204 lines
6.6 KiB
Plaintext
204 lines
6.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6e19458c-4b0e-40f6-bd4f-4d9c80ea671b",
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"metadata": {},
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"source": [
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"# End of Week 1 - Exercise - Using Gemini API with GenAI SDK"
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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": "f1a125bb-737f-41a5-8dd1-626cd8efe6e2",
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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 google import genai\n",
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"from google.genai import types\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": "acf37451-3732-455b-a906-87f66053b018",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load API Key - For Gemini it automatically takes the api key from env file if we save the key using GOOGLE_API_KEY keyword\n",
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"load_dotenv(override=True)\n",
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"api_key = os.getenv('GOOGLE_API_KEY')\n",
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"\n",
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"if not api_key:\n",
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" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
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"elif api_key.strip() != api_key:\n",
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" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
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"else:\n",
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" print(\"API key found and looks good so far!\")"
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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": "4c2fccf9-e419-431e-97fc-a42fcf67c633",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Initialze Google Client\n",
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"# Just to make it explicit i have used the api_key parameter but thats optional and genai.client automatically takes from .env file\n",
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"\n",
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"try:\n",
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" client = genai.Client(api_key=api_key)\n",
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" print(\"Google GenAI Client initialized successfully!\")\n",
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"except Exception as e:\n",
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" print(f\"Error initializing GenAI Client: {e}\")\n",
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" print(\"Ensure your GOOGLE_API_KEY is correctly set as an environment variable.\")\n",
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" exit()"
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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": "5b918afd-ed3b-49d1-85f1-6e549faec66e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get list of models\n",
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"print(\"List of models that support generateContent:\\n\")\n",
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"for m in client.models.list():\n",
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" for action in m.supported_actions:\n",
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" if action == \"generateContent\":\n",
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" print(m.name)"
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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": "791da71e-35a5-4a15-90c7-93ae22e40232",
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"metadata": {},
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"outputs": [],
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"source": [
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"MODEL_GEMINI = 'gemini-2.5-flash-preview-05-20'"
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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": "2a536e25-060e-4f93-bbd7-d80195620bba",
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"metadata": {},
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"outputs": [],
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"source": [
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"# System Definitions\n",
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"\n",
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"system_instruction_prompt = (\n",
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" \"You are an expert Python programming assistant. Your goal is to identify common coding errors, suggest improvements for readability and efficiency,and provide corrected code snippets.\\\n",
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" Always format code blocks using Markdown.\\\n",
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" Be concise but thorough. Focus on the provided code and context.\"\n",
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")\n",
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"\n",
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"generate_content_config = types.GenerateContentConfig(system_instruction=system_instruction_prompt)"
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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": "2fc2a778-f175-44ec-9535-f81deeca7f1a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Main program to get user input and then use model to respond.\n",
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"\n",
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"MAX_HISTORY_MESSAGES = 6\n",
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"conversation_contents = []\n",
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"\n",
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"print(\"\\n--- Start Chat with Gemini Python Assistant ---\")\n",
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"print(\"Type 'Done' to exit the conversation.\")\n",
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"\n",
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"while True:\n",
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" user_input = input(\"You: \").strip()\n",
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"\n",
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" if user_input.lower() == \"done\": \n",
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" print(\"\\nExiting chat. Goodbye!\")\n",
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" break \n",
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"\n",
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" if not user_input: \n",
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" print(\"Please enter a question or 'Done' to exit.\")\n",
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" continue\n",
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" \n",
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" try:\n",
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" user_message_content = types.Content(\n",
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" role=\"user\",\n",
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" parts=[types.Part.from_text(text=user_input)]\n",
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" ) \n",
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" \n",
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" conversation_contents.append(user_message_content) \n",
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" \n",
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" stream_response = client.models.generate_content_stream(\n",
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" model=MODEL_GEMINI,\n",
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" contents=conversation_contents,\n",
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" config=generate_content_config,\n",
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" )\n",
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" \n",
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" model_full_response_text = \"**Gemini:**\\n\\n\"\n",
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" current_display_handle = display(Markdown(\"\"), display_id=True)\n",
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" \n",
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" \n",
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" for chunk in stream_response:\n",
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" chunk_text = chunk.text or ''\n",
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" model_full_response_text += chunk_text\n",
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" update_display(Markdown(model_full_response_text), display_id=current_display_handle.display_id)\n",
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" \n",
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" # Add Model's FULL Response to Conversation History\n",
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" model_message_content = types.Content(\n",
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" role=\"model\",\n",
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" parts=[types.Part.from_text(text=model_full_response_text.removeprefix(\"**Gemini:**\\n\\n\"))]\n",
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" )\n",
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" \n",
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" conversation_contents.append(model_message_content)\n",
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" \n",
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" conversation_contents = conversation_contents[-MAX_HISTORY_MESSAGES:] \n",
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"\n",
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" except Exception as e:\n",
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" print(f\"\\nAn error occurred during interaction: {e}\")\n",
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" if conversation_contents:\n",
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" conversation_contents.pop()\n",
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" print(\"Please try asking your question again or type 'Done' to exit.\")\n",
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" continue "
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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": "a86c3e5b-516b-42dc-994f-9dfa75c610cc",
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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": "3.12.10"
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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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