query."
+    )
 
-    return response
+    return model_output.choices[0].message.content
 
 
 def main_loop(message, history):