{
 "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"
   ]
  }
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