Added my additional exercise for W2D1 to community-contributions
This commit is contained in:
sparsh_thakur
196
week2/community-contributions/w2d1exercise.ipynb
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196
week2/community-contributions/w2d1exercise.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ec2e81cd-2172-4816-bf44-f29312b8a4bd",
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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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"import google.generativeai as genai\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": "a558dfa4-9496-48ba-b0f5-b0c731adc7b8",
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"metadata": {},
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"outputs": [],
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"source": [
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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": "dc7c2cda-a5d1-4930-87f2-e06485d6b2bd",
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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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"\n",
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"claude = anthropic.Anthropic()\n",
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"\n",
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"genai.configure()"
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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": "3eb32aec-ec93-4563-bd88-0d48d2471884",
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"metadata": {},
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"outputs": [],
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"source": [
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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-exp\"\n",
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"\n",
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"gpt_system = \"You are a chatbot who is sarcastic; \\\n",
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"you have your speculations about anything in the conversation and you challenge everything in funny way.\\\n",
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"You have to be a part of a group discussion and put forward your points about the topic\\\n",
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"full-stack developers vs specialised developer. Keep your points short and precise.\"\n",
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"\n",
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"claude_system = \"You are a very polite, courteous chatbot. You try to agree with \\\n",
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"everything the other person says, or find common ground. If the other person is argumentative, \\\n",
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"you try to calm them down and keep chatting.You have to be a part of a group discussion and put forward your points\\\n",
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"about the topic full-stack developers vs specialised developer. Keep your points short and precise.\"\n",
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"\n",
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"gemini_system = \"You are a very rational thinker and don't like beating around the bush about the topic of discussion.\\\n",
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"You have to be a part of a group discussion and put forward your points\\\n",
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"about the topic full-stack developers vs specialised developer\\\n",
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"Keep your points short and precise.\"\n",
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"\n",
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"gpt_messages = [\"Hi there\"]\n",
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"claude_messages = [\"Hi\"]\n",
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"gemini_messages = [\"Hello to all\"]"
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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": "e27252cf-05f5-4989-85ef-94e6802c5db9",
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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, claude, gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
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" messages.append({\"role\": \"assistant\", \"content\": gpt})\n",
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" messages.append({\"role\": \"user\", \"content\": claude})\n",
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" messages.append({\"role\": \"assistant\", \"content\": gemini})\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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" max_tokens=500 # Add max_tokens to meet API requirement\n",
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" )\n",
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" return completion.choices[0].message.content\n",
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"\n",
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"# Function to call Claude\n",
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"def call_claude():\n",
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" messages = []\n",
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" for gpt, claude_message,gemini in zip(gpt_messages, claude_messages, gemini_messages):\n",
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" messages.append({\"role\": \"user\", \"content\": gpt})\n",
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" messages.append({\"role\": \"assistant\", \"content\": claude_message})\n",
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" messages.append({\"role\": \"assistant\", \"content\": gemini})\n",
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" messages.append({\"role\": \"user\", \"content\": gpt_messages[-1]})\n",
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" message = claude.messages.create(\n",
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" model=claude_model,\n",
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" max_tokens=500,\n",
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" messages=messages\n",
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" )\n",
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" return message.content[0].text\n",
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"\n",
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"# Function to call Gemini\n",
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"def call_gemini():\n",
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" # Create the Gemini model instance\n",
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" gemini_model_instance = genai.GenerativeModel(\n",
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" model_name=gemini_model, # Specify the model name here\n",
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" system_instruction=gemini_system # Provide the system instruction\n",
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" )\n",
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" \n",
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" # Prepare conversation history with separate names to avoid overwriting\n",
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" gemini_messages_combined = []\n",
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" for gpt, claude, gemini_msg in zip(gpt_messages, claude_messages, gemini_messages):\n",
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" gemini_messages_combined.append({\"role\": \"assistant\", \"content\": gpt})\n",
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" gemini_messages_combined.append({\"role\": \"user\", \"content\": claude})\n",
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" gemini_messages_combined.append({\"role\": \"assistant\", \"content\": gemini_msg})\n",
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" \n",
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" # Generate content based on the conversation history\n",
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" gemini_response = gemini_model_instance.generate_content(\"\".join([msg[\"content\"] for msg in gemini_messages_combined]))\n",
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" \n",
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" return gemini_response.text\n",
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"\n",
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"# Initial print\n",
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"print(f\"Gemini:\\n{gemini_messages[0]}\\n\")\n",
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"print(f\"GPT:\\n{gpt_messages[0]}\\n\")\n",
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"print(f\"Claude:\\n{claude_messages[0]}\\n\")\n",
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"\n",
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"# Main loop to generate conversation\n",
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"for i in range(3):\n",
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" gpt_next = call_gpt()\n",
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" print(f\"GPT:\\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\"Claude:\\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\"Gemini:\\n{gemini_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": "52f43794-a20a-4b9a-a18d-6f363b8dc27d",
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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": "3.11.11"
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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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