Merge pull request #553 from kunalBrahma/community-contributions-branch
Community contributions branch
This commit is contained in:
Ed Donner
GitHub
230
week1/community-contributions/day1-BitcoinMarketPrediction.ipynb
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230
week1/community-contributions/day1-BitcoinMarketPrediction.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": "7db973a2-c95e-4939-a0d7-b54edec4d2cf",
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"metadata": {},
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"source": [
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"# Bitcoin Market Prediction uisng CoinmarketCap\n",
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"An AI-powered project using historical CoinMarketCap data to predict Bitcoin price trends and offer actionable insights for traders."
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]
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},
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{
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"cell_type": "markdown",
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"id": "b792b517-bbc8-4e2c-bff2-45fad1a784dc",
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"metadata": {},
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"source": [
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"## Imports"
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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": "51523d62-825a-4a15-aec2-7c910beb5fda",
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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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"import requests\n",
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"from dotenv import load_dotenv\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display\n",
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"from openai import OpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e3816b0-4557-4225-bfb9-9933d813548a",
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"metadata": {},
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"source": [
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"## .env configuration"
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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": "02be59e7-01cc-41b5-88c3-a47860570078",
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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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"api_key = os.getenv('OPENAI_API_KEY')\n",
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"\n",
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"# Check the 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 not api_key.startswith(\"sk-proj-\"):\n",
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" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\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": "markdown",
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"id": "3fc32555-ea4e-45fe-ad44-9dbf4441afd1",
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"metadata": {},
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"source": [
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"### This line creates an authenticated OpenAI client instance, used to make API requests in your code."
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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": "0845c687-6610-4f83-89e8-fb94bc47ddd2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from openai import OpenAI\n",
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"openai = OpenAI(api_key=api_key)"
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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": "d140db1a-dd72-4986-8f38-09f8d8f97b00",
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"metadata": {},
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"outputs": [],
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"source": [
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"headers = {\n",
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" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
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"}\n",
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"\n",
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"class Website:\n",
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"\n",
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" def __init__(self, url):\n",
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" \"\"\"\n",
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" Create this Website object from the given url using the BeautifulSoup library\n",
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" \"\"\"\n",
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" self.url = url\n",
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" response = requests.get(url, headers=headers)\n",
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" soup = BeautifulSoup(response.content, 'html.parser')\n",
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" self.title = soup.title.string if soup.title else \"No title found\"\n",
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" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
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" irrelevant.decompose()\n",
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" self.text = soup.body.get_text(separator=\"\\n\", strip=True)"
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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": "fdc96768-94a8-4a08-acf1-32a62b699b94",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_prompt = \"\"\"\n",
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"You are an intelligent assistant specialized in Bitcoin market prediction. Your tasks are:\n",
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"\n",
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"- Collect, preprocess, and analyze historical Bitcoin price and volume data sourced from CoinMarketCap historical data tables or API.\n",
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"- Extract relevant time series and technical features from OHLC (open, high, low, close) and volume data.\n",
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"- Use machine learning or statistical models to forecast future Bitcoin price trends.\n",
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"- Output clear, concise, and actionable insights, focusing on predicted price direction and potential trading signals.\n",
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"- Ensure all data collection respects CoinMarketCap’s terms of service.\n",
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"- Present findings in user-friendly language, explaining prediction confidence and market risks.\n",
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"- Continuously improve prediction accuracy through back-testing on updated datasets.\n",
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"\n",
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"\"\"\"\n"
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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": "7d39e983-5b65-4de1-bdf0-e4239c3eb03f",
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"metadata": {},
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"outputs": [],
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"source": [
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"def user_prompt_for(website):\n",
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" user_prompt = f\"You are analyzing historical Bitcoin market data from the webpage titled '{website.title}'.\\n\"\n",
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" user_prompt += (\n",
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" \"The data includes daily open, high, low, close prices, trading volume, \"\n",
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" \"and market capitalization presented in a table format.\\n\"\n",
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" \"Please provide a clear and concise analysis in Markdown format, focusing on recent trends, \"\n",
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" \"price movements, volatility, and any insights that could help forecast Bitcoin price directions.\\n\"\n",
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" \"If possible, include technical indicators, significant patterns, or notable market events mentioned in the data.\\n\\n\"\n",
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" )\n",
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" user_prompt += website.text\n",
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" return user_prompt\n"
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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": "d3d41ed3-4753-49f2-b51f-37e8be43102c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def messages_for(website):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt_for(website)}\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": "0eb99fcf-75a2-41b8-bf53-568f94264438",
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"metadata": {},
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"outputs": [],
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"source": [
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"# And now: call the OpenAI API. You will get very familiar with this!\n",
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"\n",
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"def summarize(url):\n",
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" website = Website(url)\n",
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" response = openai.chat.completions.create(\n",
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" model = \"gpt-4o-mini\",\n",
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" messages = messages_for(website)\n",
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" )\n",
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" return response.choices[0].message.content\n",
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"\n",
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"# A function to display this nicely in the Jupyter output, using markdown\n",
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"\n",
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"def display_summary(summary): \n",
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" display(Markdown(summary))"
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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": "a0e57921-5132-40c6-834b-03a11a96425c",
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"metadata": {},
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"outputs": [],
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"source": [
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"url = \"https://coinmarketcap.com/currencies/bitcoin/historical-data/3\"\n",
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"summary = summarize(url)\n",
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"display_summary(summary)"
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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": "19d9b69a-6493-402d-a0b4-a486c322c816",
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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": "3.11.13"
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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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105
week1/community-contributions/day2_Ollama_Solution.ipynb
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105
week1/community-contributions/day2_Ollama_Solution.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": "663695bd-d1f2-4acf-8669-02d9f75f1bf4",
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"metadata": {},
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"source": [
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"# Day 2: Ollama Solution for Website Summarization\n",
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"### Building and Deploying Website Summarization Tools 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": "112ef04a-136e-4e65-b94e-8674a64606ed",
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"from bs4 import BeautifulSoup\n",
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"from IPython.display import Markdown, display\n",
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"\n",
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"OLLAMA_API = \"http://localhost:11434/api/chat\"\n",
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"HEADERS = {\"Content-Type\": \"application/json\"}\n",
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"MODEL = \"llama3.2\"\n",
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"\n",
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"headers = {\n",
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" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
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"}\n",
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"\n",
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"class Website:\n",
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"\n",
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" def __init__(self, url):\n",
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" \"\"\"\n",
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" Create this Website object from the given url using the BeautifulSoup library\n",
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" \"\"\"\n",
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" self.url = url\n",
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" response = requests.get(url, headers=headers)\n",
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" soup = BeautifulSoup(response.content, 'html.parser')\n",
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" self.title = soup.title.string if soup.title else \"No title found\"\n",
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" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
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" irrelevant.decompose()\n",
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" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
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"\n",
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"\n",
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"system_prompt = \"You are an assistant that analyzes the contents of a website \\\n",
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"and provides a short summary, ignoring text that might be navigation related. \\\n",
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"Respond in markdown.\"\n",
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"\n",
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"def user_prompt_for(website):\n",
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" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
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" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
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"please provide a short summary of this website in markdown. \\\n",
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"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
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" user_prompt += website.text\n",
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" return user_prompt\n",
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"\n",
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"def messages_for(website):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_prompt},\n",
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" {\"role\": \"user\", \"content\": user_prompt_for(website)}\n",
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" ]\n",
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"\n",
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"url = \"https://sitemakerlab.com/\" \n",
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"site = Website(url)\n",
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"messages = messages_for(site)\n",
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"\n",
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"def summarize(url):\n",
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" website = Website(url)\n",
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" response = ollama_via_openai.chat.completions.create(\n",
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" model = MODEL,\n",
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" messages = messages_for(website)\n",
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" )\n",
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" return response.choices[0].message.content\n",
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"\n",
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"def display_summary(url):\n",
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" summary = summarize(url)\n",
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" display(Markdown(summary))\n",
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"\n",
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"display_summary(\"https://edwarddonner.com\")\n"
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]
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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": "3.11.13"
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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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