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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🌊 DeepSeek Integration i Google Colab\n",
"\n",
"Dette notebook viser hvordan du bruger DeepSeek i Google Colab/Antigravity\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 📦 Step 1: Installer Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install OpenAI SDK (DeepSeek er kompatibel)\n",
"!pip install openai --quiet\n",
"print(\"✅ OpenAI SDK installeret!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🔑 Step 2: Konfigurer API Key"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import openai\n",
"from openai import OpenAI\n",
"\n",
"# Din DeepSeek API key\n",
"DEEPSEEK_API_KEY = \"sk-a3f8e6b48271466b981396dc97fd904a\"\n",
"\n",
"# Opret client\n",
"client = OpenAI(\n",
" api_key=DEEPSEEK_API_KEY,\n",
" base_url=\"https://api.deepseek.com/v1\"\n",
")\n",
"\n",
"print(\"✅ DeepSeek client konfigureret!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 💬 Step 3: Simple Chat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def chat_with_deepseek(prompt, temperature=0.7, max_tokens=2000):\n",
" \"\"\"\n",
" Chat med DeepSeek\n",
" \"\"\"\n",
" response = client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": prompt}\n",
" ],\n",
" temperature=temperature,\n",
" max_tokens=max_tokens\n",
" )\n",
" \n",
" return response.choices[0].message.content\n",
"\n",
"# Test det\n",
"response = chat_with_deepseek(\"Hej DeepSeek! Sig hej på dansk.\")\n",
"print(\"🤖 DeepSeek:\", response)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🎯 Step 4: Praktiske Eksempler"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Eksempel 1: Code Generation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"code_prompt = \"\"\"\n",
"Skriv en Python funktion der:\n",
"1. Tager en liste af tal\n",
"2. Fjerner duplikater\n",
"3. Sorterer listen\n",
"4. Returnerer resultat\n",
"\n",
"Inkluder docstring og type hints.\n",
"\"\"\"\n",
"\n",
"code = chat_with_deepseek(code_prompt, temperature=0.3)\n",
"print(\"💻 Genereret kode:\")\n",
"print(code)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Eksempel 2: Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_prompt = \"\"\"\n",
"Jeg har følgende data om website traffic:\n",
"- Mandag: 1500 besøg\n",
"- Tirsdag: 1800 besøg\n",
"- Onsdag: 1200 besøg\n",
"- Torsdag: 2100 besøg\n",
"- Fredag: 2500 besøg\n",
"\n",
"Analyser dataen og giv insights på dansk.\n",
"\"\"\"\n",
"\n",
"analysis = chat_with_deepseek(data_prompt)\n",
"print(\"📊 Analyse:\")\n",
"print(analysis)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Eksempel 3: Multi-turn Conversation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"conversation = [\n",
" {\"role\": \"system\", \"content\": \"Du er en hjælpsom dansk programmerings-assistent.\"},\n",
" {\"role\": \"user\", \"content\": \"Hvad er forskellen på list og tuple i Python?\"}\n",
"]\n",
"\n",
"# First message\n",
"response1 = client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=conversation\n",
")\n",
"\n",
"answer1 = response1.choices[0].message.content\n",
"print(\"Q1: Hvad er forskellen på list og tuple?\")\n",
"print(\"A1:\", answer1)\n",
"print()\n",
"\n",
"# Add to conversation\n",
"conversation.append({\"role\": \"assistant\", \"content\": answer1})\n",
"conversation.append({\"role\": \"user\", \"content\": \"Kan du give et konkret eksempel?\"})\n",
"\n",
"# Second message\n",
"response2 = client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=conversation\n",
")\n",
"\n",
"answer2 = response2.choices[0].message.content\n",
"print(\"Q2: Kan du give et konkret eksempel?\")\n",
"print(\"A2:\", answer2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🚀 Step 5: Advanced - Stream Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"\n",
"def stream_chat(prompt):\n",
" \"\"\"\n",
" Stream response fra DeepSeek\n",
" \"\"\"\n",
" stream = client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
" stream=True\n",
" )\n",
" \n",
" print(\"🤖 DeepSeek (streaming): \", end=\"\")\n",
" \n",
" for chunk in stream:\n",
" if chunk.choices[0].delta.content is not None:\n",
" content = chunk.choices[0].delta.content\n",
" print(content, end=\"\", flush=True)\n",
" \n",
" print() # New line\n",
"\n",
"# Test streaming\n",
"stream_chat(\"Fortæl mig en kort historie om en robot der lærer at kode.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🎨 Step 6: Interactive Chat Interface"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import display, Markdown, clear_output\n",
"import ipywidgets as widgets\n",
"\n",
"# Chat history\n",
"chat_history = []\n",
"\n",
"# UI Components\n",
"output = widgets.Output()\n",
"text_input = widgets.Textarea(\n",
" placeholder='Skriv din besked...',\n",
" layout=widgets.Layout(width='100%', height='80px')\n",
")\n",
"send_button = widgets.Button(\n",
" description='Send til DeepSeek',\n",
" button_style='success',\n",
" layout=widgets.Layout(width='200px')\n",
")\n",
"clear_button = widgets.Button(\n",
" description='Ryd chat',\n",
" button_style='warning',\n",
" layout=widgets.Layout(width='150px')\n",
")\n",
"\n",
"def send_message(b):\n",
" user_message = text_input.value\n",
" if not user_message.strip():\n",
" return\n",
" \n",
" # Add user message\n",
" chat_history.append({\"role\": \"user\", \"content\": user_message})\n",
" \n",
" # Get response\n",
" response = client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=chat_history\n",
" )\n",
" \n",
" assistant_message = response.choices[0].message.content\n",
" chat_history.append({\"role\": \"assistant\", \"content\": assistant_message})\n",
" \n",
" # Display\n",
" with output:\n",
" clear_output()\n",
" for msg in chat_history:\n",
" if msg[\"role\"] == \"user\":\n",
" display(Markdown(f\"**👤 Du:** {msg['content']}\"))\n",
" else:\n",
" display(Markdown(f\"**🤖 DeepSeek:** {msg['content']}\"))\n",
" print(\"---\")\n",
" \n",
" # Clear input\n",
" text_input.value = \"\"\n",
"\n",
"def clear_chat(b):\n",
" global chat_history\n",
" chat_history = []\n",
" with output:\n",
" clear_output()\n",
" display(Markdown(\"*Chat ryddet. Start en ny samtale!*\"))\n",
"\n",
"send_button.on_click(send_message)\n",
"clear_button.on_click(clear_chat)\n",
"\n",
"# Display UI\n",
"display(widgets.VBox([\n",
" widgets.HTML(\"<h2>🌊 Chat med DeepSeek</h2>\"),\n",
" output,\n",
" text_input,\n",
" widgets.HBox([send_button, clear_button])\n",
"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 📊 Step 7: Token Usage & Cost Tracking"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def chat_with_stats(prompt):\n",
" \"\"\"\n",
" Chat og vis token statistik\n",
" \"\"\"\n",
" response = client.chat.completions.create(\n",
" model=\"deepseek-chat\",\n",
" messages=[{\"role\": \"user\", \"content\": prompt}]\n",
" )\n",
" \n",
" content = response.choices[0].message.content\n",
" usage = response.usage\n",
" \n",
" # DeepSeek pricing (per 1M tokens)\n",
" input_cost = 0.27\n",
" output_cost = 1.1\n",
" \n",
" total_cost = (\n",
" (usage.prompt_tokens / 1_000_000) * input_cost +\n",
" (usage.completion_tokens / 1_000_000) * output_cost\n",
" )\n",
" \n",
" print(\"🤖 DeepSeek:\", content)\n",
" print()\n",
" print(\"📊 Statistik:\")\n",
" print(f\" Input tokens: {usage.prompt_tokens:,}\")\n",
" print(f\" Output tokens: {usage.completion_tokens:,}\")\n",
" print(f\" Total tokens: {usage.total_tokens:,}\")\n",
" print(f\" 💰 Estimated cost: ${total_cost:.6f}\")\n",
"\n",
"# Test\n",
"chat_with_stats(\"Forklar machine learning på dansk i 3 sætninger.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 🎉 Du er klar!\n",
"\n",
"Nu kan du bruge DeepSeek i Google Colab/Antigravity!\n",
"\n",
"### Næste skridt:\n",
"- Eksperimenter med forskellige prompts\n",
"- Prøv forskellige temperature settings\n",
"- Byg din egen chat application\n",
"- Integrer med dine data\n",
"\n",
"**Tips:**\n",
"- DeepSeek er særligt god til coding tasks\n",
"- Brug lav temperature (0.3) for code generation\n",
"- Brug høj temperature (0.8) for kreativt indhold\n"
]
}
],
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"file_extension": ".py",
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