Merge pull request #5 from gmook9/dev

Fixed conversation memory logic + compiled executable for windows

Author: gmook9

.gitignore

@@ -1,3 +1,20 @@
+# Virtual Environment
 venv/
 *.env
-__pycache__/
+
+# Python Bytecode
+__pycache__/
+*.pyc
+
+# PyInstaller Build Files
+/build/
+*.spec
+
+# Ignore everything in dist except the .exe file
+dist/*
+!dist/*.exe
+
+# Logs and OS-specific files
+*.log
+.DS_Store
+Thumbs.db

README.md

@@ -5,26 +5,59 @@
 # Detective 9 Text Based Game
 
 ## Overview
-Detective Game is an interactive text-based game where you play the role of a detective. This sample is meant to show of Llama 3 from Meta running locally. The AI bot that is running on Llama 3 via Ollama assumes a random role related to a crime scenario. Your goal is to determine whether the AI bot is innocent or guilty through a series of questions.
+Detective Game is an interactive text-based game where you play the role of a detective. This sample is meant to show off Llama 3 from Meta running locally. The AI bot that is running on Llama 3 via Ollama assumes a random role related to a crime scenario. Your goal is to determine whether the AI bot is innocent or guilty through a series of questions.
 
 ## Info
 - **Llama 3**: Open source Large Language Model from Meta for generating AI responses.
 - **Ollama**: Platform for running Llama 3 locally.
 - **Rich**: Library for creating beautiful terminal outputs.
 
-## Requirements
-- Llama 3 model files (from Ollama)
-- `python-dotenv` for environment variable management
-- `rich` for terminal UI
-- `langchain_community` for Llama 3 integration
+### Requirements for Running the Source Code:
+- **Llama 3 model files**: Required to generate AI responses (download from Ollama).
+- **Python Libraries**:
+  - `python-dotenv`: For environment variable management.
+  - `rich`: For terminal UI.
+  - `langchain_community`: For Llama 3 integration.
+  
+  These can be installed by running:
+  ```bash
+  pip install -r requirements.txt
 
 ## Ollama Download
-Download: https://ollama.com/download
+Download: https://ollama.com/download  
 Tutorial: https://www.youtube.com/watch?v=Asleok-Snfs
 
 ## Startup
-The command `ollama run llama3` initializes and runs the Llama 3 model locally using the Ollama platform. Without this it will not generate AI responses.
 
-Run the command `python startGame.py` to start the game.
+### Method 1: Running the Source Code
 
+1. **Initialize Llama 3**:
+   - Run the command `ollama run llama3` to initialize and run the Llama 3 model locally using the Ollama platform. This is required for generating AI responses.
 
+2. **Install Dependencies**:
+   - Install the required Python packages by running:
+     ```bash
+     pip install -r requirements.txt
+     ```
+
+3. **Start the Game**:
+   - Run the following command to start the game:
+     ```bash
+     python startGame.py
+     ```
+
+### Method 2: Running the Executable
+
+1. **Initialize Llama 3**:
+   - Just like with the source code method, you need to run the Llama 3 model:
+     ```bash
+     ollama run llama3
+     ```
+
+2. **Run the Game**:
+   - Navigate to the `dist` folder where the executable is located.
+   - Double-click on `startGame.exe` to launch the game.
+
+   Alternatively, you can run the executable via the command line:
+   ```bash
+   ./dist/startGame.exe

ai_bot.py

@@ -1,22 +1,37 @@
 from random import choice
 from langchain_community.llms import Ollama
 
-# TO-DO FIX THIS
 class QuestionGenerator:
     def __init__(self, ai_bot):
         self.ai_bot = ai_bot
+        self.asked_questions = set()  # Track asked questions and responses
 
     def generate_random_question(self, case_synopsis):
-        # Use the provided case synopsis instead of generating a new one
+        # Prepare the context with past questions and responses
+        context = "\n".join([f"Question: {q}\nResponse: {r}" for q, r in self.ai_bot.conversation_history])
+
         question_prompt = (
             f"You are playing the role of a detective in a text-based detective game. Below is a fictional case synopsis:\n\n"
             f"'{case_synopsis}'\n\n"
-            f"Your task is to generate a question that is appropriate for this scenario. "
+            f"Here are the previous questions and responses:\n\n"
+            f"{context}\n\n"
+            f"Your task is to generate a new question that is appropriate for this scenario. "
+            f"Make sure that the question is not similar to any of the previous ones and avoid asking redundant information. "
             f"Remember, this is a fictional game setting, so avoid any references to real-life guidance or advice. "
             f"The question should relate only to the details provided in the synopsis and should aim to uncover more information about the case.\n"
             f"Please provide only the question."
         )
-        return self.ai_bot.llm.invoke(question_prompt)
+        
+        # Generate a question
+        new_question = self.ai_bot.llm.invoke(question_prompt)
+        
+        # Check if the question was already asked
+        if new_question in self.asked_questions:
+            return self.generate_random_question(case_synopsis)  # Retry if it's a repeat
+
+        # Store the new question
+        self.asked_questions.add(new_question)
+        return new_question
 
 class AIBot:
     def __init__(self):
@@ -30,15 +45,15 @@ def generate_synopsis(self):
         return self.llm.invoke(self.prompt)
 
     def respond(self, question):
-        self.conversation_history.append(f"Question: {question}")  # Add question to convo history
-        context = "\n".join(self.conversation_history)
+        self.conversation_history.append((question, self.llm.invoke(f"Respond to: {question}")))  # Add question and response to convo history
+        context = "\n".join([f"Question: {q}\nResponse: {r}" for q, r in self.conversation_history])
         response_prompt = (
             f"Role: {self.role}. You are being questioned. "
             f"Here is the context so far:\n{context}\n\n"
-            f"Now respond to the latest question: {question}. Only response in 1-2 setnences."
+            f"Now respond to the latest question: {question}. Only respond in 1-2 sentences."
         )
         response = self.llm.invoke(response_prompt)  # Get AI Response
-        self.conversation_history.append(f"Response: {response}")  # Add response to convo history
+        self.conversation_history[-1] = (question, response)  # Update with the actual response
 
         return response
 
@@ -47,4 +62,4 @@ def is_guilty(self):
 
     # Modify this method to accept synopsis as an argument
     def generate_random_question(self, case_synopsis):
-        return self.question_generator.generate_random_question(case_synopsis)
+        return self.question_generator.generate_random_question(case_synopsis)

dist/startGame.exe

@@ -55,22 +55,37 @@ def ask_question(self):
         self.console.print("1. Random")
         self.console.print("2. Type my own\n")
         choice = self.console.input("[bold yellow]Enter your choice (1/2): [/bold yellow]").strip()
-        
+
         if choice == "1":
             user_question = self.ai_bot.generate_random_question(self.synopsis)  # Pass the stored synopsis
         elif choice == "2":
             user_question = self.console.input("Type your question below: ").strip()
+            
+            # Get the context from the conversation history
+            context = "\n".join([f"Question: {q}\nResponse: {r}" for q, r in self.ai_bot.conversation_history])
+            
+            # Create a prompt for AI to respond considering the context
+            response_prompt = (
+                f"Role: {self.ai_bot.role}. You are being questioned. It is a text-based detective game. "
+                f"You know that you are {'guilty' if self.ai_bot.is_guilty() else 'innocent'}, but try not to give that away directly in your responses. "
+                f"Here is the context so far:\n{context}\n\n"
+                f"Now respond to the latest question: {user_question}. Only respond in 1-2 sentences."
+            )
+            response = self.ai_bot.llm.invoke(response_prompt)  # Get AI Response
+            self.ai_bot.conversation_history.append((user_question, response))  # Add the user question and AI response to convo history
+            
         else:
             self.console.print("[bold red]Invalid choice. Please try again.[/bold red]\n")
             return self.ask_question()
 
         self.questions_asked += 1
         self.console.print("\n")
         self.console.print(Text(f"Question {self.questions_asked}: {user_question}", style="bold red"))
-        response = self.ai_bot.respond(user_question)
+        response = self.ai_bot.respond(user_question) if choice == "1" else response
         self.console.print(Panel(response, title=f"Response [{self.questions_asked}/{self.max_questions}]"))
         self.console.print("\n")
 
+
     def make_final_decision(self):
         self.console.print("\n[bold bright_magenta]Final Decision:[/bold bright_magenta]")
         self.console.print("1. Release")