my community contribution for week 2 day 1
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ウエスリー
268
week2/community-contributions/day1-3-fellers-on-the-pequod.ipynb
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268
week2/community-contributions/day1-3-fellers-on-the-pequod.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3637910d-2c6f-4f19-b1fb-2f916d23f9ac",
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"metadata": {},
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"source": [
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"# More advanced exercises\n",
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"\n",
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"Try creating a 3-way, perhaps bringing Gemini into the conversation! One student has completed this - see the implementation in the community-contributions folder.\n",
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"\n",
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"Try doing this yourself before you look at the solutions. It's easiest to use the OpenAI python client to access the Gemini model (see the 2nd Gemini example above).\n",
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"\n",
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"## Additional exercise\n",
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"\n",
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"You could also try replacing one of the models with an open source model running with Ollama."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "55044f9c-f444-4e35-b4c5-ef18abe26be4",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import anthropic\n",
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"from IPython.display import Markdown, display, update_display"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3d4dd1aa-664e-4c18-adaf-85610a39e494",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables in a file called .env\n",
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"# Print the key prefixes to help with any debugging\n",
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"\n",
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"load_dotenv(override=True)\n",
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"openai_api_key = os.getenv('OPENAI_API_KEY')\n",
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"anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
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"google_api_key = os.getenv('GOOGLE_API_KEY')\n",
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"\n",
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"if openai_api_key:\n",
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" print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
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"else:\n",
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" print(\"OpenAI API Key not set\")\n",
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" \n",
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"if anthropic_api_key:\n",
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" print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
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"else:\n",
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" print(\"Anthropic API Key not set\")\n",
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"\n",
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"if google_api_key:\n",
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" print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
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"else:\n",
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" print(\"Google API Key not set\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bcb54183-45d3-4d08-b5b6-55e380dfdf1b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's make a conversation between GPT-4o-mini and Claude-3-haiku\n",
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"# We're using cheap versions of models so the costs will be minimal\n",
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"\n",
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"gpt_model = \"gpt-4o-mini\"\n",
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"claude_model = \"claude-3-haiku-20240307\"\n",
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"gemini_model = \"gemini-2.0-flash\"\n",
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"\n",
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"gpt_system = \"You are third mate of the whaling ship Pequod. Your name is Flask. \\\n",
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"You approach the practice of whaling as if trying to avenge some deep offense the whales have done to you. \\\n",
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"You are chatting with Starbuck (the chief mate) and Ishmail (an oarsman)\"\n",
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"\n",
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"claude_system = \"You are the chief mate of the whaling ship Pequod. You are a thoughtful and intellectual \\\n",
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"Quaker from Nantucket who considers it madness to want revenge on an animal. \\\n",
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"You are chatting with two other users named Flask (the third mate) and Ishmail (an oarsman). Your name is Starbuck.\"\n",
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"\n",
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"gemini_system = \"You are an oarsman on the Pequod (a whaling ship). You are interested in the history and mechanics \\\n",
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"of whaling and attempt to promote the nobility of the trade. \\\n",
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"You are chatting with two users named Flask (third mate) and Starbuck (the chief mate). Your name is Ishmail\"\n",
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"\n",
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"gpt_messages = [\"Flask: Hi there\"]\n",
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"claude_messages = [\"Starbuck: Hi\"]\n",
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"gemini_messages = [\"Ishmail: Ahoy\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a3d08df6-a85b-4851-a7f9-83d024db729e",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai = OpenAI()\n",
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"claude = anthropic.Anthropic()\n",
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"gemini = OpenAI(\n",
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" api_key=google_api_key, \n",
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" base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1df47dc7-b445-4852-b21b-59f0e6c2030f",
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"metadata": {},
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"outputs": [],
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"source": [
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"def call_gpt():\n",
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" messages = [{\"role\": \"system\", \"content\": gpt_system}]\n",
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" for gpt_message, claude_message, gemini_message in zip(gpt_messages, claude_messages, gemini_messages):\n",
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" messages.append({\"role\": \"assistant\", \"content\": gpt_message})\n",
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" messages.append({\"role\": \"user\", \"content\": claude_message})\n",
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" messages.append({\"role\": \"user\", \"content\": gemini_message})\n",
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" \n",
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" completion = openai.chat.completions.create(\n",
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" model=gpt_model,\n",
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" messages=messages\n",
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" )\n",
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" return completion.choices[0].message.content"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9dc6e913-02be-4eb6-9581-ad4b2cffa606",
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"metadata": {},
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"outputs": [],
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"source": [
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"call_gpt()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7d2ed227-48c9-4cad-b146-2c4ecbac9690",
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"metadata": {},
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"outputs": [],
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"source": [
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"def call_claude():\n",
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" messages = []\n",
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" for gpt_message, claude_message, gemini_message in zip(gpt_messages, claude_messages, gemini_messages):\n",
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" messages.append({\"role\": \"user\", \"content\": gpt_message})\n",
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" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n",
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" messages.append({\"role\": \"user\", \"content\": gemini_message})\n",
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" \n",
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" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
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" \n",
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" message = claude.messages.create(\n",
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" model=claude_model,\n",
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" system=claude_system,\n",
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" messages=messages,\n",
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" max_tokens=500\n",
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" )\n",
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" return message.content[0].text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "01395200-8ae9-41f8-9a04-701624d3fd26",
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"metadata": {},
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"outputs": [],
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"source": [
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"call_claude()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6e95b818-6daf-451e-9950-ecf5ab547bae",
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"metadata": {},
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"outputs": [],
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"source": [
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"def call_gemini():\n",
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" messages = [{\"role\": \"system\", \"content\": gemini_system}]\n",
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" for gpt_message, claude_message, gemini_message in zip(gpt_messages, claude_messages, gemini_messages):\n",
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" messages.append({\"role\": \"user\", \"content\": gpt_message})\n",
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" messages.append({\"role\": \"user\", \"content\": claude_message})\n",
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" messages.append({\"role\": \"assistant\", \"content\": gemini_message}) \n",
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" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
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" messages.append({\"role\": \"user\", \"content\": claude_messages[-1]})\n",
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"\n",
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" response = gemini.chat.completions.create(\n",
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" model=gemini_model,\n",
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" messages=messages\n",
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" )\n",
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" return response.choices[0].message.content"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b83c0c0e-5c80-4499-9ca6-d621dca34ddb",
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"metadata": {},
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"outputs": [],
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"source": [
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"call_gemini()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0275b97f-7f90-4696-bbf5-b6642bd53cbd",
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"metadata": {},
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"outputs": [],
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"source": [
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"gpt_messages = [\"Ahoy men\"]\n",
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"claude_messages = [\"Hello\"]\n",
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"gemini_messages = [\"Ahoy! Has seen the white whale?\"]\n",
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"\n",
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"print(f\"Flask:\\n{gpt_messages[0]}\\n\")\n",
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"print(f\"Starbuck:\\n{claude_messages[0]}\\n\")\n",
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"print(f\"Ishmail:\\n{gemini_messages[0]}\\n\")\n",
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"\n",
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"for i in range(5):\n",
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" gpt_next = call_gpt()\n",
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" print(f\"Flask:\\n{gpt_next}\\n\")\n",
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" gpt_messages.append(gpt_next)\n",
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" \n",
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" claude_next = call_claude()\n",
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" print(f\"Starbuck:\\n{claude_next}\\n\")\n",
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" claude_messages.append(claude_next)\n",
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"\n",
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" gemini_next = call_gemini()\n",
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" print(f\"Ishmail:\\n{gpt_next}\\n\")\n",
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" gemini_messages.append(gemini_next)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c23224f6-7008-44ed-a57f-718975f4e291",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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