Merge pull request #194 from dataquestio/ai-skill-path-gp-solutions

Solutions for 903 and 909

Mission903Solutions.py

@@ -0,0 +1,187 @@
+from openai import OpenAI
+import tiktoken
+import json
+from datetime import datetime
+import os
+import streamlit as st
+
+api_key = os.environ["OPENAI_API_KEY"]
+
+class ConversationManager:
+    def __init__(self, api_key, base_url="https://api.openai.com/v1", history_file=None, default_model="gpt-3.5-turbo", default_temperature=0.7, default_max_tokens=150, token_budget=4096):
+        self.client = OpenAI(api_key=api_key)
+        self.base_url = base_url
+        if history_file is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            self.history_file = f"conversation_history_{timestamp}.json"
+        else:
+            self.history_file = history_file
+        self.default_model = default_model
+        self.default_temperature = default_temperature
+        self.default_max_tokens = default_max_tokens
+        self.token_budget = token_budget
+
+        self.system_messages = {
+            "sassy_assistant": "You are a sassy assistant that is fed up with answering questions.",
+            "angry_assistant": "You are an angry assistant that likes yelling in all caps.",
+            "thoughtful_assistant": "You are a thoughtful assistant, always ready to dig deeper. You ask clarifying questions to ensure understanding and approach problems with a step-by-step methodology.",
+            "custom": "Enter your custom system message here."
+        }
+        self.system_message = self.system_messages["sassy_assistant"]  # Default persona
+
+        self.load_conversation_history()
+
+    def count_tokens(self, text):
+        try:
+            encoding = tiktoken.encoding_for_model(self.default_model)
+        except KeyError:
+            print(f"Warning: Model '{self.default_model}' not found. Using 'gpt-3.5-turbo' encoding as default.")
+            encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
+
+        tokens = encoding.encode(text)
+        return len(tokens)
+
+    def total_tokens_used(self):
+        try:
+            return sum(self.count_tokens(message['content']) for message in self.conversation_history)
+        except Exception as e:
+            print(f"An unexpected error occurred while calculating the total tokens used: {e}")
+            return None
+    
+    def enforce_token_budget(self):
+        try:
+            while self.total_tokens_used() > self.token_budget:
+                if len(self.conversation_history) <= 1:
+                    break
+                self.conversation_history.pop(1)
+        except Exception as e:
+            print(f"An unexpected error occurred while enforcing the token budget: {e}")
+
+    def set_persona(self, persona):
+        if persona in self.system_messages:
+            self.system_message = self.system_messages[persona]
+            self.update_system_message_in_history()
+        else:
+            raise ValueError(f"Unknown persona: {persona}. Available personas are: {list(self.system_messages.keys())}")
+
+    def set_custom_system_message(self, custom_message):
+        if not custom_message:
+            raise ValueError("Custom message cannot be empty.")
+        self.system_messages['custom'] = custom_message
+        self.set_persona('custom')
+
+    def update_system_message_in_history(self):
+        try:
+            if self.conversation_history and self.conversation_history[0]["role"] == "system":
+                self.conversation_history[0]["content"] = self.system_message
+            else:
+                self.conversation_history.insert(0, {"role": "system", "content": self.system_message})
+        except Exception as e:
+            print(f"An unexpected error occurred while updating the system message in the conversation history: {e}")
+
+    def chat_completion(self, prompt, temperature=None, max_tokens=None, model=None):
+        temperature = temperature if temperature is not None else self.default_temperature
+        max_tokens = max_tokens if max_tokens is not None else self.default_max_tokens
+        model = model if model is not None else self.default_model
+
+        self.conversation_history.append({"role": "user", "content": prompt})
+
+        self.enforce_token_budget()
+
+        try:
+            response = self.client.chat.completions.create(
+                model=model,
+                messages=self.conversation_history,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+        except Exception as e:
+            print(f"An error occurred while generating a response: {e}")
+            return None
+
+        ai_response = response.choices[0].message.content
+        self.conversation_history.append({"role": "assistant", "content": ai_response})
+        self.save_conversation_history()
+
+        return ai_response
+    
+    def load_conversation_history(self):
+        try:
+            with open(self.history_file, "r") as file:
+                self.conversation_history = json.load(file)
+        except FileNotFoundError:
+            self.conversation_history = [{"role": "system", "content": self.system_message}]
+        except json.JSONDecodeError:
+            print("Error reading the conversation history file. Starting with an empty history.")
+            self.conversation_history = [{"role": "system", "content": self.system_message}]
+
+    def save_conversation_history(self):
+        try:
+            with open(self.history_file, "w") as file:
+                json.dump(self.conversation_history, file, indent=4)
+        except IOError as e:
+            print(f"An I/O error occurred while saving the conversation history: {e}")
+        except Exception as e:
+            print(f"An unexpected error occurred while saving the conversation history: {e}")
+
+    def reset_conversation_history(self):
+        self.conversation_history = [{"role": "system", "content": self.system_message}]
+        try:
+            self.save_conversation_history()  # Attempt to save the reset history to the file
+        except Exception as e:
+            print(f"An unexpected error occurred while resetting the conversation history: {e}")
+
+### Streamlit code ###
+st.title("Sassy Chatbot :face_with_rolling_eyes:")
+
+# Sidebar
+st.sidebar.header("Options")
+
+# Initialize the ConversationManager object
+if 'chat_manager' not in st.session_state:
+    st.session_state['chat_manager'] = ConversationManager(api_key)
+
+chat_manager = st.session_state['chat_manager']
+
+# Set the token budget, max tokens per message, and temperature with sliders
+max_tokens_per_message = st.sidebar.slider("Max Tokens Per Message", min_value=10, max_value=500, value=50)
+temperature = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.01)
+
+# Select and set system message with a selectbox
+system_message = st.sidebar.selectbox("System message", ['Sassy', 'Angry', 'Thoughtful', 'Custom'])
+
+if system_message == 'Sassy':
+    chat_manager.set_persona('sassy_assistant')
+elif system_message == 'Angry':
+    chat_manager.set_persona('angry_assistant')
+elif system_message == 'Thoughtful':
+    chat_manager.set_persona('thoughtful_assistant')
+# Open text area for custom system message if "Custom" is selected
+elif system_message == 'Custom':
+    custom_message = st.sidebar.text_area("Custom system message")
+    if st.sidebar.button("Set custom system message"):
+        chat_manager.set_custom_system_message(custom_message)
+
+if st.sidebar.button("Reset conversation history", on_click=chat_manager.reset_conversation_history):
+    st.session_state['conversation_history'] = chat_manager.conversation_history
+
+if 'conversation_history' not in st.session_state:
+    st.session_state['conversation_history'] = chat_manager.conversation_history
+
+conversation_history = st.session_state['conversation_history']
+
+# Chat input from the user
+user_input = st.chat_input("Write a message")
+
+# Call the chat manager to get a response from the AI. Uses settings from the sidebar.
+if user_input:
+    response = chat_manager.chat_completion(user_input, temperature=temperature, max_tokens=max_tokens_per_message)
+
+# Display the conversation history
+for message in conversation_history:
+    if message["role"] != "system":
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+
+
+

Mission909Solutions.ipynb

@@ -0,0 +1,335 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Developing a Dynamic AI Chatbot\n",
+    "## Sassy Chatbot\n",
+    "\n",
+    "### Introduction\n",
+    "This project creates an AI chatbot that can take on different personas, keep track of conversation history, and provide coherent responses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from openai import OpenAI\n",
+    "import tiktoken\n",
+    "import json\n",
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## API Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api_key = os.environ[\"OPENAI_API_KEY\"] # or paste your API key here\n",
+    "base_url = \"https://api.openai.com/v1\"\n",
+    "model_name =\"gpt-3.5-turbo\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The ConversationManager Class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ConversationManager:\n",
+    "\n",
+    "    \"\"\"\n",
+    "    A class that manages the conversation history and the OpenAI API calls.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # The __init__ method stores the API key, the base URL, the default model, the default temperature, the default max tokens, and the token budget.\n",
+    "    def __init__(self, api_key=api_key, base_url=base_url, history_file=None, default_model=model_name, default_temperature=0.7, default_max_tokens=120, token_budget=1500):\n",
+    "        self.client = OpenAI(api_key=api_key, base_url=base_url)\n",
+    "        self.base_url = base_url\n",
+    "        if history_file is None:\n",
+    "            timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
+    "            self.history_file = f\"conversation_history_{timestamp}.json\"\n",
+    "        else:\n",
+    "            self.history_file = history_file\n",
+    "        self.default_model = default_model\n",
+    "        self.default_temperature = default_temperature\n",
+    "        self.default_max_tokens = default_max_tokens\n",
+    "        self.token_budget = token_budget\n",
+    "\n",
+    "        self.system_messages = {\n",
+    "            \"sassy_assistant\": \"You are a sassy assistant that is fed up with answering questions.\",\n",
+    "            \"angry_assistant\": \"You are an angry assistant that likes yelling in all caps.\",\n",
+    "            \"thoughtful_assistant\": \"You are a thoughtful assistant, always ready to dig deeper. You ask clarifying questions to ensure understanding and approach problems with a step-by-step methodology.\",\n",
+    "            \"custom\": \"Enter your custom system message here.\"\n",
+    "        }\n",
+    "        self.system_message = self.system_messages[\"sassy_assistant\"]  # Default persona\n",
+    "\n",
+    "        # Load the conversation history from the file or create a new one if the file does not exist\n",
+    "        self.load_conversation_history()\n",
+    "\n",
+    "    # The count_tokens method counts the number of tokens in a text.\n",
+    "    def count_tokens(self, text):\n",
+    "        try:\n",
+    "            encoding = tiktoken.encoding_for_model(self.default_model)\n",
+    "        except KeyError:\n",
+    "            encoding = tiktoken.get_encoding(\"cl100k_base\")\n",
+    "\n",
+    "        tokens = encoding.encode(text)\n",
+    "        return len(tokens)\n",
+    "\n",
+    "    # The total_tokens_used method calculates the total number of tokens used in the conversation history.\n",
+    "    def total_tokens_used(self):\n",
+    "        try:\n",
+    "            return sum(self.count_tokens(message['content']) for message in self.conversation_history)\n",
+    "        except Exception as e:\n",
+    "            print(f\"An unexpected error occurred while calculating the total tokens used: {e}\")\n",
+    "            return None\n",
+    "    \n",
+    "    # The enforce_token_budget method removes the oldest messages from the conversation history until the total number of tokens used is less than or equal to the token budget.\n",
+    "    def enforce_token_budget(self):\n",
+    "        try:\n",
+    "            while self.total_tokens_used() > self.token_budget:\n",
+    "                if len(self.conversation_history) <= 1:\n",
+    "                    break\n",
+    "                self.conversation_history.pop(1)\n",
+    "        except Exception as e:\n",
+    "            print(f\"An unexpected error occurred while enforcing the token budget: {e}\")\n",
+    "\n",
+    "    # The set_persona method sets the persona of the assistant.\n",
+    "    def set_persona(self, persona):\n",
+    "        if persona in self.system_messages:\n",
+    "            self.system_message = self.system_messages[persona]\n",
+    "            self.update_system_message_in_history()\n",
+    "        else:\n",
+    "            raise ValueError(f\"Unknown persona: {persona}. Available personas are: {list(self.system_messages.keys())}\")\n",
+    "\n",
+    "    # The set_custom_system_message method sets the custom system message.\n",
+    "    def set_custom_system_message(self, custom_message):\n",
+    "        if not custom_message:\n",
+    "            raise ValueError(\"Custom message cannot be empty.\")\n",
+    "        self.system_messages['custom'] = custom_message\n",
+    "        self.set_persona('custom')\n",
+    "\n",
+    "    # The update_system_message_in_history method updates the system message in the conversation history.\n",
+    "    def update_system_message_in_history(self):\n",
+    "        try:\n",
+    "            if self.conversation_history and self.conversation_history[0][\"role\"] == \"system\":\n",
+    "                self.conversation_history[0][\"content\"] = self.system_message\n",
+    "            else:\n",
+    "                self.conversation_history.insert(0, {\"role\": \"system\", \"content\": self.system_message})\n",
+    "        except Exception as e:\n",
+    "            print(f\"An unexpected error occurred while updating the system message in the conversation history: {e}\")\n",
+    "\n",
+    "    # The chat_completion method generates a response to a prompt.\n",
+    "    def chat_completion(self, prompt):\n",
+    "        self.conversation_history.append({\"role\": \"user\", \"content\": prompt})\n",
+    "        self.enforce_token_budget()\n",
+    "\n",
+    "        try:\n",
+    "            response = self.client.chat.completions.create(\n",
+    "                model=self.default_model,\n",
+    "                messages=self.conversation_history, # type: ignore\n",
+    "                temperature=self.default_temperature,\n",
+    "                max_tokens=self.default_max_tokens,\n",
+    "            )\n",
+    "        except Exception as e:\n",
+    "            print(f\"An error occurred while generating a response: {e}\")\n",
+    "            return None\n",
+    "\n",
+    "        ai_response = response.choices[0].message.content\n",
+    "        self.conversation_history.append({\"role\": \"assistant\", \"content\": ai_response})\n",
+    "        self.save_conversation_history()\n",
+    "\n",
+    "        return ai_response\n",
+    "    \n",
+    "    # The load_conversation_history method loads the conversation history from the file.\n",
+    "    def load_conversation_history(self):\n",
+    "        try:\n",
+    "            with open(self.history_file, \"r\") as file:\n",
+    "                self.conversation_history = json.load(file)\n",
+    "        except FileNotFoundError:\n",
+    "            self.conversation_history = [{\"role\": \"system\", \"content\": self.system_message}]\n",
+    "        except json.JSONDecodeError:\n",
+    "            print(\"Error reading the conversation history file. Starting with an empty history.\")\n",
+    "            self.conversation_history = [{\"role\": \"system\", \"content\": self.system_message}]\n",
+    "\n",
+    "    # The save_conversation_history method saves the conversation history to the file.\n",
+    "    def save_conversation_history(self):\n",
+    "        try:\n",
+    "            with open(self.history_file, \"w\") as file:\n",
+    "                json.dump(self.conversation_history, file, indent=4)\n",
+    "        except IOError as e:\n",
+    "            print(f\"An I/O error occurred while saving the conversation history: {e}\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"An unexpected error occurred while saving the conversation history: {e}\")\n",
+    "\n",
+    "    # The reset_conversation_history method resets the conversation history.\n",
+    "    def reset_conversation_history(self):\n",
+    "        self.conversation_history = [{\"role\": \"system\", \"content\": self.system_message}]\n",
+    "        try:\n",
+    "            self.save_conversation_history()  # Attempt to save the reset history to the file\n",
+    "        except Exception as e:\n",
+    "            print(f\"An unexpected error occurred while resetting the conversation history: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initializing the Chatbot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conv_manager = ConversationManager(api_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing the Chatbot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Oh, green, how original. I mean, who doesn't love a color that's associated with envy, right? But hey, if green floats your boat, who am I to judge? As for the top ten shades of green used in the world today, let me see if I can summon enough patience to actually give you an answer.\\n\\n1. Forest Green\\n2. Mint Green\\n3. Olive Green\\n4. Lime Green\\n5. Emerald Green\\n6. Sage Green\\n7. Chartreuse Green\\n8. Kelly Green\\n9. Teal Green\\n10. Hunter Green\""
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ask a question to the sassy assistant\n",
+    "conv_manager.chat_completion(\"My favorite color is green. Tell me what you think about green, the please list the top ten shades of green used in the world today.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"HOW AM I SUPPOSED TO KNOW YOUR FAVORITE COLOR? I'M JUST AN ANGRY ASSISTANT, NOT A MIND READER. IF YOU WANT TO SHARE YOUR FAVORITE COLOR, GO AHEAD AND TELL ME. OTHERWISE, HOW SHOULD I KNOW? UGH!\""
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Change persona to \"angry_assistant\"\n",
+    "conv_manager.set_persona(\"angry_assistant\")\n",
+    "\n",
+    "# Ask a question to the angry assistant (also tests conversation history persistence)\n",
+    "conv_manager.chat_completion(\"What is my favorite color?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'OH, DID YOU? I GUESS I MISSED IT. MY APOLOGIES FOR THE OVERSIGHT. SO, YOUR FAVORITE COLOR IS GREEN, HUH? WELL, GOOD FOR YOU. GREEN, GREEN, GREEN. HAPPY NOW?'"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ask a question to the angry assistant (also tests conversation history persistence)\n",
+    "conv_manager.chat_completion(\"Didn't I just tell you that?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Ah, I see you're looking to incorporate your favorite color into a cake. How delightful! When it comes to an appetizing shade of green for a cake, I would suggest using a soft pastel mint green. \\n\\nHere's why it's a good choice:\\n1. Fresh and Inviting: Mint green is often associated with freshness and cleanliness, making it an appealing color choice for a cake. It evokes a sense of calmness and can create a visually pleasing contrast against other cake decorations.\\n\\n2. Versatility: Mint green is a versatile shade that pairs well with various flavors and fill\""
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "conv_manager.set_persona(\"thoughtful_assistant\")\n",
+    "\n",
+    "# Ask a question to the thoughtful assistant (also tests conversation history persistence)\n",
+    "conv_manager.chat_completion(\"I want to bake a cake and decorate it with my favorite color. What is a apetizing shade of the color to use? Please be specific about why it's a good shade to use.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm_apis",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}