Added the app

.env

@@ -0,0 +1 @@
+OPENAI_API_KEY=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

.gradio/certificate.pem

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+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq
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+-----END CERTIFICATE-----

app.py

@@ -0,0 +1,271 @@
+# Core LlamaIndex imports
+from llama_index.core import (
+	VectorStoreIndex,
+	SimpleDirectoryReader,
+	Document,
+	Settings
+)
+from llama_index.llms.openai import OpenAI
+from llama_index.core.memory import ChatMemoryBuffer
+
+# Python standard library imports
+import os
+import re
+import sys
+from datetime import datetime
+from typing import List, Dict
+
+# Third-party imports
+from dotenv import load_dotenv
+import gradio as gr
+
+# Load environment variables
+load_dotenv()
+
+# Check for API key
+if not os.getenv("OPENAI_API_KEY"):
+	print("Error: OpenAI API key not found in .env file")
+	print("Please create a .env file with your API key: API_KEY=your_key_here")
+	sys.exit(1)
+
+# Configure global settings
+Settings.llm = OpenAI(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
+Settings.chunk_size = 1024
+Settings.chunk_overlap = 20
+
+class PersonalityAnalyzer:
+	def __init__(self):
+		self.memory = ChatMemoryBuffer.from_defaults(token_limit=4096)
+
+	def analyze_mbti(self, conversation_history: List[Dict]) -> str:
+		# Create a document from conversation history
+		history_text = self._format_history_for_analysis(conversation_history)
+		doc = Document(text=history_text)
+		
+		# Create an index for the conversation history
+		index = VectorStoreIndex.from_documents([doc])
+		
+		# Create a query engine
+		query_engine = index.as_query_engine()
+		
+		prompt = """
+		Based on the conversation history, analyze the player's likely MBTI personality type.
+		Consider:
+		- How they approach problems (direct vs. creative)
+		- Their interaction style with NPCs
+		- Whether they seek information or action
+		- How they make decisions
+		
+		Provide the most likely MBTI type with reasoning based on their actions.
+		"""
+
+		response = query_engine.query(prompt)
+		return str(response)
+
+	def analyze_enneagram(self, conversation_history: List[Dict]) -> str:
+		# Create a document from conversation history
+		history_text = self._format_history_for_analysis(conversation_history)
+		doc = Document(text=history_text)
+		
+		# Create an index for the conversation history
+		index = VectorStoreIndex.from_documents([doc])
+		
+		# Create a query engine
+		query_engine = index.as_query_engine()
+		
+		prompt = """
+		Based on the conversation history, analyze the player's likely Enneagram type.
+		Consider:
+		- Their core motivations and fears shown through actions
+		- What rewards or outcomes they seek
+		- How they handle challenges
+		- Their interaction patterns with the world
+		
+		Provide the most likely Enneagram type with reasoning based on their actions.
+		"""
+
+		response = query_engine.query(prompt)
+		return str(response)
+
+	def _format_history_for_analysis(self, history: List[Dict]) -> str:
+		return "\n".join([
+			f"Player: {h['action']}\nContext: {h['context']}\nResponse: {h['response']}" 
+			for h in history
+		])
+
+class WorldState:
+	def __init__(self):
+		self.inventory: List[str] = []
+		self.relationships: Dict[str, int] = {}  # NPC relationships
+		self.current_context: str = ""
+		self.significant_choices: List[Dict] = []  # Tracks important decisions
+		self.conversation_history: List[Dict] = []
+		self.discovered_locations: set = set()
+		self.active_quests: List[Dict] = []
+		
+	def add_to_history(self, action: str, context: str, response: str):
+		self.conversation_history.append({
+			"timestamp": datetime.now().isoformat(),
+			"action": action,
+			"context": context,
+			"response": response
+		})
+
+class StoryEngine:
+	def __init__(self):
+		self.memory = ChatMemoryBuffer.from_defaults(token_limit=4096)
+
+	def generate_response(self, action: str, world_state: WorldState) -> str:
+		# Build context from recent history
+		recent_history = world_state.conversation_history[-5:] if world_state.conversation_history else []
+		history_text = "\n".join([
+			f"Player: {h['action']}\nGame: {h['response']}" 
+			for h in recent_history
+		])
+
+		context = f"""
+		Current game state:
+		Inventory: {world_state.inventory}
+		Active quests: {world_state.active_quests}
+		Discovered locations: {world_state.discovered_locations}
+		
+		Recent history:
+		{history_text}
+		"""
+
+		doc = Document(text=context)
+		index = VectorStoreIndex.from_documents([doc])
+		
+		query_engine = index.as_query_engine()
+		
+		prompt = f"""
+		Based on the current game state and player's action: "{action}"
+				 Aims to capture the personality type of the user in terms of MBTI (present scenarios/quests that may reveal that) (DON'T Explicitly mention that) 
+
+		Generate a response that:
+		2. Acknowledges the player's action
+		3. Describes the consequences
+		4. Updates the world state if needed
+		5. Introduces potential opportunities or challenges
+		6. Maintains internal consistency with previous events
+		7. Is short and doesn't contain unnecessary details or elaborate language
+		
+		[Important] Don't explicitly mention world state items.  
+		[Important] Try to present scenarios or developments which aims to reveal personality traits of the player.  
+		[Important] Response should be engaging, consequential and open-ended. It shouldn't lead the player at all, don't mention ANY call to action or what a player does or feel without an explicit prompt from the user. 
+		"""
+
+		response = query_engine.query(prompt)
+		return str(response)
+
+class TextAdventure:
+	def __init__(self):
+		self.story_engine = StoryEngine()
+		self.personality_analyzer = PersonalityAnalyzer()
+		self.world_state = WorldState()
+
+	def _extract_game_updates(self, response: str) -> None:
+		"""Parse response for any updates to world state"""
+		# Look for inventory changes
+		inventory_matches = re.findall(r"You (picked up|received|found|lost|dropped) (a|an|the) ([^\.]+)", response.lower())
+		for action, _, item in inventory_matches:
+			if action in ['picked up', 'received', 'found']:
+				self.world_state.inventory.append(item.strip())
+			elif action in ['lost', 'dropped']:
+				if item.strip() in self.world_state.inventory:
+					self.world_state.inventory.remove(item.strip())
+
+		# Look for new locations
+		location_matches = re.findall(r"You (discovered|arrived at|entered) (the|a|an) ([^\.]+)", response.lower())
+		for _, _, location in location_matches:
+			self.world_state.discovered_locations.add(location.strip())
+
+	def gradio_play(self, action, history):
+		if action.lower() in ['quit', 'exit', 'bye']:
+			self._show_personality_analysis()
+			return history + [[action, "Game ended. Thanks for playing!"]], history + [[action, "Game ended. Thanks for playing!"]]
+		
+		if action.lower() in ['inventory', 'i']:
+			inventory_text = "\nInventory: " + (", ".join(self.world_state.inventory) or "empty")
+			return history + [[action, inventory_text]], history + [[action, inventory_text]]
+		
+		if action.lower() in ['help', 'h', '?']:
+			help_text = self._get_help_text()
+			return history + [[action, help_text]], history + [[action, help_text]]
+		
+		response = self.story_engine.generate_response(action, self.world_state)
+		
+		# Update world state
+		self.world_state.add_to_history(action, self.world_state.current_context, response)
+		self._extract_game_updates(response)
+		
+		# Periodically show personality insights
+		if len(self.world_state.conversation_history) % 10 == 0:
+			response += "\n\n" + self._get_personality_analysis()
+		
+		return history + [[action, response]], history + [[action, response]]
+
+	def _get_help_text(self):
+		return """
+		Game Help:
+		- Type any action you want to try
+		- Use 'inventory' or 'i' to check your items  
+		- Type 'quit' to end the game
+		- The story adapts to your choices
+		- Your actions reveal your personality
+		"""
+
+	def _get_personality_analysis(self):
+		if len(self.world_state.conversation_history) < 3:
+			return "Need more interactions for personality analysis..."
+		
+		mbti = self.personality_analyzer.analyze_mbti(self.world_state.conversation_history)
+		enneagram = self.personality_analyzer.analyze_enneagram(self.world_state.conversation_history)
+		
+		return f"""
+				=== Personality Analysis ===
+
+				MBTI Analysis:
+				{mbti}
+
+				Enneagram Analysis: 
+				{enneagram}
+
+				=========================
+				"""
+
+if __name__ == "__main__":
+	game = TextAdventure()
+	
+	with gr.Blocks() as demo:
+		chatbot = gr.Chatbot([], elem_id="chatbot",height=500)
+		
+		with gr.Row():
+			with gr.Column(scale=0.85):
+				txt = gr.Textbox(
+					show_label=False,
+					placeholder="Enter your action...",container=False
+				)
+			with gr.Column(scale=0.15, min_width=0):
+				btn = gr.Button("Send")
+				
+		btn.click(game.gradio_play, [txt, chatbot], [chatbot, chatbot])
+		txt.submit(game.gradio_play, [txt, chatbot], [chatbot, chatbot])
+		
+		gr.Markdown(
+			"""
+			Welcome to the Open-Ended Personality Adventure! This is a completely free-form adventure where you can try anything you want. Your choices and actions will reveal aspects of your personality. 
+			
+			You find yourself standing in a mysterious forest clearing on a misty morning. What would you like to do?
+			
+			Game Help:
+			- Type any action you want to try
+			- Use 'inventory' or 'i' to check your items  
+			- Type 'quit' to end the game
+			- The story adapts to your choices
+			- Your actions reveal your personality
+
+			"""
+		)
+		
+	demo.launch(share=True)