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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Business idea generator and evaluator \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Start with imports - ask ChatGPT to explain any package that you don't know\n",
"\n",
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from anthropic import Anthropic\n",
"from IPython.display import Markdown, display"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Always remember to do this!\n",
"load_dotenv(override=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Print the key prefixes to help with any debugging\n",
"\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",
"deepseek_api_key = os.getenv('DEEPSEEK_API_KEY')\n",
"groq_api_key = os.getenv('GROQ_API_KEY')\n",
"\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",
"if anthropic_api_key:\n",
" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
"else:\n",
" print(\"Anthropic API Key not set (and this is optional)\")\n",
"\n",
"if google_api_key:\n",
" print(f\"Google API Key exists and begins {google_api_key[:2]}\")\n",
"else:\n",
" print(\"Google API Key not set (and this is optional)\")\n",
"\n",
"if deepseek_api_key:\n",
" print(f\"DeepSeek API Key exists and begins {deepseek_api_key[:3]}\")\n",
"else:\n",
" print(\"DeepSeek API Key not set (and this is optional)\")\n",
"\n",
"if groq_api_key:\n",
" print(f\"Groq API Key exists and begins {groq_api_key[:4]}\")\n",
"else:\n",
" print(\"Groq API Key not set (and this is optional)\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"request = (\n",
" \"Please generate three innovative business ideas aligned with the latest global trends. \"\n",
" \"For each idea, include a brief description (2–3 sentences).\"\n",
")\n",
"messages = [{\"role\": \"user\", \"content\": request}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"messages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"openai = OpenAI()\n",
"'''\n",
"response = openai.chat.completions.create(\n",
" model=\"gpt-4o-mini\",\n",
" messages=messages,\n",
")\n",
"question = response.choices[0].message.content\n",
"print(question)\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"competitors = []\n",
"answers = []\n",
"#messages = [{\"role\": \"user\", \"content\": question}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The API we know well\n",
"\n",
"model_name = \"gpt-4o-mini\"\n",
"\n",
"response = openai.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Anthropic has a slightly different API, and Max Tokens is required\n",
"\n",
"model_name = \"claude-3-7-sonnet-latest\"\n",
"\n",
"claude = Anthropic()\n",
"response = claude.messages.create(model=model_name, messages=messages, max_tokens=1000)\n",
"answer = response.content[0].text\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gemini = OpenAI(api_key=google_api_key, base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\")\n",
"model_name = \"gemini-2.0-flash\"\n",
"\n",
"response = gemini.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"deepseek = OpenAI(api_key=deepseek_api_key, base_url=\"https://api.deepseek.com/v1\")\n",
"model_name = \"deepseek-chat\"\n",
"\n",
"response = deepseek.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"groq = OpenAI(api_key=groq_api_key, base_url=\"https://api.groq.com/openai/v1\")\n",
"model_name = \"llama-3.3-70b-versatile\"\n",
"\n",
"response = groq.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!ollama pull llama3.2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ollama = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
"model_name = \"llama3.2\"\n",
"\n",
"response = ollama.chat.completions.create(model=model_name, messages=messages)\n",
"answer = response.choices[0].message.content\n",
"\n",
"display(Markdown(answer))\n",
"competitors.append(model_name)\n",
"answers.append(answer)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# So where are we?\n",
"\n",
"print(competitors)\n",
"print(answers)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# It's nice to know how to use \"zip\"\n",
"for competitor, answer in zip(competitors, answers):\n",
" print(f\"Competitor: {competitor}\\n\\n{answer}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Let's bring this together - note the use of \"enumerate\"\n",
"\n",
"together = \"\"\n",
"for index, answer in enumerate(answers):\n",
" together += f\"# Response from competitor {index+1}\\n\\n\"\n",
" together += answer + \"\\n\\n\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(together)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"judge = f\"\"\"You are judging a competition between {len(competitors)} competitors.\n",
"Each model was asked to generate three innovative business ideas aligned with the latest global trends.\n",
"\n",
"Your job is to evaluate the likelihood of success for each idea on a scale from 0 to 100 percent. For each competitor, list the three percentages in the same order as their ideas.\n",
"\n",
"Respond only with JSON in this format:\n",
"{{\"results\": [\n",
" {{\"competitor\": 1, \"success_chances\": [perc1, perc2, perc3]}},\n",
" {{\"competitor\": 2, \"success_chances\": [perc1, perc2, perc3]}},\n",
" ...\n",
"]}}\n",
"\n",
"Here are the ideas from each competitor:\n",
"\n",
"{together}\n",
"\n",
"Now respond with only the JSON, nothing else.\"\"\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(judge)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"judge_messages = [{\"role\": \"user\", \"content\": judge}]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Judgement time!\n",
"\n",
"openai = OpenAI()\n",
"response = openai.chat.completions.create(\n",
" model=\"o3-mini\",\n",
" messages=judge_messages,\n",
")\n",
"results = response.choices[0].message.content\n",
"print(results)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Parse judge results JSON and display success probabilities\n",
"results_dict = json.loads(results)\n",
"for entry in results_dict[\"results\"]:\n",
" comp_num = entry[\"competitor\"]\n",
" comp_name = competitors[comp_num - 1]\n",
" chances = entry[\"success_chances\"]\n",
" print(f\"{comp_name}:\")\n",
" for idx, perc in enumerate(chances, start=1):\n",
" print(f\" Idea {idx}: {perc}% chance of success\")\n",
" print()\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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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