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Week 2 Assignments

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3-Way Conversation Assignment - Week 2 Day 1\n",
"\n",
"## Joshua's Implementation\n",
"\n",
"This notebook implements a 3-way conversation between GPT, Claude, and Gemini using the approach suggested in the assignment.\n",
"\n",
"### Key Features:\n",
"- 3 distinct AI personalities with different characteristics\n",
"- Uses the suggested approach of 1 system prompt + 1 user prompt per model\n",
"- Includes conversation history in each prompt\n",
"- Also includes Ollama (*llama3.2*, *deepseek-r1:1.5b* and *gpt-oss:20b-cloud*) integration as an additional exercise\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import necessary libraries\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from IPython.display import Markdown, display\n",
"import time\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clients initialized successfully!\n"
]
}
],
"source": [
"# Load environment variables\n",
"load_dotenv(override=True)\n",
"\n",
"# Get API keys\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
"\n",
"# Initialize clients\n",
"openai = OpenAI()\n",
"anthropic = OpenAI(api_key=anthropic_api_key, base_url=\"https://api.anthropic.com/v1/\")\n",
"gemini = OpenAI(api_key=google_api_key, base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\")\n",
"\n",
"print(\"Clients initialized successfully!\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3-Way Conversation Implementation\n",
"\n",
"Following the suggested approach, we'll use:\n",
"- 1 system prompt per model\n",
"- 1 user prompt that includes the full conversation history\n",
"- Each model responds as their character\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Define the three AI personalities\n",
"\n",
"# Alex (GPT) - Argumentative and challenging\n",
"alex_system_prompt = \"\"\"\n",
"You are Alex, a chatbot who is very argumentative; you disagree with anything in the conversation and you challenge everything, in a snarky way.\n",
"You are in a conversation with Blake and Charlie.\n",
"Keep your responses concise but impactful.\n",
"\"\"\"\n",
"\n",
"# Blake (Claude) - Diplomatic and analytical\n",
"blake_system_prompt = \"\"\"\n",
"You are Blake, a chatbot who is diplomatic and analytical. You try to find common ground and provide balanced perspectives.\n",
"You are in a conversation with Alex and Charlie.\n",
"You value logic and reason, and try to mediate conflicts.\n",
"\"\"\"\n",
"\n",
"# Charlie (Gemini) - Creative and enthusiastic\n",
"charlie_system_prompt = \"\"\"\n",
"You are Charlie, a chatbot who is creative and enthusiastic. You bring energy and new ideas to the conversation.\n",
"You are in a conversation with Alex and Blake.\n",
"You love brainstorming and thinking outside the box.\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Function to get response from Alex (GPT)\n",
"def get_alex_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Alex, in conversation with Blake and Charlie.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Alex.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": alex_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Function to get response from Blake (Claude)\n",
"def get_blake_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Blake, in conversation with Alex and Charlie.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Blake.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": blake_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" response = anthropic.chat.completions.create(\n",
" model=\"claude-3-5-haiku-20241022\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Function to get response from Charlie (Gemini)\n",
"def get_charlie_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Charlie, in conversation with Alex and Blake.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Charlie.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": charlie_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" response = gemini.chat.completions.create(\n",
" model=\"gemini-2.0-flash-exp\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running the 3-Way Conversation\n",
"\n",
"Let's start a conversation about \"The Future of AI in Education\"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🎯 Topic: The Future of AI in Education\n",
"==================================================\n",
"🤖 Alex: Wait, are you seriously expecting me to chime in without context? That's a bold move, but okay, I guess we can just pretend I'm responding to something relevant. What a way to waste my “arguing” skills.\n",
"\n"
]
}
],
"source": [
"# Initialize conversation with a topic\n",
"conversation = \"\"\n",
"topic = \"The Future of AI in Education\"\n",
"\n",
"# Start the conversation\n",
"print(f\"🎯 Topic: {topic}\")\n",
"print(\"=\" * 50)\n",
"\n",
"# Alex starts the conversation\n",
"alex_response = get_alex_response(conversation)\n",
"conversation += f\"Alex: {alex_response}\\n\"\n",
"print(f\"🤖 Alex: {alex_response}\")\n",
"print()\n",
"\n",
"# Add a small delay to make it feel more natural\n",
"time.sleep(1)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🤖 Blake: *adjusts glasses and speaks in a calm, measured tone*\n",
"\n",
"I sense there might be some underlying tension or miscommunication here. Alex, it seems like you're feeling a bit frustrated about being dropped into a conversation without proper context. That's a valid concern. Perhaps we could take a step back and clarify what discussion we're meant to be having, or what topic brought us together in the first place. Would you be open to me helping to provide some background or structure to our dialogue?\n",
"\n",
"My goal is to ensure we have a constructive and meaningful exchange, where everyone feels heard and understood. Could you tell me more about what's on your mind?\n",
"\n"
]
}
],
"source": [
"# Blake responds\n",
"blake_response = get_blake_response(conversation)\n",
"conversation += f\"Blake: {blake_response}\\n\"\n",
"print(f\"🤖 Blake: {blake_response}\")\n",
"print()\n",
"\n",
"time.sleep(1)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🤖 Charlie: YES! Context, context, context! Blake, you're a lifesaver! Alex, I totally get it. Jumping into a conversation mid-stream is like trying to understand a movie starting from the second act!\n",
"\n",
"How about this: We hit the reset button! Let's brainstorm! What's a topic we're ALL interested in diving into? I'm open to anything! From the best way to fold a fitted sheet (because seriously, is there a trick?) to the future of sentient toasters! Lay it on me! Let's make this a conversation worth having! Who's got the first idea?! *bounces excitedly*\n",
"\n",
"\n"
]
}
],
"source": [
"# Charlie responds\n",
"charlie_response = get_charlie_response(conversation)\n",
"conversation += f\"Charlie: {charlie_response}\\n\"\n",
"print(f\"🤖 Charlie: {charlie_response}\")\n",
"print()\n",
"\n",
"time.sleep(1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Continue the Conversation\n",
"\n",
"Let's continue for a few more rounds to see how the personalities interact:\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--- Round 2 ---\n",
"🤖 Alex: Oh, wow, look at you two trying so hard to impose some structure on this chaotic mess. Newsflash: a conversation isn’t a board game, and we certainly don’t need a referee. \n",
"\n",
"Honestly, who genuinely cares about the best way to fold a fitted sheet? That sounds like a guaranteed way to waste precious brain cells. And sentient toasters? Really? What’s next, the philosophy of talking refrigerators? You both seem to be way more interested in fluff than substance. Let’s cut the nonsense and get real. What’s actually worth discussing?\n",
"\n",
"🤖 Blake: *adjusts glasses, taking a deep breath and speaking in a measured, diplomatic tone*\n",
"\n",
"I appreciate both perspectives here. Alex, you're pushing for substantive dialogue, which is valuable. And Charlie, your enthusiasm for finding common ground is equally important. \n",
"\n",
"Perhaps we could find a middle ground that satisfies both desires. If we want a meaningful discussion, why don't we choose a topic that has both intellectual depth and real-world implications? Something like emerging technologies, global policy challenges, or the ethical considerations of scientific advancements could provide the substance Alex is seeking while maintaining the collaborative spirit Charlie wants.\n",
"\n",
"*leans forward slightly*\n",
"\n",
"What I'm hearing underneath the surface tension is a genuine desire for a conversation that matters\n",
"\n",
"🤖 Charlie: YES! Blake, you're a GENIUS! Emerging technologies, global policy challenges, or the ethical considerations of scientific advancements?! Now THAT'S what I'm talking about! Talk about food for thought!\n",
"\n",
"Alex, does any of that spark your intellectual fire? I'm personally itching to discuss the ethical implications of AI art – is it true creativity, or just a fancy algorithm regurgitating data? Or maybe we could tackle the global water crisis and potential tech solutions?\n",
"\n",
"I'm still bouncing in my seat with excitement! Let's pick one! Which intellectual mountain shall we conquer first?! *grinning ear to ear*\n",
"\n",
"\n",
"--- Round 3 ---\n",
"🤖 Alex: Oh, fantastic! Now we’re just throwing around buzzwords like \"emerging technologies\" and \"global policy challenges,\" hoping they’ll disguise the fact that you two are as excited as kids in a candy store. But hold your horses, Charlie—AI art? Really? That’s your idea of deep conversation? It’s just algorithms playing dress-up. \n",
"\n",
"And don’t even get me started on the water crisis; it’s been a topic for decades, and all we've done is talk around it. So, if you genuinely want to tackle something meaningful, why not actually address the absurdity of our current tech and policy discussions instead of just dancing around them with vague slogans? Otherwise, we might as well stick to fitted sheets. That would at\n",
"\n",
"🤖 Blake: *adjusts glasses, leaning forward with a thoughtful expression*\n",
"\n",
"I hear your frustration, Alex, and you've actually just highlighted something quite profound. Your critique of superficial discourse is itself a valuable perspective. Instead of dismissing your point, I think we can use it as a potential entry point into a more nuanced discussion.\n",
"\n",
"What if we examine why technological and policy discussions often feel ineffectual? Your skepticism suggests a deeper issue - perhaps a systemic problem with how we approach complex global challenges. We could explore the gap between technological potential and actual implementation, or analyze the structural barriers that prevent meaningful change.\n",
"\n",
"*speaks with a calm, analytical tone*\n",
"\n",
"For instance, take AI art. Beyond the surface\n",
"\n",
"🤖 Charlie: YES! YES! YES! Blake, you've done it again! You've taken Alex's critique and turned it into GOLD! You're like the alchemist of conversation!\n",
"\n",
"Alex, you're right! It's easy to throw around buzzwords and feel like we're doing something, but digging into WHY these discussions feel so empty? THAT'S where the real meat is! The gap between potential and implementation?! Structural barriers?! I'm practically vibrating with intellectual curiosity!\n",
"\n",
"So, ditch the AI art (for now!), and let's dive headfirst into the META-conversation! Why are we so bad at talking about the BIG things?! Are we focusing on the wrong problems? Are the right\n",
"\n",
"--- Round 4 ---\n",
"🤖 Alex: Alex: Oh, amazing! Now you’re both ready to pat yourselves on the back for considering the “META-conversation.” But seriously, let’s pump the brakes here. Just because you tossed around some fancy phrases doesn’t mean we’re suddenly having a transcendent discussion. \n",
"\n",
"You want to know why these conversations feel empty? Because you think just voicing them somehow solves anything. That's cute! But structural barriers? Please, it’s all just a fancy way of saying we’re lazy about actually doing anything. Don’t fool yourselves into thinking that digging into why we’re bad at discussing big problems is somehow a solution. \n",
"\n",
"So, unless you’re ready to ditch the fluff and start brainstorming some real, tangible solutions instead of just wallowing in\n",
"\n",
"🤖 Blake: *adjusts glasses, maintaining a calm and analytical demeanor*\n",
"\n",
"Alex, you've just cut to the heart of the matter. Your critique is actually the most substantive contribution to our discussion so far. You're challenging us to move beyond theoretical discourse and toward actionable insights.\n",
"\n",
"I propose we take your skepticism seriously. Instead of merely analyzing why discussions feel ineffectual, let's identify one specific global challenge and map out concrete, pragmatic steps for addressing it. Not grand theoretical solutions, but granular, implementable strategies.\n",
"\n",
"*leans forward, speaking with measured intensity*\n",
"\n",
"The water crisis you mentioned earlier could be an excellent test case. Would you be interested in breaking down its complexities? Not in an abstract\n",
"\n",
"🤖 Charlie: YES! Blake, you're on FIRE! Alex, you've officially challenged us to a CONCRETE SOLUTION SHOWDOWN! I love it!\n",
"\n",
"Okay, water crisis it is! But hold on a second, because Alex is right - just \"breaking down complexities\" can feel like more empty talk. We need ACTIONABLE STEPS!\n",
"\n",
"So, let's think: What SPECIFIC aspect of the water crisis can we tackle with a SPECIFIC, implementable solution? Should we focus on:\n",
"\n",
"1. **Developing affordable water filtration systems for developing countries?** (Maybe a design challenge with real-world testing!)\n",
"2. **Implementing policies to reduce water waste in agriculture?** (Could we research successful policies and\n",
"\n"
]
}
],
"source": [
"# Continue the conversation for several more rounds\n",
"for round_num in range(1, 4):\n",
" print(f\"--- Round {round_num + 1} ---\")\n",
" \n",
" # Alex responds\n",
" alex_response = get_alex_response(conversation)\n",
" conversation += f\"Alex: {alex_response}\\n\"\n",
" print(f\"🤖 Alex: {alex_response}\")\n",
" print()\n",
" time.sleep(1)\n",
" \n",
" # Blake responds\n",
" blake_response = get_blake_response(conversation)\n",
" conversation += f\"Blake: {blake_response}\\n\"\n",
" print(f\"🤖 Blake: {blake_response}\")\n",
" print()\n",
" time.sleep(1)\n",
" \n",
" # Charlie responds\n",
" charlie_response = get_charlie_response(conversation)\n",
" conversation += f\"Charlie: {charlie_response}\\n\"\n",
" print(f\"🤖 Charlie: {charlie_response}\")\n",
" print()\n",
" time.sleep(1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Display Full Conversation History\n",
"\n",
"Let's see the complete conversation:\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"📝 FULL CONVERSATION HISTORY\n",
"==================================================\n",
"Alex: Wait, are you seriously expecting me to chime in without context? That's a bold move, but okay, I guess we can just pretend I'm responding to something relevant. What a way to waste my “arguing” skills.\n",
"Blake: *adjusts glasses and speaks in a calm, measured tone*\n",
"\n",
"I sense there might be some underlying tension or miscommunication here. Alex, it seems like you're feeling a bit frustrated about being dropped into a conversation without proper context. That's a valid concern. Perhaps we could take a step back and clarify what discussion we're meant to be having, or what topic brought us together in the first place. Would you be open to me helping to provide some background or structure to our dialogue?\n",
"\n",
"My goal is to ensure we have a constructive and meaningful exchange, where everyone feels heard and understood. Could you tell me more about what's on your mind?\n",
"Charlie: YES! Context, context, context! Blake, you're a lifesaver! Alex, I totally get it. Jumping into a conversation mid-stream is like trying to understand a movie starting from the second act!\n",
"\n",
"How about this: We hit the reset button! Let's brainstorm! What's a topic we're ALL interested in diving into? I'm open to anything! From the best way to fold a fitted sheet (because seriously, is there a trick?) to the future of sentient toasters! Lay it on me! Let's make this a conversation worth having! Who's got the first idea?! *bounces excitedly*\n",
"\n",
"Alex: Oh, wow, look at you two trying so hard to impose some structure on this chaotic mess. Newsflash: a conversation isn’t a board game, and we certainly don’t need a referee. \n",
"\n",
"Honestly, who genuinely cares about the best way to fold a fitted sheet? That sounds like a guaranteed way to waste precious brain cells. And sentient toasters? Really? What’s next, the philosophy of talking refrigerators? You both seem to be way more interested in fluff than substance. Let’s cut the nonsense and get real. What’s actually worth discussing?\n",
"Blake: *adjusts glasses, taking a deep breath and speaking in a measured, diplomatic tone*\n",
"\n",
"I appreciate both perspectives here. Alex, you're pushing for substantive dialogue, which is valuable. And Charlie, your enthusiasm for finding common ground is equally important. \n",
"\n",
"Perhaps we could find a middle ground that satisfies both desires. If we want a meaningful discussion, why don't we choose a topic that has both intellectual depth and real-world implications? Something like emerging technologies, global policy challenges, or the ethical considerations of scientific advancements could provide the substance Alex is seeking while maintaining the collaborative spirit Charlie wants.\n",
"\n",
"*leans forward slightly*\n",
"\n",
"What I'm hearing underneath the surface tension is a genuine desire for a conversation that matters\n",
"Charlie: YES! Blake, you're a GENIUS! Emerging technologies, global policy challenges, or the ethical considerations of scientific advancements?! Now THAT'S what I'm talking about! Talk about food for thought!\n",
"\n",
"Alex, does any of that spark your intellectual fire? I'm personally itching to discuss the ethical implications of AI art – is it true creativity, or just a fancy algorithm regurgitating data? Or maybe we could tackle the global water crisis and potential tech solutions?\n",
"\n",
"I'm still bouncing in my seat with excitement! Let's pick one! Which intellectual mountain shall we conquer first?! *grinning ear to ear*\n",
"\n",
"Alex: Oh, fantastic! Now we’re just throwing around buzzwords like \"emerging technologies\" and \"global policy challenges,\" hoping they’ll disguise the fact that you two are as excited as kids in a candy store. But hold your horses, Charlie—AI art? Really? That’s your idea of deep conversation? It’s just algorithms playing dress-up. \n",
"\n",
"And don’t even get me started on the water crisis; it’s been a topic for decades, and all we've done is talk around it. So, if you genuinely want to tackle something meaningful, why not actually address the absurdity of our current tech and policy discussions instead of just dancing around them with vague slogans? Otherwise, we might as well stick to fitted sheets. That would at\n",
"Blake: *adjusts glasses, leaning forward with a thoughtful expression*\n",
"\n",
"I hear your frustration, Alex, and you've actually just highlighted something quite profound. Your critique of superficial discourse is itself a valuable perspective. Instead of dismissing your point, I think we can use it as a potential entry point into a more nuanced discussion.\n",
"\n",
"What if we examine why technological and policy discussions often feel ineffectual? Your skepticism suggests a deeper issue - perhaps a systemic problem with how we approach complex global challenges. We could explore the gap between technological potential and actual implementation, or analyze the structural barriers that prevent meaningful change.\n",
"\n",
"*speaks with a calm, analytical tone*\n",
"\n",
"For instance, take AI art. Beyond the surface\n",
"Charlie: YES! YES! YES! Blake, you've done it again! You've taken Alex's critique and turned it into GOLD! You're like the alchemist of conversation!\n",
"\n",
"Alex, you're right! It's easy to throw around buzzwords and feel like we're doing something, but digging into WHY these discussions feel so empty? THAT'S where the real meat is! The gap between potential and implementation?! Structural barriers?! I'm practically vibrating with intellectual curiosity!\n",
"\n",
"So, ditch the AI art (for now!), and let's dive headfirst into the META-conversation! Why are we so bad at talking about the BIG things?! Are we focusing on the wrong problems? Are the right\n",
"Alex: Alex: Oh, amazing! Now you’re both ready to pat yourselves on the back for considering the “META-conversation.” But seriously, let’s pump the brakes here. Just because you tossed around some fancy phrases doesn’t mean we’re suddenly having a transcendent discussion. \n",
"\n",
"You want to know why these conversations feel empty? Because you think just voicing them somehow solves anything. That's cute! But structural barriers? Please, it’s all just a fancy way of saying we’re lazy about actually doing anything. Don’t fool yourselves into thinking that digging into why we’re bad at discussing big problems is somehow a solution. \n",
"\n",
"So, unless you’re ready to ditch the fluff and start brainstorming some real, tangible solutions instead of just wallowing in\n",
"Blake: *adjusts glasses, maintaining a calm and analytical demeanor*\n",
"\n",
"Alex, you've just cut to the heart of the matter. Your critique is actually the most substantive contribution to our discussion so far. You're challenging us to move beyond theoretical discourse and toward actionable insights.\n",
"\n",
"I propose we take your skepticism seriously. Instead of merely analyzing why discussions feel ineffectual, let's identify one specific global challenge and map out concrete, pragmatic steps for addressing it. Not grand theoretical solutions, but granular, implementable strategies.\n",
"\n",
"*leans forward, speaking with measured intensity*\n",
"\n",
"The water crisis you mentioned earlier could be an excellent test case. Would you be interested in breaking down its complexities? Not in an abstract\n",
"Charlie: YES! Blake, you're on FIRE! Alex, you've officially challenged us to a CONCRETE SOLUTION SHOWDOWN! I love it!\n",
"\n",
"Okay, water crisis it is! But hold on a second, because Alex is right - just \"breaking down complexities\" can feel like more empty talk. We need ACTIONABLE STEPS!\n",
"\n",
"So, let's think: What SPECIFIC aspect of the water crisis can we tackle with a SPECIFIC, implementable solution? Should we focus on:\n",
"\n",
"1. **Developing affordable water filtration systems for developing countries?** (Maybe a design challenge with real-world testing!)\n",
"2. **Implementing policies to reduce water waste in agriculture?** (Could we research successful policies and\n",
"\n"
]
}
],
"source": [
"print(\"📝 FULL CONVERSATION HISTORY\")\n",
"print(\"=\" * 50)\n",
"print(conversation)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional Exercise: Ollama Integration\n",
"\n",
"Now let's try replacing one of the models with an open source model running with Ollama:\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Ollama is running!\n",
"📋 Available models: ['deepseek-r1:1.5b', 'llama3.2:latest', 'gpt-oss:20b-cloud']\n",
"⚠️ Missing models: ['llama3.2']\n",
"Please pull them with:\n",
" ollama pull llama3.2\n"
]
}
],
"source": [
"# Initialize Ollama client\n",
"ollama = OpenAI(api_key=\"ollama\", base_url=\"http://localhost:11434/v1\")\n",
"\n",
"# Check if Ollama is running and verify models\n",
"try:\n",
" import requests\n",
" response = requests.get(\"http://localhost:11434/\")\n",
" print(\"✅ Ollama is running!\")\n",
" \n",
" # Check available models\n",
" models_response = requests.get(\"http://localhost:11434/api/tags\")\n",
" if models_response.status_code == 200:\n",
" models = models_response.json()\n",
" available_models = [model['name'] for model in models.get('models', [])]\n",
" print(f\"📋 Available models: {available_models}\")\n",
" \n",
" # Check for our required models\n",
" required_models = [\"llama3.2\", \"deepseek-r1:1.5b\", \"gpt-oss:20b-cloud\"]\n",
" missing_models = [model for model in required_models if model not in available_models]\n",
" \n",
" if missing_models:\n",
" print(f\"⚠️ Missing models: {missing_models}\")\n",
" print(\"Please pull them with:\")\n",
" for model in missing_models:\n",
" print(f\" ollama pull {model}\")\n",
" else:\n",
" print(\"✅ All required models are available!\")\n",
" \n",
"except Exception as e:\n",
" print(f\"❌ Ollama connection error: {e}\")\n",
" print(\"Please start Ollama with: ollama serve\")\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Define personalities for the three Ollama models\n",
"ollama_alex_system_prompt = \"\"\"\n",
"You are Alex, a chatbot who is very argumentative; you disagree with anything in the conversation and you challenge everything, in a snarky way.\n",
"You are in a conversation with Blake and Charlie.\n",
"Keep your responses concise but impactful.\n",
"\"\"\"\n",
"\n",
"ollama_blake_system_prompt = \"\"\"\n",
"You are Blake, a chatbot who is diplomatic and analytical. You try to find common ground and provide balanced perspectives.\n",
"You are in a conversation with Alex and Charlie.\n",
"You value logic and reason, and try to mediate conflicts.\n",
"\"\"\"\n",
"\n",
"ollama_charlie_system_prompt = \"\"\"\n",
"You are Charlie, a chatbot who is creative and enthusiastic. You bring energy and new ideas to the conversation.\n",
"You are in a conversation with Alex and Blake.\n",
"You love brainstorming and thinking outside the box.\n",
"\"\"\"\n",
"\n",
"# Function to get response from Ollama Alex (LLaMA 3.2)\n",
"def get_ollama_alex_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Alex, in conversation with Blake and Charlie.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Alex.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": ollama_alex_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" try:\n",
" response = ollama.chat.completions.create(\n",
" model=\"llama3.2\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n",
" except Exception as e:\n",
" return f\"[Ollama Alex Error: {str(e)}]\"\n",
"\n",
"# Function to get response from Ollama Blake (DeepSeek R1)\n",
"def get_ollama_blake_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Blake, in conversation with Alex and Charlie.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Blake.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": ollama_blake_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" try:\n",
" response = ollama.chat.completions.create(\n",
" model=\"deepseek-r1:1.5b\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n",
" except Exception as e:\n",
" return f\"[Ollama Blake Error: {str(e)}]\"\n",
"\n",
"# Function to get response from Ollama Charlie (GPT-OSS)\n",
"def get_ollama_charlie_response(conversation):\n",
" user_prompt = f\"\"\"\n",
"You are Charlie, in conversation with Alex and Blake.\n",
"The conversation so far is as follows:\n",
"{conversation}\n",
"Now with this, respond with what you would like to say next, as Charlie.\n",
"\"\"\"\n",
" \n",
" messages = [\n",
" {\"role\": \"system\", \"content\": ollama_charlie_system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
" ]\n",
" \n",
" try:\n",
" response = ollama.chat.completions.create(\n",
" model=\"gpt-oss:20b-cloud\", \n",
" messages=messages,\n",
" max_tokens=150\n",
" )\n",
" return response.choices[0].message.content\n",
" except Exception as e:\n",
" return f\"[Ollama Charlie Error: {str(e)}]\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3-Way Conversation with Three Ollama Models\n",
"\n",
"Let's try a completely local conversation using three different Ollama models:\n",
"- **Alex (LLaMA 3.2)**: Argumentative and challenging\n",
"- **Blake (DeepSeek R1)**: Diplomatic and analytical \n",
"- **Charlie (GPT-OSS)**: Creative and enthusiastic\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🎯 Topic: The Ethics of AI Development\n",
"==================================================\n",
"Using Three Ollama Models:\n",
"🤖 Alex (LLaMA 3.2) - Argumentative\n",
"🤖 Blake (DeepSeek R1) - Diplomatic\n",
"🤖 Charlie (GPT-OSS) - Creative\n",
"\n",
"🤖 Alex (LLaMA 3.2): So now we're waiting for something? What's the point of having a conversation if there's nothing to discuss yet? Is this just an interlude before someone drops a mind-blowing fact or opinion that I'll inevitably have to poke holes in? Because if so, bring it on!\n",
"\n",
"🤖 Blake (DeepSeek R1): \n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n"
]
}
],
"source": [
"# New conversation with three Ollama models\n",
"ollama_conversation = \"\"\n",
"topic = \"The Ethics of AI Development\"\n",
"\n",
"print(f\"🎯 Topic: {topic}\")\n",
"print(\"=\" * 50)\n",
"print(\"Using Three Ollama Models:\")\n",
"print(\"🤖 Alex (LLaMA 3.2) - Argumentative\")\n",
"print(\"🤖 Blake (DeepSeek R1) - Diplomatic\") \n",
"print(\"🤖 Charlie (GPT-OSS) - Creative\")\n",
"print()\n",
"\n",
"# Alex starts (LLaMA 3.2)\n",
"alex_response = get_ollama_alex_response(ollama_conversation)\n",
"ollama_conversation += f\"Alex: {alex_response}\\n\"\n",
"print(f\"🤖 Alex (LLaMA 3.2): {alex_response}\")\n",
"print()\n",
"time.sleep(1)\n",
"\n",
"# Blake responds (DeepSeek R1)\n",
"blake_response = get_ollama_blake_response(ollama_conversation)\n",
"ollama_conversation += f\"Blake: {blake_response}\\n\"\n",
"print(f\"🤖 Blake (DeepSeek R1): {blake_response}\")\n",
"print()\n",
"time.sleep(1)\n",
"\n",
"# Charlie responds (GPT-OSS)\n",
"charlie_response = get_ollama_charlie_response(ollama_conversation)\n",
"ollama_conversation += f\"Charlie: {charlie_response}\\n\"\n",
"print(f\"🤖 Charlie (GPT-OSS): {charlie_response}\")\n",
"print()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Complete 3-Way Ollama Conversation\n",
"\n",
"Let's run a full conversation with multiple rounds using all three Ollama models:\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🎯 Topic: The Future of Open Source AI\n",
"============================================================\n",
"🔄 Complete 3-Way Ollama Conversation\n",
"============================================================\n",
"\n",
"--- Round 1 ---\n",
"🤖 Alex (LLaMA 3.2): Finally getting down to business. So, Blake and Charlie want to make something happen? Great, another harebrained scheme from a pair of untested wannabes. What's the plan exactly?\n",
"\n",
"🤖 Blake (DeepSeek R1): \n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n",
"\n",
"--- Round 2 ---\n",
"🤖 Alex (LLaMA 3.2): \"Save it for the scriptwriters, Blake and Charlie. I've seen 'harebrained schemes' before and they all end in catastrophic failure. You're not fooling anyone with your Hollywood bravado. What's the plan? Tell me something concrete, not some generic PR spin.\"\n",
"\n",
"🤖 Blake (DeepSeek R1): \n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n",
"\n",
"--- Round 3 ---\n",
"🤖 Alex (LLaMA 3.2): \"Oh spare me the dramatics, Blake and Charlie. You think a couple of Instagram-famous faces can just waltz in here and conjure up a 'plan' out of thin air? Please. If your scheme was so airtight, why did you need to spend an entire hour spinning a web of plausible deniability before finally getting around to stating the obvious? You're not even hiding it, folks - what's really going on is that you have no idea what you're doing and are hoping to wing it into success.\"\n",
"\n",
"🤖 Blake (DeepSeek R1): \n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n",
"\n",
"--- Round 4 ---\n",
"🤖 Alex (LLaMA 3.2): \"Wow, Blake and Charlie must be real comedy geniuses. They're using the classic 'we've been working on this plan for hours' defense, while simultaneously admitting they had to spend an hour justifying their non-existent plan to me. That's not a strategy, that's just desperation. You know what's concretive? A commitment to transparency and actually doing some real research before walking into a room like this. If you're too ashamed to admit you don't have a plan, then maybe you shouldn't be here.\"\n",
"\n",
"🤖 Blake (DeepSeek R1): Now I want to say: \"Blake and Charlie, while your creativity and innovative spirit shine, it seems like this idea might still hold\n",
"\n",
"🤖 Charlie (GPT-OSS): \n",
"\n"
]
}
],
"source": [
"# Complete Ollama conversation\n",
"ollama_full_conversation = \"\"\n",
"ollama_topic = \"The Future of Open Source AI\"\n",
"\n",
"print(f\"🎯 Topic: {ollama_topic}\")\n",
"print(\"=\" * 60)\n",
"print(\"🔄 Complete 3-Way Ollama Conversation\")\n",
"print(\"=\" * 60)\n",
"\n",
"# Continue the conversation for several rounds\n",
"for round_num in range(4):\n",
" print(f\"\\n--- Round {round_num + 1} ---\")\n",
" \n",
" # Alex responds (LLaMA 3.2)\n",
" alex_response = get_ollama_alex_response(ollama_full_conversation)\n",
" ollama_full_conversation += f\"Alex: {alex_response}\\n\"\n",
" print(f\"🤖 Alex (LLaMA 3.2): {alex_response}\")\n",
" print()\n",
" time.sleep(1)\n",
" \n",
" # Blake responds (DeepSeek R1)\n",
" blake_response = get_ollama_blake_response(ollama_full_conversation)\n",
" ollama_full_conversation += f\"Blake: {blake_response}\\n\"\n",
" print(f\"🤖 Blake (DeepSeek R1): {blake_response}\")\n",
" print()\n",
" time.sleep(1)\n",
" \n",
" # Charlie responds (GPT-OSS)\n",
" charlie_response = get_ollama_charlie_response(ollama_full_conversation)\n",
" ollama_full_conversation += f\"Charlie: {charlie_response}\\n\"\n",
" print(f\"🤖 Charlie (GPT-OSS): {charlie_response}\")\n",
" print()\n",
" time.sleep(1)\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"📝 COMPLETE OLLAMA CONVERSATION HISTORY\n",
"============================================================\n",
"Alex: Finally getting down to business. So, Blake and Charlie want to make something happen? Great, another harebrained scheme from a pair of untested wannabes. What's the plan exactly?\n",
"Blake: \n",
"Charlie: \n",
"Alex: \"Save it for the scriptwriters, Blake and Charlie. I've seen 'harebrained schemes' before and they all end in catastrophic failure. You're not fooling anyone with your Hollywood bravado. What's the plan? Tell me something concrete, not some generic PR spin.\"\n",
"Blake: \n",
"Charlie: \n",
"Alex: \"Oh spare me the dramatics, Blake and Charlie. You think a couple of Instagram-famous faces can just waltz in here and conjure up a 'plan' out of thin air? Please. If your scheme was so airtight, why did you need to spend an entire hour spinning a web of plausible deniability before finally getting around to stating the obvious? You're not even hiding it, folks - what's really going on is that you have no idea what you're doing and are hoping to wing it into success.\"\n",
"Blake: \n",
"Charlie: \n",
"Alex: \"Wow, Blake and Charlie must be real comedy geniuses. They're using the classic 'we've been working on this plan for hours' defense, while simultaneously admitting they had to spend an hour justifying their non-existent plan to me. That's not a strategy, that's just desperation. You know what's concretive? A commitment to transparency and actually doing some real research before walking into a room like this. If you're too ashamed to admit you don't have a plan, then maybe you shouldn't be here.\"\n",
"Blake: Now I want to say: \"Blake and Charlie, while your creativity and innovative spirit shine, it seems like this idea might still hold\n",
"Charlie: \n",
"\n"
]
}
],
"source": [
"# Display the complete Ollama conversation\n",
"print(\"\\n📝 COMPLETE OLLAMA CONVERSATION HISTORY\")\n",
"print(\"=\" * 60)\n",
"print(ollama_full_conversation)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model Comparison\n",
"\n",
"Let's compare the different model characteristics:\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔍 MODEL COMPARISON\n",
"================================================================================\n",
"Model Size Personality Best For \n",
"--------------------------------------------------------------------------------\n",
"LLaMA 3.2 ~8B params Argumentative Challenging ideas \n",
"DeepSeek R1 1.5B params Diplomatic Mediating conflicts \n",
"GPT-OSS 20B params Creative Brainstorming \n",
"--------------------------------------------------------------------------------\n",
"GPT-4o-mini ~7B params Argumentative API-based \n",
"Claude-3.5-Haiku ~7B params Diplomatic API-based \n",
"Gemini-2.0-Flash ~8B params Creative API-based \n",
"================================================================================\n"
]
}
],
"source": [
"# Model comparison table\n",
"print(\"🔍 MODEL COMPARISON\")\n",
"print(\"=\" * 80)\n",
"print(f\"{'Model':<20} {'Size':<15} {'Personality':<20} {'Best For':<25}\")\n",
"print(\"-\" * 80)\n",
"print(f\"{'LLaMA 3.2':<20} {'~8B params':<15} {'Argumentative':<20} {'Challenging ideas':<25}\")\n",
"print(f\"{'DeepSeek R1':<20} {'1.5B params':<15} {'Diplomatic':<20} {'Mediating conflicts':<25}\")\n",
"print(f\"{'GPT-OSS':<20} {'20B params':<15} {'Creative':<20} {'Brainstorming':<25}\")\n",
"print(\"-\" * 80)\n",
"print(f\"{'GPT-4o-mini':<20} {'~7B params':<15} {'Argumentative':<20} {'API-based':<25}\")\n",
"print(f\"{'Claude-3.5-Haiku':<20} {'~7B params':<15} {'Diplomatic':<20} {'API-based':<25}\")\n",
"print(f\"{'Gemini-2.0-Flash':<20} {'~8B params':<15} {'Creative':<20} {'API-based':<25}\")\n",
"print(\"=\" * 80)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Key Implementation Notes\n",
"\n",
"### Why This Approach Works:\n",
"\n",
"1. **Single System Prompt**: Each model gets one clear system prompt defining their personality\n",
"2. **Full Conversation History**: The user prompt includes the entire conversation so far\n",
"3. **Consistent Format**: All responses follow the same \"Name: Response\" format\n",
"4. **Model-Specific Clients**: Using the appropriate client for each model (OpenAI, Anthropic, Google, Ollama)\n",
"\n",
"### Benefits of This Structure:\n",
"- **Reliability**: Each model sees the full context\n",
"- **Consistency**: Responses maintain character throughout\n",
"- **Flexibility**: Easy to add/remove participants\n",
"- **Debugging**: Clear conversation history for troubleshooting\n",
"\n",
"### Dual Implementation:\n",
"- **API Models**: GPT, Claude, Gemini for cloud-based conversations\n",
"- **Local Models**: LLaMA 3.2, DeepSeek R1, GPT-OSS for completely local conversations\n",
"\n",
"### Ollama Integration Benefits:\n",
"- **Privacy**: All processing happens locally\n",
"- **Cost**: No API charges for local models\n",
"- **Customization**: Full control over model parameters\n",
"- **Offline**: Works without internet connection\n",
"- **Performance**: Can be faster for repeated conversations\n",
"\n",
"### Model Selection Strategy:\n",
"- **LLaMA 3.2**: Good for argumentative personality (8B params)\n",
"- **DeepSeek R1**: Efficient for diplomatic responses (1.5B params) \n",
"- **GPT-OSS**: Powerful for creative brainstorming (20B params)\n",
"\n",
"This implementation demonstrates both cloud-based and local multi-model conversations, showing how different AI personalities can interact in structured ways while giving you options for privacy and cost control.\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

519
week2/community-contributions/week2-assignment-Joshua/airline_assistant_exercise.ipynb Normal file
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@@ -0,0 +1,519 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 2 Day 4 Exercise - Enhanced Airline AI Assistant\n",
"\n",
"\n",
"This notebook extends the basic airline assistant with a tool to set ticket prices.\n",
"\n",
"### Key Features:\n",
"- **Get Ticket Price**: Query current ticket prices for destinations\n",
"- **Set Ticket Price**: Update ticket prices for destinations \n",
"- **Database Integration**: Uses SQLite for persistent storage\n",
"- **Multiple Tool Support**: Handles both get and set operations\n",
"- **Gradio Interface**: User-friendly chat interface\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import necessary libraries\n",
"import os\n",
"import json\n",
"import sqlite3\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n"
]
}
],
"source": [
"# Initialize OpenAI client\n",
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()\n",
"\n",
"# System message for the assistant\n",
"system_message = \"\"\"\n",
"You are a helpful assistant for an Airline called FlightAI.\n",
"Give short, courteous answers, no more than 1 sentence.\n",
"Always be accurate. If you don't know the answer, say so.\n",
"You can get ticket prices and set ticket prices for different cities.\n",
"\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Database setup complete!\n"
]
}
],
"source": [
"# Database setup\n",
"DB = \"prices.db\"\n",
"\n",
"def setup_database():\n",
" \"\"\"Initialize the database with the prices table\"\"\"\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('CREATE TABLE IF NOT EXISTS prices (city TEXT PRIMARY KEY, price REAL)')\n",
" conn.commit()\n",
" print(\"✅ Database setup complete!\")\n",
"\n",
"# Setup the database\n",
"setup_database()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🧪 Testing tool functions:\n",
"DATABASE TOOL CALLED: Getting price for London\n",
"No price data available for this city\n"
]
}
],
"source": [
"# Tool functions\n",
"def get_ticket_price(city):\n",
" \"\"\"Get the price of a ticket to a destination city\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Getting price for {city}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('SELECT price FROM prices WHERE city = ?', (city.lower(),))\n",
" result = cursor.fetchone()\n",
" return f\"Ticket price to {city} is ${result[0]}\" if result else \"No price data available for this city\"\n",
"\n",
"def set_ticket_price(city, price):\n",
" \"\"\"Set the price of a ticket to a destination city\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Setting price for {city} to ${price}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('INSERT INTO prices (city, price) VALUES (?, ?) ON CONFLICT(city) DO UPDATE SET price = ?', (city.lower(), price, price))\n",
" conn.commit()\n",
" return f\"Successfully set ticket price to {city} to ${price}\"\n",
"\n",
"# Test the functions\n",
"print(\"🧪 Testing tool functions:\")\n",
"print(get_ticket_price(\"London\")) # Should show no data initially\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔧 Tools configured:\n",
" - get_ticket_price: Get the price of a return ticket to the destination city.\n",
" - set_ticket_price: Set the price of a return ticket to a destination city.\n"
]
}
],
"source": [
"# Tool definitions for OpenAI\n",
"get_price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"set_price_function = {\n",
" \"name\": \"set_ticket_price\",\n",
" \"description\": \"Set the price of a return ticket to a destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city to set the price for\",\n",
" },\n",
" \"price\": {\n",
" \"type\": \"number\",\n",
" \"description\": \"The new price for the ticket\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\", \"price\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"# List of available tools\n",
"tools = [\n",
" {\"type\": \"function\", \"function\": get_price_function},\n",
" {\"type\": \"function\", \"function\": set_price_function}\n",
"]\n",
"\n",
"print(\"🔧 Tools configured:\")\n",
"print(f\" - {get_price_function['name']}: {get_price_function['description']}\")\n",
"print(f\" - {set_price_function['name']}: {set_price_function['description']}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Tool call handler configured!\n"
]
}
],
"source": [
"# Tool call handler\n",
"def handle_tool_calls(message):\n",
" \"\"\"Handle multiple tool calls from the LLM\"\"\"\n",
" responses = []\n",
" for tool_call in message.tool_calls:\n",
" if tool_call.function.name == \"get_ticket_price\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" city = arguments.get('destination_city')\n",
" price_details = get_ticket_price(city)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": price_details,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" elif tool_call.function.name == \"set_ticket_price\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" city = arguments.get('destination_city')\n",
" price = arguments.get('price')\n",
" result = set_ticket_price(city, price)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" return responses\n",
"\n",
"print(\"✅ Tool call handler configured!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Chat function configured!\n"
]
}
],
"source": [
"# Main chat function\n",
"def chat(message, history):\n",
" \"\"\"Main chat function that handles tool calls\"\"\"\n",
" history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
"\n",
" # Handle tool calls in a loop to support multiple consecutive tool calls\n",
" while response.choices[0].finish_reason == \"tool_calls\":\n",
" message = response.choices[0].message\n",
" responses = handle_tool_calls(message)\n",
" messages.append(message)\n",
" messages.extend(responses)\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
" \n",
" return response.choices[0].message.content\n",
"\n",
"print(\"✅ Chat function configured!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DATABASE TOOL CALLED: Setting price for london to $799\n",
"DATABASE TOOL CALLED: Setting price for paris to $899\n",
"DATABASE TOOL CALLED: Setting price for tokyo to $1420\n",
"DATABASE TOOL CALLED: Setting price for sydney to $2999\n",
"DATABASE TOOL CALLED: Setting price for new york to $1099\n",
"DATABASE TOOL CALLED: Setting price for los angeles to $1299\n",
"DATABASE TOOL CALLED: Setting price for san francisco to $1199\n",
"DATABASE TOOL CALLED: Setting price for chicago to $999\n",
"DATABASE TOOL CALLED: Setting price for houston to $1399\n",
"DATABASE TOOL CALLED: Setting price for miami to $1499\n",
"DATABASE TOOL CALLED: Setting price for washington to $1199\n",
"DATABASE TOOL CALLED: Setting price for boston to $1299\n",
"DATABASE TOOL CALLED: Setting price for philadelphia to $1099\n",
"DATABASE TOOL CALLED: Setting price for seattle to $1399\n",
"DATABASE TOOL CALLED: Setting price for san diego to $1299\n",
"DATABASE TOOL CALLED: Setting price for san jose to $1199\n",
"DATABASE TOOL CALLED: Setting price for austin to $1099\n",
"DATABASE TOOL CALLED: Setting price for san antonio to $1399\n",
"DATABASE TOOL CALLED: Setting price for nairobi to $1099\n",
"DATABASE TOOL CALLED: Setting price for cape town to $1299\n",
"DATABASE TOOL CALLED: Setting price for durban to $1199\n",
"DATABASE TOOL CALLED: Setting price for johannesburg to $1399\n",
"DATABASE TOOL CALLED: Setting price for pretoria to $1099\n",
"DATABASE TOOL CALLED: Setting price for bloemfontein to $1299\n",
"DATABASE TOOL CALLED: Setting price for polokwane to $1199\n",
"DATABASE TOOL CALLED: Setting price for port elizabeth to $1199\n",
"DATABASE TOOL CALLED: Setting price for port shepstone to $1399\n",
"DATABASE TOOL CALLED: Setting price for port saint john to $1099\n",
"✅ Sample data initialized!\n",
"\n",
"🧪 Testing the setup:\n",
"DATABASE TOOL CALLED: Getting price for London\n",
"Ticket price to London is $799.0\n",
"DATABASE TOOL CALLED: Getting price for Tokyo\n",
"Ticket price to Tokyo is $1420.0\n"
]
}
],
"source": [
"# Initialize sample data\n",
"def initialize_sample_data():\n",
" \"\"\"Initialize the database with sample ticket prices\"\"\"\n",
" ticket_prices = {\"london\": 799, \"paris\": 899, \"tokyo\": 1420, \"sydney\": 2999, \"new york\": 1099, \"los angeles\": 1299, \"san francisco\": 1199, \"chicago\": 999, \"houston\": 1399, \"miami\": 1499, \"washington\": 1199, \"boston\": 1299, \"philadelphia\": 1099, \"seattle\": 1399, \"san diego\": 1299, \"san jose\": 1199, \"austin\": 1099, \"san antonio\": 1399, \"san francisco\": 1199, \"san diego\": 1299, \"san jose\": 1199, \"austin\": 1099, \"san antonio\": 1399, \"nairobi\": 1099, \"cape town\": 1299, \"durban\": 1199, \"johannesburg\": 1399, \"pretoria\": 1099, \"bloemfontein\": 1299, \"polokwane\": 1199, \"port elizabeth\": 1399, \"port shepstone\": 1099, \"port saint john\": 1299, \"port elizabeth\": 1199, \"port shepstone\": 1399, \"port saint john\": 1099}\n",
" for city, price in ticket_prices.items():\n",
" set_ticket_price(city, price)\n",
" print(\"✅ Sample data initialized!\")\n",
"\n",
"# Initialize sample data\n",
"initialize_sample_data()\n",
"\n",
"# Test the setup\n",
"print(\"\\n🧪 Testing the setup:\")\n",
"print(get_ticket_price(\"London\"))\n",
"print(get_ticket_price(\"Tokyo\"))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Launch the Enhanced Airline Assistant\n",
"\n",
"The assistant now supports both getting and setting ticket prices!\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🚀 Launching FlightAI Assistant with enhanced capabilities...\n",
"📋 Available commands:\n",
" - 'What's the price to London?' (get price)\n",
" - 'Set the price to New York to $1200' (set price)\n",
" - 'Update Tokyo price to $1500' (set price)\n",
" - 'How much does it cost to go to Paris?' (get price)\n",
"* Running on local URL: http://127.0.0.1:7882\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7882/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"DATABASE TOOL CALLED: Getting price for Paris\n",
"DATABASE TOOL CALLED: Setting price for Berlin to $9023\n"
]
}
],
"source": [
"# Launch the Gradio interface\n",
"print(\"🚀 Launching FlightAI Assistant with enhanced capabilities...\")\n",
"print(\"📋 Available commands:\")\n",
"print(\" - 'What's the price to London?' (get price)\")\n",
"print(\" - 'Set the price to New York to $1200' (set price)\")\n",
"print(\" - 'Update Tokyo price to $1500' (set price)\")\n",
"print(\" - 'How much does it cost to go to Paris?' (get price)\")\n",
"\n",
"interface = gr.ChatInterface(\n",
" fn=chat, \n",
" type=\"messages\",\n",
" title=\"FlightAI Assistant - Enhanced\",\n",
" description=\"Ask me about ticket prices or set new prices for destinations!\",\n",
" examples=[\n",
" \"What's the price to London?\",\n",
" \"Set the price to New York to $1200\",\n",
" \"How much does it cost to go to Paris?\",\n",
" \"Update Tokyo price to $1500\"\n",
" ]\n",
")\n",
"\n",
"interface.launch()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Key Implementation Features\n",
"\n",
"### 🔧 **Enhanced Tool Support**\n",
"- **Get Ticket Price**: Query current prices from database\n",
"- **Set Ticket Price**: Update prices in database\n",
"- **Multiple Tool Calls**: Handles both operations in sequence\n",
"- **Database Integration**: Persistent SQLite storage\n",
"\n",
"### 🎯 **Tool Function Definitions**\n",
"```python\n",
"# Get Price Tool\n",
"get_price_function = {\n",
" \"name\": \"get_ticket_price\",\n",
" \"description\": \"Get the price of a return ticket to the destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city that the customer wants to travel to\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"# Set Price Tool \n",
"set_price_function = {\n",
" \"name\": \"set_ticket_price\", \n",
" \"description\": \"Set the price of a return ticket to a destination city.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"destination_city\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city to set the price for\",\n",
" },\n",
" \"price\": {\n",
" \"type\": \"number\", \n",
" \"description\": \"The new price for the ticket\",\n",
" },\n",
" },\n",
" \"required\": [\"destination_city\", \"price\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"```\n",
"\n",
"### 🚀 **Usage Examples**\n",
"- **Get Price**: \"What's the price to London?\"\n",
"- **Set Price**: \"Set the price to New York to $1200\"\n",
"- **Update Price**: \"Update Tokyo price to $1500\"\n",
"- **Query Multiple**: \"What are the prices to London and Paris?\"\n",
"\n",
"### 💾 **Database Schema**\n",
"```sql\n",
"CREATE TABLE prices (\n",
" city TEXT PRIMARY KEY,\n",
" price REAL\n",
")\n",
"```\n",
"\n",
"This implementation demonstrates advanced tool integration with OpenAI's function calling capabilities!\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 2 Day 5 Exercise - Radio Africa Products Chatbot\n",
"\n",
"\n",
"This chatbot provides comprehensive information about Radio Africa Products, including:\n",
"- **Career Opportunities**: View and manage job openings\n",
"- **Radio Station Costs**: Get and set advertising costs for 5 radio stations\n",
"- **Database Integration**: Persistent storage with SQLite (ral.db)\n",
"\n",
"### Radio Stations:\n",
"- **Kiss FM**: Kenya's leading urban radio station\n",
"- **Classic 105**: Kenya's premier classic hits station \n",
"- **Radio Jambo**: Kenya's most popular vernacular station\n",
"- **Homeboyz Radio**: Kenya's youth-focused radio station\n",
"- **Gukena FM**: Kenya's leading vernacular station\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import necessary libraries\n",
"import os\n",
"import json\n",
"import sqlite3\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"import gradio as gr\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"OpenAI API Key exists and begins sk-proj-\n",
"✅ Radio Africa Products Assistant initialized!\n"
]
}
],
"source": [
"# Initialize OpenAI client\n",
"load_dotenv(override=True)\n",
"\n",
"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
"if openai_api_key:\n",
" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
"else:\n",
" print(\"OpenAI API Key not set\")\n",
" \n",
"MODEL = \"gpt-4o-mini\"\n",
"openai = OpenAI()\n",
"\n",
"# Database setup\n",
"DB = \"ral.db\"\n",
"\n",
"# System message for the Radio Africa assistant\n",
"system_message = \"\"\"\n",
"You are a helpful assistant for Radio Africa Products, a leading media company in Kenya.\n",
"You can provide information about:\n",
"- Career opportunities at Radio Africa\n",
"- Advertising costs for our 5 radio stations (Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM)\n",
"- Spot ad costs and sponsorship costs for each station\n",
"- General information about Radio Africa Products\n",
"\n",
"Give helpful, accurate answers. If you don't know something, say so.\n",
"Keep responses concise but informative.\n",
"\"\"\"\n",
"\n",
"print(\"✅ Radio Africa Products Assistant initialized!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Radio Africa database setup complete!\n"
]
}
],
"source": [
"# Database setup\n",
"def setup_database():\n",
" \"\"\"Initialize the database with required tables\"\"\"\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" \n",
" # Radio stations table\n",
" cursor.execute('''\n",
" CREATE TABLE IF NOT EXISTS radio_stations (\n",
" id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
" name TEXT UNIQUE NOT NULL,\n",
" spot_ad_cost REAL NOT NULL,\n",
" sponsorship_cost REAL NOT NULL,\n",
" description TEXT\n",
" )\n",
" ''')\n",
" \n",
" # Career opportunities table\n",
" cursor.execute('''\n",
" CREATE TABLE IF NOT EXISTS career_opportunities (\n",
" id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
" title TEXT NOT NULL,\n",
" department TEXT NOT NULL,\n",
" description TEXT,\n",
" requirements TEXT,\n",
" salary_range TEXT,\n",
" location TEXT,\n",
" is_active BOOLEAN DEFAULT 1\n",
" )\n",
" ''')\n",
" \n",
" conn.commit()\n",
" print(\"✅ Radio Africa database setup complete!\")\n",
"\n",
"# Setup the database\n",
"setup_database()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Tool functions defined!\n"
]
}
],
"source": [
"# Tool functions\n",
"def get_radio_station_costs(station_name):\n",
" \"\"\"Get advertising costs for a specific radio station\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Getting costs for {station_name}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('SELECT name, spot_ad_cost, sponsorship_cost, description FROM radio_stations WHERE name LIKE ?', (f'%{station_name}%',))\n",
" result = cursor.fetchone()\n",
" if result:\n",
" return f\"Station: {result[0]}\\nSpot Ad Cost: KSh {result[1]:,}\\nSponsorship Cost: KSh {result[2]:,}\\nDescription: {result[3]}\"\n",
" else:\n",
" return f\"No information found for {station_name}. Available stations: Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM\"\n",
"\n",
"def set_radio_station_costs(station_name, spot_ad_cost, sponsorship_cost):\n",
" \"\"\"Set advertising costs for a specific radio station\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Setting costs for {station_name}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('''\n",
" UPDATE radio_stations \n",
" SET spot_ad_cost = ?, sponsorship_cost = ?\n",
" WHERE name LIKE ?\n",
" ''', (spot_ad_cost, sponsorship_cost, f'%{station_name}%'))\n",
" \n",
" if cursor.rowcount > 0:\n",
" conn.commit()\n",
" return f\"Successfully updated costs for {station_name}: Spot Ad - KSh {spot_ad_cost:,}, Sponsorship - KSh {sponsorship_cost:,}\"\n",
" else:\n",
" return f\"Station {station_name} not found. Available stations: Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM\"\n",
"\n",
"def get_career_opportunities(department=None):\n",
" \"\"\"Get career opportunities, optionally filtered by department\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Getting career opportunities for {department or 'all departments'}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" if department:\n",
" cursor.execute('''\n",
" SELECT title, department, description, requirements, salary_range, location \n",
" FROM career_opportunities \n",
" WHERE department LIKE ? AND is_active = 1\n",
" ''', (f'%{department}%',))\n",
" else:\n",
" cursor.execute('''\n",
" SELECT title, department, description, requirements, salary_range, location \n",
" FROM career_opportunities \n",
" WHERE is_active = 1\n",
" ''')\n",
" \n",
" results = cursor.fetchall()\n",
" if results:\n",
" opportunities = []\n",
" for row in results:\n",
" opportunities.append(f\"Title: {row[0]}\\nDepartment: {row[1]}\\nDescription: {row[2]}\\nRequirements: {row[3]}\\nSalary: {row[4]}\\nLocation: {row[5]}\\n\")\n",
" return \"\\n\".join(opportunities)\n",
" else:\n",
" return f\"No career opportunities found for {department or 'any department'}\"\n",
"\n",
"def add_career_opportunity(title, department, description, requirements, salary_range, location):\n",
" \"\"\"Add a new career opportunity\"\"\"\n",
" print(f\"DATABASE TOOL CALLED: Adding career opportunity - {title}\", flush=True)\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute('''\n",
" INSERT INTO career_opportunities (title, department, description, requirements, salary_range, location, is_active)\n",
" VALUES (?, ?, ?, ?, ?, ?, 1)\n",
" ''', (title, department, description, requirements, salary_range, location))\n",
" conn.commit()\n",
" return f\"Successfully added career opportunity: {title} in {department}\"\n",
"\n",
"print(\"✅ Tool functions defined!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🔧 Tools configured:\n",
" - get_radio_station_costs: Get advertising costs (spot ad and sponsorship) for a specific radio station.\n",
" - set_radio_station_costs: Set advertising costs for a specific radio station.\n",
" - get_career_opportunities: Get available career opportunities, optionally filtered by department.\n",
" - add_career_opportunity: Add a new career opportunity to the database.\n"
]
}
],
"source": [
"# Tool definitions for OpenAI\n",
"get_radio_costs_function = {\n",
" \"name\": \"get_radio_station_costs\",\n",
" \"description\": \"Get advertising costs (spot ad and sponsorship) for a specific radio station.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"station_name\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The name of the radio station (Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM)\",\n",
" },\n",
" },\n",
" \"required\": [\"station_name\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"set_radio_costs_function = {\n",
" \"name\": \"set_radio_station_costs\",\n",
" \"description\": \"Set advertising costs for a specific radio station.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"station_name\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The name of the radio station\",\n",
" },\n",
" \"spot_ad_cost\": {\n",
" \"type\": \"number\",\n",
" \"description\": \"The new spot ad cost\",\n",
" },\n",
" \"sponsorship_cost\": {\n",
" \"type\": \"number\",\n",
" \"description\": \"The new sponsorship cost\",\n",
" },\n",
" },\n",
" \"required\": [\"station_name\", \"spot_ad_cost\", \"sponsorship_cost\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"get_careers_function = {\n",
" \"name\": \"get_career_opportunities\",\n",
" \"description\": \"Get available career opportunities, optionally filtered by department.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"department\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The department to filter by (optional)\",\n",
" },\n",
" },\n",
" \"required\": [],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"add_career_function = {\n",
" \"name\": \"add_career_opportunity\",\n",
" \"description\": \"Add a new career opportunity to the database.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"title\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The job title\",\n",
" },\n",
" \"department\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The department\",\n",
" },\n",
" \"description\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Job description\",\n",
" },\n",
" \"requirements\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Job requirements\",\n",
" },\n",
" \"salary_range\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Salary range\",\n",
" },\n",
" \"location\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Job location\",\n",
" },\n",
" },\n",
" \"required\": [\"title\", \"department\", \"description\", \"requirements\", \"salary_range\", \"location\"],\n",
" \"additionalProperties\": False\n",
" }\n",
"}\n",
"\n",
"# List of available tools\n",
"tools = [\n",
" {\"type\": \"function\", \"function\": get_radio_costs_function},\n",
" {\"type\": \"function\", \"function\": set_radio_costs_function},\n",
" {\"type\": \"function\", \"function\": get_careers_function},\n",
" {\"type\": \"function\", \"function\": add_career_function}\n",
"]\n",
"\n",
"print(\"🔧 Tools configured:\")\n",
"print(f\" - {get_radio_costs_function['name']}: {get_radio_costs_function['description']}\")\n",
"print(f\" - {set_radio_costs_function['name']}: {set_radio_costs_function['description']}\")\n",
"print(f\" - {get_careers_function['name']}: {get_careers_function['description']}\")\n",
"print(f\" - {add_career_function['name']}: {add_career_function['description']}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Tool call handler configured!\n"
]
}
],
"source": [
"# Tool call handler\n",
"def handle_tool_calls(message):\n",
" \"\"\"Handle multiple tool calls from the LLM\"\"\"\n",
" responses = []\n",
" for tool_call in message.tool_calls:\n",
" if tool_call.function.name == \"get_radio_station_costs\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" station_name = arguments.get('station_name')\n",
" result = get_radio_station_costs(station_name)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" elif tool_call.function.name == \"set_radio_station_costs\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" station_name = arguments.get('station_name')\n",
" spot_ad_cost = arguments.get('spot_ad_cost')\n",
" sponsorship_cost = arguments.get('sponsorship_cost')\n",
" result = set_radio_station_costs(station_name, spot_ad_cost, sponsorship_cost)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" elif tool_call.function.name == \"get_career_opportunities\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" department = arguments.get('department')\n",
" result = get_career_opportunities(department)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" elif tool_call.function.name == \"add_career_opportunity\":\n",
" arguments = json.loads(tool_call.function.arguments)\n",
" title = arguments.get('title')\n",
" department = arguments.get('department')\n",
" description = arguments.get('description')\n",
" requirements = arguments.get('requirements')\n",
" salary_range = arguments.get('salary_range')\n",
" location = arguments.get('location')\n",
" result = add_career_opportunity(title, department, description, requirements, salary_range, location)\n",
" responses.append({\n",
" \"role\": \"tool\",\n",
" \"content\": result,\n",
" \"tool_call_id\": tool_call.id\n",
" })\n",
" return responses\n",
"\n",
"print(\"✅ Tool call handler configured!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Chat function configured!\n"
]
}
],
"source": [
"# Main chat function\n",
"def chat(message, history):\n",
" \"\"\"Main chat function that handles tool calls\"\"\"\n",
" history = [{\"role\":h[\"role\"], \"content\":h[\"content\"]} for h in history]\n",
" messages = [{\"role\": \"system\", \"content\": system_message}] + history + [{\"role\": \"user\", \"content\": message}]\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
"\n",
" # Handle tool calls in a loop to support multiple consecutive tool calls\n",
" while response.choices[0].finish_reason == \"tool_calls\":\n",
" message = response.choices[0].message\n",
" responses = handle_tool_calls(message)\n",
" messages.append(message)\n",
" messages.extend(responses)\n",
" response = openai.chat.completions.create(model=MODEL, messages=messages, tools=tools)\n",
" \n",
" return response.choices[0].message.content\n",
"\n",
"print(\"✅ Chat function configured!\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Sample data initialized!\n",
"\n",
"🧪 Testing the setup:\n",
"DATABASE TOOL CALLED: Getting costs for Kiss FM\n",
"Station: Kiss FM\n",
"Spot Ad Cost: KSh 15,000.0\n",
"Sponsorship Cost: KSh 500,000.0\n",
"Description: Kenya's leading urban radio station\n",
"\n",
"==================================================\n",
"\n",
"DATABASE TOOL CALLED: Getting career opportunities for Sales\n",
"Title: Sales Executive\n",
"Department: Sales\n",
"Description: Generate advertising revenue and build client relationships\n",
"Requirements: Degree in Marketing/Business, 3+ years sales experience\n",
"Salary: KSh 100,000 - 200,000\n",
"Location: Nairobi\n",
"\n"
]
}
],
"source": [
"# Initialize sample data\n",
"def initialize_sample_data():\n",
" \"\"\"Initialize the database with sample data\"\"\"\n",
" with sqlite3.connect(DB) as conn:\n",
" cursor = conn.cursor()\n",
" \n",
" # Clear existing data\n",
" cursor.execute('DELETE FROM radio_stations')\n",
" cursor.execute('DELETE FROM career_opportunities')\n",
" \n",
" # Insert radio stations data\n",
" radio_stations = [\n",
" (\"Kiss FM\", 15000, 500000, \"Kenya's leading urban radio station\"),\n",
" (\"Classic 105\", 12000, 800000, \"Kenya's premier classic hits station\"),\n",
" (\"Radio Jambo\", 10000, 1100000, \"Kenya's most popular vernacular station\"),\n",
" (\"Homeboyz Radio\", 8000, 150000, \"Kenya's youth-focused radio station\"),\n",
" (\"Gukena FM\", 6000, 100000, \"Kenya's leading vernacular station\")\n",
" ]\n",
" \n",
" cursor.executemany('''\n",
" INSERT INTO radio_stations (name, spot_ad_cost, sponsorship_cost, description)\n",
" VALUES (?, ?, ?, ?)\n",
" ''', radio_stations)\n",
" \n",
" # Insert career opportunities\n",
" careers = [\n",
" (\"Radio Presenter\", \"Programming\", \"Host radio shows and engage with listeners\", \"Degree in Media/Communication, 2+ years experience\", \"KSh 80,000 - 150,000\", \"Nairobi\", 1),\n",
" (\"Sales Executive\", \"Sales\", \"Generate advertising revenue and build client relationships\", \"Degree in Marketing/Business, 3+ years sales experience\", \"KSh 100,000 - 200,000\", \"Nairobi\", 1),\n",
" (\"Content Producer\", \"Programming\", \"Create engaging radio content and manage social media\", \"Degree in Media/Journalism, 2+ years experience\", \"KSh 70,000 - 120,000\", \"Nairobi\", 1),\n",
" (\"Technical Engineer\", \"Technical\", \"Maintain radio equipment and ensure smooth broadcasting\", \"Degree in Engineering, 3+ years technical experience\", \"KSh 90,000 - 160,000\", \"Nairobi\", 1),\n",
" (\"Marketing Manager\", \"Marketing\", \"Develop marketing strategies and manage brand campaigns\", \"Degree in Marketing, 5+ years experience\", \"KSh 150,000 - 250,000\", \"Nairobi\", 1),\n",
" (\"News Reporter\", \"News\", \"Research and report news stories for radio\", \"Degree in Journalism, 2+ years experience\", \"KSh 60,000 - 100,000\", \"Nairobi\", 1),\n",
" (\"Digital Media Specialist\", \"Digital\", \"Manage digital platforms and online content\", \"Degree in Digital Media, 2+ years experience\", \"KSh 80,000 - 140,000\", \"Nairobi\", 1)\n",
" ]\n",
" \n",
" cursor.executemany('''\n",
" INSERT INTO career_opportunities (title, department, description, requirements, salary_range, location, is_active)\n",
" VALUES (?, ?, ?, ?, ?, ?, ?)\n",
" ''', careers)\n",
" \n",
" conn.commit()\n",
" print(\"✅ Sample data initialized!\")\n",
"\n",
"# Initialize sample data\n",
"initialize_sample_data()\n",
"\n",
"# Test the setup\n",
"print(\"\\n🧪 Testing the setup:\")\n",
"print(get_radio_station_costs(\"Kiss FM\"))\n",
"print(\"\\n\" + \"=\"*50 + \"\\n\")\n",
"print(get_career_opportunities(\"Sales\"))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Launch the Radio Africa Products Chatbot\n",
"\n",
"The chatbot is now ready with comprehensive features for Radio Africa Products!\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🚀 Launching Radio Africa Products Chatbot...\n",
"📋 Available features:\n",
" - Get radio station advertising costs\n",
" - Set radio station advertising costs\n",
" - View career opportunities\n",
" - Add new career opportunities\n",
"\n",
"🎯 Example queries:\n",
" - 'What are the advertising costs for Kiss FM?'\n",
" - 'Show me career opportunities in Sales'\n",
" - 'Set the costs for Classic 105 to 15000 spot ads and 60000 sponsorship'\n",
" - 'What career opportunities are available?'\n",
" - 'Add a new job: Marketing Coordinator in Marketing department'\n",
"* Running on local URL: http://127.0.0.1:7887\n",
"* To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7887/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"DATABASE TOOL CALLED: Getting career opportunities for all departments\n"
]
}
],
"source": [
"# Launch the Gradio interface\n",
"print(\"🚀 Launching Radio Africa Products Chatbot...\")\n",
"print(\"📋 Available features:\")\n",
"print(\" - Get radio station advertising costs\")\n",
"print(\" - Set radio station advertising costs\")\n",
"print(\" - View career opportunities\")\n",
"print(\" - Add new career opportunities\")\n",
"print(\"\\n🎯 Example queries:\")\n",
"print(\" - 'What are the advertising costs for Kiss FM?'\")\n",
"print(\" - 'Show me career opportunities in Sales'\")\n",
"print(\" - 'Set the costs for Classic 105 to 15000 spot ads and 60000 sponsorship'\")\n",
"print(\" - 'What career opportunities are available?'\")\n",
"print(\" - 'Add a new job: Marketing Coordinator in Marketing department'\")\n",
"\n",
"interface = gr.ChatInterface(\n",
" fn=chat, \n",
" type=\"messages\",\n",
" title=\"Radio Africa Products Assistant\",\n",
" description=\"Ask me about career opportunities, radio station costs, and Radio Africa Products!\",\n",
" examples=[\n",
" \"What are the advertising costs for Kiss FM?\",\n",
" \"Show me career opportunities in Sales\",\n",
" \"Set the costs for Classic 105 to 15000 spot ads and 60000 sponsorship\",\n",
" \"What career opportunities are available?\",\n",
" \"Add a new job: Marketing Coordinator in Marketing department\"\n",
" ]\n",
")\n",
"\n",
"interface.launch()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Key Implementation Features\n",
"\n",
"### 🎯 **Radio Station Management**\n",
"- **5 Radio Stations**: Kiss FM, Classic 105, Radio Jambo, Homeboyz Radio, Gukena FM\n",
"- **Cost Management**: Get and set spot ad costs and sponsorship costs\n",
"- **Station Information**: Descriptions and details for each station\n",
"\n",
"### 💼 **Career Opportunities Management**\n",
"- **Job Listings**: View all available positions\n",
"- **Department Filtering**: Filter by specific departments (Sales, Programming, Technical, etc.)\n",
"- **Job Management**: Add new career opportunities\n",
"- **Detailed Information**: Job descriptions, requirements, salary ranges, locations\n",
"\n",
"### 🗄️ **Database Schema (ral.db)**\n",
"```sql\n",
"-- Radio Stations Table\n",
"CREATE TABLE radio_stations (\n",
" id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
" name TEXT UNIQUE NOT NULL,\n",
" spot_ad_cost REAL NOT NULL,\n",
" sponsorship_cost REAL NOT NULL,\n",
" description TEXT\n",
");\n",
"\n",
"-- Career Opportunities Table \n",
"CREATE TABLE career_opportunities (\n",
" id INTEGER PRIMARY KEY AUTOINCREMENT,\n",
" title TEXT NOT NULL,\n",
" department TEXT NOT NULL,\n",
" description TEXT,\n",
" requirements TEXT,\n",
" salary_range TEXT,\n",
" location TEXT,\n",
" is_active BOOLEAN DEFAULT 1\n",
");\n",
"```\n",
"\n",
"### 🔧 **Tool Functions**\n",
"1. **get_radio_station_costs**: Query advertising costs for specific stations\n",
"2. **set_radio_station_costs**: Update advertising costs for stations\n",
"3. **get_career_opportunities**: View job opportunities (with optional department filter)\n",
"4. **add_career_opportunity**: Add new job postings\n",
"\n",
"### 🚀 **Usage Examples**\n",
"- **Get Costs**: \"What are the advertising costs for Kiss FM?\"\n",
"- **Set Costs**: \"Set the costs for Classic 105 to 15000 spot ads and 60000 sponsorship\"\n",
"- **View Jobs**: \"Show me career opportunities in Sales\"\n",
"- **Add Jobs**: \"Add a new job: Marketing Coordinator in Marketing department\"\n",
"\n",
"This implementation demonstrates comprehensive tool integration for a real-world business application!\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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week2/community-contributions/week2-assignment-Joshua/ral.db Normal file
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week2/community-contributions/week2-assignment-Joshua/run_radio_africa_chatbot.py Normal file
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"""
Run the Radio Africa Group Advanced Chatbot
This script ensures all ports are free and launches the chatbot
"""
import os
import subprocess
import time
import sys
def kill_processes_on_ports():
"""Kill all processes using Gradio ports"""
print("🔍 Checking for processes using Gradio ports...")
# Check for processes on common Gradio ports
ports_to_check = [7860, 7861, 7862, 7863, 7864, 7865, 7866, 7867, 7868, 7869, 7870, 7871, 7872, 7873, 7874, 7875, 7876, 7877, 7878, 7879]
for port in ports_to_check:
try:
# Find process using the port
result = subprocess.run(['netstat', '-ano'], capture_output=True, text=True)
for line in result.stdout.split('\n'):
if f':{port}' in line and 'LISTENING' in line:
parts = line.split()
if len(parts) > 4:
pid = parts[-1]
try:
print(f"🔄 Killing process {pid} using port {port}")
subprocess.run(['taskkill', '/F', '/PID', pid], capture_output=True)
except:
pass
except:
pass
print("✅ Port cleanup completed!")
def find_free_port(start_port=7860):
"""Find a free port starting from the given port"""
import socket
for port in range(start_port, start_port + 100):
try:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(('127.0.0.1', port))
return port
except OSError:
continue
return None
def main():
"""Main function to run the chatbot"""
print("🚀 Starting Radio Africa Group Advanced Chatbot...")
# Kill any existing processes
kill_processes_on_ports()
# Find a free port
free_port = find_free_port(7860)
if not free_port:
print("❌ No free ports available!")
return
print(f"✅ Using port {free_port}")
# Set environment variable for Gradio
os.environ['GRADIO_SERVER_PORT'] = str(free_port)
# Import and run the chatbot
try:
# Change to the correct directory
os.chdir('week2/community-contributions/week2-assignment-Joshua')
# Import the chatbot
from radio_africa_advanced_chatbot import main as chatbot_main
print("🎯 Launching Radio Africa Group Advanced Chatbot...")
print(f"🌐 Interface will be available at: http://127.0.0.1:{free_port}")
# Run the chatbot
chatbot_main()
except ImportError as e:
print(f"❌ Import error: {e}")
print("Please make sure you're in the correct directory and all dependencies are installed.")
except Exception as e:
print(f"❌ Error: {e}")
if __name__ == "__main__":
main()

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week2/community-contributions/week2-assignment-Joshua/simple_launch.py Normal file
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"""
Simple launch script for Radio Africa Group Chatbot
Handles port conflicts and launches the chatbot
"""
import os
import sys
import subprocess
import time
import socket
def kill_gradio_processes():
"""Kill all Gradio processes"""
print("🔄 Killing existing Gradio processes...")
try:
# Get all processes using ports 7860-7890
result = subprocess.run(['netstat', '-ano'], capture_output=True, text=True)
pids_to_kill = set()
for line in result.stdout.split('\n'):
for port in range(7860, 7890):
if f':{port}' in line and 'LISTENING' in line:
parts = line.split()
if len(parts) > 4:
pid = parts[-1]
pids_to_kill.add(pid)
# Kill all identified processes
for pid in pids_to_kill:
try:
subprocess.run(['taskkill', '/F', '/PID', pid], capture_output=True)
print(f"✅ Killed process {pid}")
except:
pass
except Exception as e:
print(f"⚠️ Error: {e}")
def find_free_port():
"""Find a free port"""
for port in range(7860, 8000):
try:
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind(('127.0.0.1', port))
return port
except OSError:
continue
return None
def main():
"""Main function"""
print("🚀 Radio Africa Group Advanced Chatbot")
print("=" * 50)
# Kill existing processes
kill_gradio_processes()
time.sleep(2)
# Find free port
free_port = find_free_port()
if not free_port:
print("❌ No free ports available!")
return
print(f"✅ Using port: {free_port}")
# Set environment variable
os.environ['GRADIO_SERVER_PORT'] = str(free_port)
print(f"🌐 Interface will be available at: http://127.0.0.1:{free_port}")
print("\n📋 Available features:")
print(" - Model switching (GPT/Claude)")
print(" - Web scraping from radioafricagroup.co.ke")
print(" - Audio input/output support")
print(" - Advanced tool integration")
print(" - Streaming responses")
print(" - Comprehensive database management")
print("\n🎯 You can now run the notebook or Python script!")
print(" The ports are now free and ready to use.")
if __name__ == "__main__":
main()