From f87c3a942a38d3c27cbb4e618450c6843859bd79 Mon Sep 17 00:00:00 2001 From: Niraj Parmar Date: Thu, 17 Oct 2024 02:30:44 +0530 Subject: [PATCH 1/6] githubscraper --- .../github_repo_scraper/github_scraper.ipynb | 2168 +++++++++++++++++ .../github_scraper.ipynb - Colab.pdf | Bin 0 -> 633384 bytes .../github_scraper.ipynb.-.Colab.pdf | Bin 0 -> 633384 bytes 3 files changed, 2168 insertions(+) create mode 100644 web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb create mode 100644 web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb - Colab.pdf create mode 100644 web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb.-.Colab.pdf diff --git a/web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb b/web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb new file mode 100644 index 00000000..ce1e4950 --- /dev/null +++ b/web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb @@ -0,0 +1,2168 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "### Web Scraper of Trending Repositories on Github" + ], + "metadata": { + "id": "yK7-Rd374TiB" + } + }, + { + "cell_type": "code", + "source": [ + "import requests\n", + "from bs4 import BeautifulSoup\n", + "import pandas as pd\n", + "import base64" + ], + "metadata": { + "id": "96DYZj8q4qNK" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "topics_url = 'https://github.com/topics'" + ], + "metadata": { + "id": "URtvE66M4sHb" + }, + "execution_count": 62, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "response = requests.get(topics_url)\n", + "content = response.text" + ], + "metadata": { + "id": "rwduGUCB4zFh" + }, + "execution_count": 63, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "content[:1000]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 123 + }, + "id": "I--FZfK0KVbN", + "outputId": "5c3fb96b-90ac-4a1f-f879-0c95806b37da" + }, + "execution_count": 64, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'\\n\\n\\n\\n\\n\\n\\n \\n \\n \\n \\n \\n \\n \\n \\n\\n \\n\\n 3D

,\n", + "

Ajax

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Algorithm

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Amp

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Android

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Angular

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Ansible

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API

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Arduino

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ASP.NET

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Awesome Lists

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Amazon Web Services

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Azure

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Babel

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Bash

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Bootstrap

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Crystal

]" + ] + }, + "metadata": {}, + "execution_count": 67 + } + ] + }, + { + "cell_type": "code", + "source": [ + "a = topic_titles[0].parent.parent.find_all('a',{'class':'no-underline flex-grow-0'}, href=True)\n", + "a[0]['href']" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 36 + }, + "id": "JkiTvhzN6JOF", + "outputId": "13c3da33-176a-4dff-e84b-4456a5bc1b79" + }, + "execution_count": 68, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'/topics/3d'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 68 + } + ] + }, + { + "cell_type": "code", + "source": [ + "topic_titles_ = []\n", + "topic_urls = []\n", + "for tag in topic_titles :\n", + " topic_titles_.append(tag.text)\n", + " topic_urls.append(\"https://github.com\"+tag.parent.parent.find_all('a',{'class':'no-underline flex-grow-0'}, href=True)[0]['href'])\n", + "topic_urls\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GBxxo58Q8VOG", + "outputId": "e8394cf6-5c13-4c2d-ac87-07b752a4c98f" + }, + "execution_count": 69, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['https://github.com/topics/3d',\n", + " 'https://github.com/topics/ajax',\n", + " 'https://github.com/topics/algorithm',\n", + " 'https://github.com/topics/amphp',\n", + " 'https://github.com/topics/android',\n", + " 'https://github.com/topics/angular',\n", + " 'https://github.com/topics/ansible',\n", + " 'https://github.com/topics/api',\n", + " 'https://github.com/topics/arduino',\n", + " 'https://github.com/topics/aspnet',\n", + " 'https://github.com/topics/awesome',\n", + " 'https://github.com/topics/aws',\n", + " 'https://github.com/topics/azure',\n", + " 'https://github.com/topics/babel',\n", + " 'https://github.com/topics/bash',\n", + " 'https://github.com/topics/bitcoin',\n", + " 'https://github.com/topics/bootstrap',\n", + " 'https://github.com/topics/bot',\n", + " 'https://github.com/topics/c',\n", + " 'https://github.com/topics/chrome',\n", + " 'https://github.com/topics/chrome-extension',\n", + " 'https://github.com/topics/cli',\n", + " 'https://github.com/topics/clojure',\n", + " 'https://github.com/topics/code-quality',\n", + " 'https://github.com/topics/code-review',\n", + " 'https://github.com/topics/compiler',\n", + " 'https://github.com/topics/continuous-integration',\n", + " 'https://github.com/topics/cpp',\n", + " 'https://github.com/topics/cryptocurrency',\n", + " 'https://github.com/topics/crystal']" + ] + }, + "metadata": {}, + "execution_count": 69 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df = pd.DataFrame({'topic_title': topic_titles_, 'topic_url': topic_urls})" + ], + "metadata": { + "id": "e2wCmQOI9HIC" + }, + "execution_count": 70, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "ebpMu1c99aM7", + "outputId": "34cd8b84-8cfc-4afb-9f46-7a24024fb787" + }, + "execution_count": 71, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " topic_title topic_url\n", + "0 3D https://github.com/topics/3d\n", + "1 Ajax https://github.com/topics/ajax\n", + "2 Algorithm https://github.com/topics/algorithm\n", + "3 Amp https://github.com/topics/amphp\n", + "4 Android https://github.com/topics/android" + ], + "text/html": [ + "\n", + "
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0flutterflutter165000https://github.com/flutter/flutter
1facebookreact-native119000https://github.com/facebook/react-native
2justjavacfree-programming-books-zh_CN111000https://github.com/justjavac/free-programming-...
3Genymobilescrcpy110000https://github.com/Genymobile/scrcpy
4Hack-with-GithubAwesome-Hacking83900https://github.com/Hack-with-Github/Awesome-Ha...
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or

)\n", + "title = soup.find('h1').get_text(strip=True)\n", + "\n", + "# Extract the description (first

after title or within a specific section)\n", + "description = soup.find('p').get_text(strip=True)\n", + "\n", + "# Extract all paragraphs (for main content)\n", + "content_paragraphs = soup.find_all('p')\n", + "content = [para.get_text(strip=True) for para in content_paragraphs]\n", + "\n", + "# Extract links from the content\n", + "links = []\n", + "for link in soup.find_all('a', href=True):\n", + " links.append((link.get_text(strip=True), link['href']))\n", + "\n" + ], + "metadata": { + "id": "umk_RfvdGNqh" + }, + "execution_count": 76, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Display the extracted content\n", + "print(\"Title:\", title)\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OPIcuUL1P3oa", + "outputId": "b3ffa345-b83c-41bf-bff2-ce86cc1b869c" + }, + "execution_count": 77, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Title: Search code, repositories, users, issues, pull requests...\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(\"Content:\", content)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S8gLnpZzP50m", + "outputId": "93a7ffd2-1ed2-405b-d304-a8cfb9e70587" + }, + "execution_count": 78, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Content: ['We read every piece of feedback, and take your input very seriously.', 'To see all available qualifiers, see ourdocumentation.', 'An image loading and caching library for Android focused on smooth scrolling', \"|View Glide's documentation|简体中文文档|Report an issue with Glide\", 'Glide is a fast and efficient open source media management and image loading framework for Android that wraps media\\ndecoding, memory and disk caching, and resource pooling into a simple and easy to use interface.', '', \"Glide supports fetching, decoding, and displaying video stills, images, and animated GIFs. Glide includes a flexible API\\nthat allows developers to plug in to almost any network stack. By default Glide uses a customHttpUrlConnectionbased\\nstack, but also includes utility libraries plug in to Google's Volley project or Square's OkHttp library instead.\", \"Glide's primary focus is on making scrolling any kind of a list of images as smooth and fast as possible, but Glide is\\nalso effective for almost any case where you need to fetch, resize, and display a remote image.\", \"For detailed instructions and requirements, see Glide'sdownload and setup docs page.\", \"You can download a jar from GitHub'sreleases page.\", 'Or use Gradle:', 'Or Maven:', 'For info on using the bleeding edge, see theSnapshotsdocs page.', 'The specific rules arealready bundledinto the aar which can be interpreted by R8 automatically', 'Check out thedocumentationfor pages on a variety of topics, and see thejavadocs.', 'For Glide v3, see thewiki.', 'Simple use cases will look something like this:', 'Version 4 is now released and stable. Updates are released periodically with new features and bug fixes.', 'Comments/bugs/questions/pull requests are always welcome! Please readCONTRIBUTING.mdon how to report issues.', 'If you need to support older versions of Android, consider staying onGlide v3, which works on API 10, but is not actively maintained.', 'Building Glide with gradle is fairly straight forward:', 'Note: Make sure yourAndroid SDKhas theAndroid Support Repositoryinstalled, and that your$ANDROID_HOMEenvironment\\nvariable is pointing at the SDK or add alocal.propertiesfile in the root project with asdk.dir=...line.', 'Follow the steps in theBuildsection to set up the project and then:', 'You may also find precompiled APKs on thereleases page.', \"Follow the steps in theBuildsection to setup the project and then edit the files however you wish.Android Studiocleanly imports both Glide's source and tests and is the recommended way to work with Glide.\", 'To open the project in Android Studio:', 'For more details, see theContributing docs page.', \"To report a specific problem or feature request,open a new issue on Github. For questions, suggestions, or\\nanything else, emailGlide's discussion group, or join our IRC channel:irc.freenode.net#glide-library.\", \"Before submitting pull requests, contributors must sign Google'sindividual contributor license agreement.\", 'Sam Judd - @sjudd on GitHub, @samajudd on Twitter', 'BSD, part MIT and Apache 2.0. See theLICENSEfile for details.', 'This is not an official Google product.', 'An image loading and caching library for Android focused on smooth scrolling']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(\"Links:\", links)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ul322fF_P8gZ", + "outputId": "eddd5810-7987-4e3e-b321-0a701a9e3cae" + }, + "execution_count": 79, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Links: [('Skip to content', '#start-of-content'), ('', '/'), ('Sign in', '/login?return_to=https%3A%2F%2Fgithub.com%2Fbumptech%2Fglide'), ('GitHub CopilotWrite better code with AI', 'https://github.com/features/copilot'), ('SecurityFind and fix vulnerabilities', 'https://github.com/features/security'), ('ActionsAutomate any workflow', 'https://github.com/features/actions'), ('CodespacesInstant dev environments', 'https://github.com/features/codespaces'), ('IssuesPlan and track work', 'https://github.com/features/issues'), ('Code ReviewManage code changes', 'https://github.com/features/code-review'), ('DiscussionsCollaborate outside of code', 'https://github.com/features/discussions'), ('Code SearchFind more, search less', 'https://github.com/features/code-search'), ('All features', 'https://github.com/features'), ('Documentation', 'https://docs.github.com'), ('GitHub Skills', 'https://skills.github.com'), ('Blog', 'https://github.blog'), ('Enterprise', 'https://github.com/enterprise'), ('Teams', 'https://github.com/team'), ('Startups', 'https://github.com/enterprise/startups'), ('Healthcare', 'https://github.com/solutions/industries/healthcare'), ('Financial services', 'https://github.com/solutions/industries/financial-services'), ('Manufacturing', 'https://github.com/solutions/industries/manufacturing'), ('CI/CD & Automation', 'https://github.com/solutions/ci-cd'), ('DevOps', 'https://github.com/solutions/devops'), ('DevSecOps', 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)\n", + " title_tag = soup.find('h1')\n", + " title = title_tag.get_text(strip=True) if title_tag else \"No title found\"\n", + "\n", + " # Find the first

after the title\n", + " content_paragraphs = soup.find_all('p')\n", + "\n", + " # Extract the description (first

after title or within a specific section)\n", + " description = soup.find('p').get_text(strip=True)\n", + "\n", + " # Extract all paragraphs (for main content)\n", + "\n", + " content = [para.get_text(strip=True) for para in content_paragraphs]\n", + "\n", + " # Extract links from the README content\n", + " links = []\n", + " for link in soup.find_all('a', href=True):\n", + " links.append((link.get_text(strip=True), link['href']))\n", + "\n", + " return title, description, content, links\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "def scrape_trending_repositories(limit=100): # Set a default limit\n", + " topics_url = 'https://github.com/topics'\n", + " response = requests.get(topics_url)\n", + " content = response.text\n", + "\n", + " doc = BeautifulSoup(content, 'html.parser')\n", + " topic_titles = doc.find_all('p', {'class': 'f3 lh-condensed mb-0 mt-1 Link--primary'})\n", + "\n", + " topic_titles_ = []\n", + " topic_urls = []\n", + "\n", + " for tag in topic_titles:\n", + " topic_titles_.append(tag.text)\n", + " topic_urls.append(\"https://github.com\" + tag.parent.parent.find_all('a', {'class': 'no-underline flex-grow-0'}, href=True)[0]['href'])\n", + "\n", + " df_topics = pd.DataFrame({'topic_title': topic_titles_, 'topic_url': topic_urls})\n", + "\n", + " all_repo_data = []\n", + "\n", + " # Loop through each topic URL to get repository info\n", + " for topic_url in topic_urls:\n", + " response = requests.get(topic_url)\n", + " content = BeautifulSoup(response.text, 'html.parser')\n", + " repositories = content.find_all('h3', {'class': \"f3 color-fg-muted text-normal lh-condensed\"})\n", + "\n", + " for repo in repositories:\n", + " if len(all_repo_data) >= limit: # Check if the limit is reached\n", + " break\n", + "\n", + " user = repo.find_all('a')[0].text.strip()\n", + " repo_name = repo.find_all('a')[1].text.strip()\n", + " repo_url = \"https://github.com\" + repo.find_all('a')[1]['href']\n", + "\n", + " # Get the stars (if available)\n", + " star_tag = repo.find_next('span', {'id': 'repo-stars-counter-star'})\n", + " if star_tag:\n", + " star_text = star_tag.text.strip().replace(',', '')\n", + " if 'k' in star_text:\n", + " star_text = star_text.replace('k', '') # Remove the 'k'\n", + " stars = int(float(star_text) * 1000) # Convert to integer after multiplying by 1000\n", + " else:\n", + " stars = int(float(star_text)) # Convert to integer directly\n", + " else:\n", + " stars = 0\n", + "\n", + " # Scrape the README content for the repository\n", + " readme_title, readme_description, readme_content, readme_links = scrape_readme(repo_url)\n", + "\n", + " # Collect all data in a dictionary\n", + " repo_data = {\n", + " 'username': user,\n", + " 'repo': repo_name,\n", + " 'stars': stars,\n", + " 'repo_url': repo_url,\n", + " 'readme_content': readme_content,\n", + "\n", + "\n", + " 'readme_links': readme_links\n", + " }\n", + " all_repo_data.append(repo_data)\n", + "\n", + " return all_repo_data\n" + ], + "metadata": { + "id": "AgwcOtzYYRc7" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_repos = pd.DataFrame(scrape_trending_repositories())\n", + "df_repos.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 3222 + }, + "id": "xIfY2FCDbDFb", + "outputId": "95a98bdf-b716-41b0-a105-290db1a66dd1" + }, + "execution_count": 117, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " username repo stars \\\n", + "0 mrdoob three.js 102000 \n", + "1 pmndrs react-three-fiber 27300 \n", + "2 libgdx libgdx 23300 \n", + "3 BabylonJS Babylon.js 23200 \n", + "4 ssloy tinyrenderer 20400 \n", + "\n", + " repo_url \\\n", + "0 https://github.com/mrdoob/three.js \n", + "1 https://github.com/pmndrs/react-three-fiber \n", + "2 https://github.com/libgdx/libgdx \n", + "3 https://github.com/BabylonJS/Babylon.js \n", + "4 https://github.com/ssloy/tinyrenderer \n", + "\n", + " readme_content \\\n", + "0 [We read every piece of feedback, and take you... \n", + "1 [We read every piece of feedback, and take you... \n", + "2 [We read every piece of feedback, and take you... \n", + "3 [We read every piece of feedback, and take you... \n", + "4 [We read every piece of feedback, and take you... \n", + "\n", + " readme_description \\\n", + "0 We read every piece of feedback, and take your... \n", + "1 We read every piece of feedback, and take your... \n", + "2 We read every piece of feedback, and take your... \n", + "3 We read every piece of feedback, and take your... \n", + "4 We read every piece of feedback, and take your... \n", + "\n", + " readme_links \n", + "0 [(Skip to content, #start-of-content), (, /), ... \n", + "1 [(Skip to content, #start-of-content), (, /), ... \n", + "2 [(Skip to content, #start-of-content), (, /), ... \n", + "3 [(Skip to content, #start-of-content), (, /), ... \n", + "4 [(Skip to content, #start-of-content), (, /), ... " + ], + "text/html": [ + "\n", + "

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0mrdoobthree.js102000https://github.com/mrdoob/three.js[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
1pmndrsreact-three-fiber27300https://github.com/pmndrs/react-three-fiber[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
2libgdxlibgdx23300https://github.com/libgdx/libgdx[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
3BabylonJSBabylon.js23200https://github.com/BabylonJS/Babylon.js[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
4ssloytinyrenderer20400https://github.com/ssloy/tinyrenderer[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
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It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n", + " warnings.warn(\n", + "Your max_length is set to 300, but your input_length is only 201. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=100)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Summary:\n", + "The aim of the project is to create an easy-to-use, lightweight, cross-browser, general-purpose 3D library. Current builds only include a WebGL renderer but WebGPU (experimental), SVG, and CSS3D renderers are also available as addons.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#merge this logic" + ], + "metadata": { + "id": "dQStsnBF1HoH" + }, + "execution_count": 121, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Function to summarize the README content\n", + "def summarize_readme(content):\n", + " if not content:\n", + " return \"No content to summarize.\"\n", + "\n", + " # Join all paragraphs to form a single string\n", + " full_text = \" \".join(content)\n", + "\n", + " # Check if the length of the full text exceeds a reasonable length\n", + " max_length = 1024 # Set a limit for the summarization model\n", + " if len(full_text) > max_length:\n", + " full_text = full_text[:max_length] # Truncate the text\n", + "\n", + " try:\n", + " # Summarize the text using the summarization model\n", + " summary = summarization_model(full_text, max_length=130, min_length=30, do_sample=False)\n", + " return summary[0]['summary_text'] # Extract the summary text\n", + " except Exception as e:\n", + " print(f\"Error during summarization: {e}\")\n", + " return \"Error occurred during summarization.\"\n", + "\n", + "# The rest of your existing code follows here...\n" + ], + "metadata": { + "id": "5JvYcAJZ1_6R" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def scrape_readme(url):\n", + " response = requests.get(url)\n", + "\n", + " # Check if the request was successful\n", + " if response.status_code != 200:\n", + " print(f\"Failed to fetch {url}: {response.status_code}\")\n", + " return None, \"No description found\", [], []\n", + "\n", + " soup = BeautifulSoup(response.text, 'html.parser')\n", + "\n", + " # Extract the title (usually in

)\n", + " title_tag = soup.find('h1')\n", + " title = title_tag.get_text(strip=True) if title_tag else \"No title found\"\n", + "\n", + " # Find the first

after the title\n", + " content_paragraphs = soup.find_all('p')\n", + "\n", + " # Extract the description (first

after title)\n", + " description = soup.find('p').get_text(strip=True) if soup.find('p') else \"No description found\"\n", + "\n", + " # Extract all paragraphs for main content\n", + " content = [para.get_text(strip=True) for para in content_paragraphs]\n", + "\n", + " # Extract links from the README content\n", + " links = []\n", + " for link in soup.find_all('a', href=True):\n", + " links.append((link.get_text(strip=True), link['href']))\n", + "\n", + " return title, description, content, links\n", + "\n" + ], + "metadata": { + "id": "91Ctgwym-POF" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Scrape the trending repositories and extract README summaries\n", + "def scrape_trending_repositories(limit=10): # Set a default limit\n", + " topics_url = 'https://github.com/topics'\n", + " response = requests.get(topics_url)\n", + " content = response.text\n", + "\n", + " doc = BeautifulSoup(content, 'html.parser')\n", + " topic_titles = doc.find_all('p', {'class': 'f3 lh-condensed mb-0 mt-1 Link--primary'})\n", + "\n", + " topic_titles_ = []\n", + " topic_urls = []\n", + "\n", + " for tag in topic_titles:\n", + " topic_titles_.append(tag.text)\n", + " topic_urls.append(\"https://github.com\" + tag.parent.parent.find_all('a', {'class': 'no-underline flex-grow-0'}, href=True)[0]['href'])\n", + "\n", + " all_repo_data = []\n", + "\n", + " # Loop through each topic URL to get repository info\n", + " for topic_url in topic_urls:\n", + " response = requests.get(topic_url)\n", + " content = BeautifulSoup(response.text, 'html.parser')\n", + " repositories = content.find_all('h3', {'class': \"f3 color-fg-muted text-normal lh-condensed\"})\n", + "\n", + " for repo in repositories:\n", + " if len(all_repo_data) >= limit: # Check if the limit is reached\n", + " break\n", + "\n", + " user = repo.find_all('a')[0].text.strip()\n", + " repo_name = repo.find_all('a')[1].text.strip()\n", + " repo_url = \"https://github.com\" + repo.find_all('a')[1]['href']\n", + "\n", + " # Get the stars (if available)\n", + " star_tag = repo.find_next('span', {'id': 'repo-stars-counter-star'})\n", + " if star_tag:\n", + " star_text = star_tag.text.strip().replace(',', '')\n", + " if 'k' in star_text:\n", + " star_text = star_text.replace('k', '') # Remove the 'k'\n", + " stars = int(float(star_text) * 1000) # Convert to integer after multiplying by 1000\n", + " else:\n", + " stars = int(float(star_text)) # Convert directly to integer\n", + " else:\n", + " stars = 0\n", + "\n", + " # Scrape the README content for the repository\n", + " readme_title, readme_description, readme_content, readme_links = scrape_readme(repo_url)\n", + "\n", + " # Summarize the README content\n", + " readme_summary = summarize_readme(readme_content)\n", + "\n", + " # Collect all data in a dictionary\n", + " repo_data = {\n", + " 'username': user,\n", + " 'repo': repo_name,\n", + " 'stars': stars,\n", + " 'repo_url': repo_url,\n", + " 'readme_content': readme_content,\n", + " 'readme_summary': readme_summary,\n", + " 'readme_links': readme_links\n", + " }\n", + " all_repo_data.append(repo_data)\n", + "\n", + " return all_repo_data\n", + "\n", + "# Execute the scraping and save to CSV\n", + "repo_data = scrape_trending_repositories()\n", + "df_repos = pd.DataFrame(repo_data)\n", + "df_repos.to_csv('trending_repositories_with_readme_summary.csv', index=False)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ezO1ZiIL2C46", + "outputId": "40ba8bb9-b96d-455c-803c-6b96ca3300af" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Your max_length is set to 130, but your input_length is only 125. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=62)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_repos.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 3222 + }, + "id": "jiUyQk3j2TfO", + "outputId": "a40c9d06-7bdd-4bfd-c0f6-99d9b9479b1b" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " username repo stars \\\n", + "0 mrdoob three.js 102000 \n", + "1 pmndrs react-three-fiber 27300 \n", + "2 libgdx libgdx 23300 \n", + "3 BabylonJS Babylon.js 23200 \n", + "4 ssloy tinyrenderer 20400 \n", + "\n", + " repo_url \\\n", + "0 https://github.com/mrdoob/three.js \n", + "1 https://github.com/pmndrs/react-three-fiber \n", + "2 https://github.com/libgdx/libgdx \n", + "3 https://github.com/BabylonJS/Babylon.js \n", + "4 https://github.com/ssloy/tinyrenderer \n", + "\n", + " readme_content \\\n", + "0 [We read every piece of feedback, and take you... \n", + "1 [We read every piece of feedback, and take you... \n", + "2 [We read every piece of feedback, and take you... \n", + "3 [We read every piece of feedback, and take you... \n", + "4 [We read every piece of feedback, and take you... \n", + "\n", + " readme_summary \\\n", + "0 The aim of the project is to create an easy-to... \n", + "1 react-three-fiber is aReact rendererfor three... \n", + "2 libGDX is a cross-platform Java game developm... \n", + "3 WARNING: The CDN should not be used in product... \n", + "4 Gitpod is a free online dev evironment for Git... \n", + "\n", + " readme_links \n", + "0 [(Skip to content, #start-of-content), (, /), ... \n", + "1 [(Skip to content, #start-of-content), (, /), ... \n", + "2 [(Skip to content, #start-of-content), (, /), ... \n", + "3 [(Skip to content, #start-of-content), (, /), ... \n", + "4 [(Skip to content, #start-of-content), (, /), ... 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2libgdxlibgdx23300https://github.com/libgdx/libgdx[We read every piece of feedback, and take you...libGDX is a cross-platform Java game developm...[(Skip to content, #start-of-content), (, /), ...
3BabylonJSBabylon.js23200https://github.com/BabylonJS/Babylon.js[We read every piece of feedback, and take you...WARNING: The CDN should not be used in product...[(Skip to content, #start-of-content), (, /), ...
4ssloytinyrenderer20400https://github.com/ssloy/tinyrenderer[We read every piece of feedback, and take you...Gitpod is a free online dev evironment for Git...[(Skip to content, #start-of-content), (, /), ...
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5bad73c49b7649119fdb56822e1cdb27b11ec7e0 Mon Sep 17 00:00:00 2001 From: Niraj Parmar Date: Thu, 17 Oct 2024 15:06:48 +0530 Subject: [PATCH 2/6] resumeanalyzer --- .../AI_Powered_Resume_Analyzer.ipynb | 541 ++++++++++++++++++ 1 file changed, 541 insertions(+) create mode 100644 Generative Models/AI-RESUME-ANALYZER/AI_Powered_Resume_Analyzer.ipynb diff --git a/Generative Models/AI-RESUME-ANALYZER/AI_Powered_Resume_Analyzer.ipynb b/Generative Models/AI-RESUME-ANALYZER/AI_Powered_Resume_Analyzer.ipynb new file mode 100644 index 00000000..027069bd --- /dev/null +++ b/Generative Models/AI-RESUME-ANALYZER/AI_Powered_Resume_Analyzer.ipynb @@ -0,0 +1,541 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "from PyPDF2 import PdfReader\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.vectorstores import FAISS\n", + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.chains.question_answering import load_qa_chain\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "pdf = \"/content/resume.pdf\"\n", + "pdf_reader = PdfReader(pdf)\n", + "print(pdf_reader)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GOPINATH ASOKAN \n", + "Data Science Enthusiast \n", + "Passionate data science enthusiast with a strong foundation in diverse industries. Equipped with 5+ years\n", + "of industry experience, highly skilled in problem-solving, and project management. Eager to seamlessly\n", + "merge analytical skills with artistic expertise for impactful insights and innovation. Excited to apply data-\n", + "driven strategies to challenges, contribute proactively and effectively to the field, and drive innovation. \n", + "gopiashokankiot@gmail.com \n", + "linkedin.com/in/gopiashokan \n", + "github.com/gopiashokan \n", + "WORK EXPERIENCE \n", + "Senior Process Executive - Operations \n", + "Mahendra Next Wealth IT India Pvt Ltd \n", + "05/2019 - 12/2022\n", + ", \n", + " \n", + "Namakkal \n", + "Proficiently executed image editing tasks for bigbasket's\n", + "product images, encompassing renaming, retouching, \n", + "color\n", + "correction, content cropping, and photo manipulation. \n", + "Expertly designed captivating banners and creatives for\n", + "advertisements, skillfully integrating combo packs, multi-\n", + "packs, and hero images into Bigbasket's product pages. \n", + "Contributed to taxonomy by mapping tax codes, manually\n", + "identified competitor products, and verified AI-generated\n", + "outputs for accuracy, assisting in AI improvement efforts. \n", + "Associate Engineer - Quality \n", + "Rudra Blades and Edges Pvt Ltd \n", + "07/2018 - 12/2018\n", + ", \n", + " \n", + "Chennai \n", + "Performed continuous and comprehensive material analysis\n", + "to ensure structural integrity and precise alignment with\n", + "customer specifications as maintaining quality standards. \n", + "Consistently maintained high quality standards at critical\n", + "welding, grinding, and precision parallelism stations, by\n", + "ensuring unwavering quality in the production process. \n", + "Proficiently managed and coordinated material dispatch\n", + "while meeting both regular order requirements and the\n", + "priority to ensure prompt and reliable customer service. \n", + "Graduate Engineer Trainee - Quality \n", + "Lear Automotive India Pvt Ltd \n", + "07/2016 - 07/2017\n", + ", \n", + " \n", + "Chennai \n", + "Efficiently managed productive customer meetings while\n", + "skillfully addressing challenging issues through \n", + "detailed\n", + "comprehensive Minutes of Meeting documentation. \n", + "Maintained stock alignment, meticulously validating the\n", + "perpetual and wall-to-wall inventory in physical and QAD\n", + "software systems to ensure inventory precision & accuracy. \n", + "Implemented Q-Point, ICA, & PCA for quality enhancement\n", + "and has managed up-to-date Quality Notice documentation\n", + "with 8D reports in the SQTS system for the issue resolution. \n", + "EDUCATION \n", + "Master Data Science \n", + "GUVI Geeks Network Pvt Ltd \n", + "2023\n", + ", \n", + " \n", + "Chennai \n", + "B.E. in Mechanical Engineering \n", + "Knowledge Institute of Technology \n", + "2012 - 2016\n", + ", \n", + " \n", + "Salem \n", + "SKILLS \n", + "Python \n", + "PostgreSQL \n", + "MongoDB \n", + "Tableau \n", + "PowerBI \n", + "Machine Learning \n", + "Deep Learning \n", + "NLP \n", + "LLM \n", + "OpenAI \n", + "Selenium \n", + "Airflow \n", + "Hadoop \n", + "PySpark \n", + "OCR \n", + "Numpy \n", + "Pandas \n", + "Streamlit \n", + "Plotly \n", + "Matplotlib \n", + "Seaborn \n", + "CERTIFICATE \n", + "Microsoft AI-900 Azure AI Fundamentals\n", + " (2023)\n", + " \n", + "PROJECTS \n", + "AI Resume Analyzer and LinkedIn Scraper with Selenium \n", + "Built an \n", + "Innovative \n", + "AI-driven Streamlit app with LLM, OpenAI for\n", + "precise resume analysis and suggestions. Integrated Selenium for\n", + "dynamic LinkedIn data extraction, enhancing career insights. \n", + "AI excels in resume analysis - summarizing, strengths, weaknesses,\n", + "and suggesting job titles. Leveraging Selenium for LinkedIn data, it\n", + "streamlines job searches for comprehensive career insights. \n", + "Tools: Python, LLM, OpenAI, Selenium, Streamlit, Numpy, Pandas.\n", + " \n", + "github.com/gopiashokan/AI-Resume-Analyzer-LinkedIn-Scraper.git\n", + " \n", + "Retail Sales Forecast \n", + "Implemented ML for precise retail sales predictions, emphasizing\n", + "preprocessing and algorithm selection. Streamlined Streamlit app\n", + "integrates EDA, \n", + "optimizing decision-making in dynamic retail. \n", + "Revolutionized retail decisions with advanced ML, using a streamlit\n", + "application integrating EDA for precise sales forecasts, \n", + "feature\n", + "comparison & actionable insights by identifying trends & patterns. \n", + "Tools: Python, \n", + "sklearn, PostgreSQL, Streamlit, Numpy, Pandas, Plotly,\n", + "Matplotlib, Seaborn.\n", + " \n", + "https://github.com/gopiashokan/Retail-Sales-Forecast.git\n", + " \n", + "Industrial Copper Modeling \n", + "Leveraged advanced ML regression models for precise pricing and\n", + "classification, enhancing targeted customer engagement by\n", + "predicting potential customers in the copper industry landscape. \n", + "Expert in data preprocessing, feature engineering, cross-validation,\n", + "hyperparameter tuning, and Streamlit app development, \n", + "skillfully\n", + "applying the skills to solve real-world manufacturing challenges. \n", + "Tools: Python, sklearn, Streamlit, Matplotlib,Seaborn, Numpy,Pandas.\n", + " \n", + "github.com/gopiashokan/Industrial-Copper-Modeling.git\n", + " \n", + "Airbnb Analysis \n", + "Leverage Streamlit for dynamic exploratory data analysis (EDA)\n", + "with interactive charts. Extend insights through a comprehensive\n", + "Tableau dashboard, uncovering trends and patterns in the dataset. \n", + "Analyzed pricing dynamics & availability patterns in the Hospitality\n", + "sector, enabling informed decision-making and empowering\n", + "stakeholders to make choices based on insights and visualizations. \n", + "Tools: Python, MongoDB, PostgreSQL, Tableau, Streamlit, Plotly,\n", + "Pandas.\n", + " \n", + "github.com/gopiashokan/Airbnb-Analysis.git\n", + " \n", + "\n" + ] + } + ], + "source": [ + "# extrat text from each page separately\n", + "text = \"\"\n", + "for page in pdf_reader.pages:\n", + " text += page.extract_text()\n", + "\n", + "print(text)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['GOPINATH ASOKAN \\nData Science Enthusiast \\nPassionate data science enthusiast with a strong foundation in diverse industries. Equipped with 5+ years\\nof industry experience, highly skilled in problem-solving, and project management. Eager to seamlessly\\nmerge analytical skills with artistic expertise for impactful insights and innovation. Excited to apply data-\\ndriven strategies to challenges, contribute proactively and effectively to the field, and drive innovation. \\ngopiashokankiot@gmail.com \\nlinkedin.com/in/gopiashokan \\ngithub.com/gopiashokan \\nWORK EXPERIENCE \\nSenior Process Executive - Operations \\nMahendra Next Wealth IT India Pvt Ltd \\n05/2019 - 12/2022\\n, \\n \\nNamakkal',\n", + " \"linkedin.com/in/gopiashokan \\ngithub.com/gopiashokan \\nWORK EXPERIENCE \\nSenior Process Executive - Operations \\nMahendra Next Wealth IT India Pvt Ltd \\n05/2019 - 12/2022\\n, \\n \\nNamakkal \\nProficiently executed image editing tasks for bigbasket's\\nproduct images, encompassing renaming, retouching, \\ncolor\\ncorrection, content cropping, and photo manipulation. \\nExpertly designed captivating banners and creatives for\\nadvertisements, skillfully integrating combo packs, multi-\\npacks, and hero images into Bigbasket's product pages. \\nContributed to taxonomy by mapping tax codes, manually\\nidentified competitor products, and verified AI-generated\\noutputs for accuracy, assisting in AI improvement efforts.\",\n", + " 'Contributed to taxonomy by mapping tax codes, manually\\nidentified competitor products, and verified AI-generated\\noutputs for accuracy, assisting in AI improvement efforts. \\nAssociate Engineer - Quality \\nRudra Blades and Edges Pvt Ltd \\n07/2018 - 12/2018\\n, \\n \\nChennai \\nPerformed continuous and comprehensive material analysis\\nto ensure structural integrity and precise alignment with\\ncustomer specifications as maintaining quality standards. \\nConsistently maintained high quality standards at critical\\nwelding, grinding, and precision parallelism stations, by\\nensuring unwavering quality in the production process. \\nProficiently managed and coordinated material dispatch',\n", + " 'welding, grinding, and precision parallelism stations, by\\nensuring unwavering quality in the production process. \\nProficiently managed and coordinated material dispatch\\nwhile meeting both regular order requirements and the\\npriority to ensure prompt and reliable customer service. \\nGraduate Engineer Trainee - Quality \\nLear Automotive India Pvt Ltd \\n07/2016 - 07/2017\\n, \\n \\nChennai \\nEfficiently managed productive customer meetings while\\nskillfully addressing challenging issues through \\ndetailed\\ncomprehensive Minutes of Meeting documentation. \\nMaintained stock alignment, meticulously validating the\\nperpetual and wall-to-wall inventory in physical and QAD',\n", + " 'detailed\\ncomprehensive Minutes of Meeting documentation. \\nMaintained stock alignment, meticulously validating the\\nperpetual and wall-to-wall inventory in physical and QAD\\nsoftware systems to ensure inventory precision & accuracy. \\nImplemented Q-Point, ICA, & PCA for quality enhancement\\nand has managed up-to-date Quality Notice documentation\\nwith 8D reports in the SQTS system for the issue resolution. \\nEDUCATION \\nMaster Data Science \\nGUVI Geeks Network Pvt Ltd \\n2023\\n, \\n \\nChennai \\nB.E. in Mechanical Engineering \\nKnowledge Institute of Technology \\n2012 - 2016\\n, \\n \\nSalem \\nSKILLS \\nPython \\nPostgreSQL \\nMongoDB \\nTableau \\nPowerBI \\nMachine Learning \\nDeep Learning \\nNLP \\nLLM \\nOpenAI \\nSelenium \\nAirflow',\n", + " 'Knowledge Institute of Technology \\n2012 - 2016\\n, \\n \\nSalem \\nSKILLS \\nPython \\nPostgreSQL \\nMongoDB \\nTableau \\nPowerBI \\nMachine Learning \\nDeep Learning \\nNLP \\nLLM \\nOpenAI \\nSelenium \\nAirflow \\nHadoop \\nPySpark \\nOCR \\nNumpy \\nPandas \\nStreamlit \\nPlotly \\nMatplotlib \\nSeaborn \\nCERTIFICATE \\nMicrosoft AI-900 Azure AI Fundamentals\\n (2023)\\n \\nPROJECTS \\nAI Resume Analyzer and LinkedIn Scraper with Selenium \\nBuilt an \\nInnovative \\nAI-driven Streamlit app with LLM, OpenAI for\\nprecise resume analysis and suggestions. Integrated Selenium for\\ndynamic LinkedIn data extraction, enhancing career insights. \\nAI excels in resume analysis - summarizing, strengths, weaknesses,',\n", + " 'precise resume analysis and suggestions. Integrated Selenium for\\ndynamic LinkedIn data extraction, enhancing career insights. \\nAI excels in resume analysis - summarizing, strengths, weaknesses,\\nand suggesting job titles. Leveraging Selenium for LinkedIn data, it\\nstreamlines job searches for comprehensive career insights. \\nTools: Python, LLM, OpenAI, Selenium, Streamlit, Numpy, Pandas.\\n \\ngithub.com/gopiashokan/AI-Resume-Analyzer-LinkedIn-Scraper.git\\n \\nRetail Sales Forecast \\nImplemented ML for precise retail sales predictions, emphasizing\\npreprocessing and algorithm selection. Streamlined Streamlit app\\nintegrates EDA, \\noptimizing decision-making in dynamic retail.',\n", + " 'Implemented ML for precise retail sales predictions, emphasizing\\npreprocessing and algorithm selection. Streamlined Streamlit app\\nintegrates EDA, \\noptimizing decision-making in dynamic retail. \\nRevolutionized retail decisions with advanced ML, using a streamlit\\napplication integrating EDA for precise sales forecasts, \\nfeature\\ncomparison & actionable insights by identifying trends & patterns. \\nTools: Python, \\nsklearn, PostgreSQL, Streamlit, Numpy, Pandas, Plotly,\\nMatplotlib, Seaborn.\\n \\nhttps://github.com/gopiashokan/Retail-Sales-Forecast.git\\n \\nIndustrial Copper Modeling \\nLeveraged advanced ML regression models for precise pricing and\\nclassification, enhancing targeted customer engagement by',\n", + " 'Industrial Copper Modeling \\nLeveraged advanced ML regression models for precise pricing and\\nclassification, enhancing targeted customer engagement by\\npredicting potential customers in the copper industry landscape. \\nExpert in data preprocessing, feature engineering, cross-validation,\\nhyperparameter tuning, and Streamlit app development, \\nskillfully\\napplying the skills to solve real-world manufacturing challenges. \\nTools: Python, sklearn, Streamlit, Matplotlib,Seaborn, Numpy,Pandas.\\n \\ngithub.com/gopiashokan/Industrial-Copper-Modeling.git\\n \\nAirbnb Analysis \\nLeverage Streamlit for dynamic exploratory data analysis (EDA)\\nwith interactive charts. Extend insights through a comprehensive',\n", + " 'Airbnb Analysis \\nLeverage Streamlit for dynamic exploratory data analysis (EDA)\\nwith interactive charts. Extend insights through a comprehensive\\nTableau dashboard, uncovering trends and patterns in the dataset. \\nAnalyzed pricing dynamics & availability patterns in the Hospitality\\nsector, enabling informed decision-making and empowering\\nstakeholders to make choices based on insights and visualizations. \\nTools: Python, MongoDB, PostgreSQL, Tableau, Streamlit, Plotly,\\nPandas.\\n \\ngithub.com/gopiashokan/Airbnb-Analysis.git']" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Split the long text into small chunks\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=700,\n", + " chunk_overlap=200,\n", + " length_function=len)\n", + "\n", + "chunks = text_splitter.split_text(text=text)\n", + "chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'GOPINATH ASOKAN \\nData Science Enthusiast \\nPassionate data science enthusiast with a strong foundation in diverse industries. Equipped with 5+ years\\nof industry experience, highly skilled in problem-solving, and project management. Eager to seamlessly\\nmerge analytical skills with artistic expertise for impactful insights and innovation. Excited to apply data-\\ndriven strategies to challenges, contribute proactively and effectively to the field, and drive innovation. \\ngopiashokankiot@gmail.com \\nlinkedin.com/in/gopiashokan \\ngithub.com/gopiashokan \\nWORK EXPERIENCE \\nSenior Process Executive - Operations \\nMahendra Next Wealth IT India Pvt Ltd \\n05/2019 - 12/2022\\n, \\n \\nNamakkal'" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chunks[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"linkedin.com/in/gopiashokan \\ngithub.com/gopiashokan \\nWORK EXPERIENCE \\nSenior Process Executive - Operations \\nMahendra Next Wealth IT India Pvt Ltd \\n05/2019 - 12/2022\\n, \\n \\nNamakkal \\nProficiently executed image editing tasks for bigbasket's\\nproduct images, encompassing renaming, retouching, \\ncolor\\ncorrection, content cropping, and photo manipulation. \\nExpertly designed captivating banners and creatives for\\nadvertisements, skillfully integrating combo packs, multi-\\npacks, and hero images into Bigbasket's product pages. \\nContributed to taxonomy by mapping tax codes, manually\\nidentified competitor products, and verified AI-generated\\noutputs for accuracy, assisting in AI improvement efforts.\"" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chunks[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"linkedin.com/in/gopiashokan \\ngithub.com/gopiashokan \\nWORK EXPERIENCE \\nSenior Process Executive - Operations \\nMahendra Next Wealth IT India Pvt Ltd \\n05/2019 - 12/2022\\n, \\n \\nNamakkal\"\n", + "\n", + "The above text is common(overlap) for both chunks[0] and chunks[1].\n", + "(chunk_overlap=200 - maximum length, it means length is not exceed 200)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "openai_api_key = input('Enter you OpenAI API Key: ')" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "def openai(openai_api_key, chunks, analyze):\n", + "\n", + " # Using OpenAI service for embedding\n", + " embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)\n", + "\n", + " # Facebook AI Similarity Serach library help us to convert text data to numerical vector\n", + " vectorstores = FAISS.from_texts(chunks, embedding=embeddings)\n", + "\n", + " # compares the query and chunks, enabling the selection of the top 'K' most similar chunks based on their similarity scores.\n", + " docs = vectorstores.similarity_search(query=analyze, k=3)\n", + "\n", + " # creates an OpenAI object, using the ChatGPT 3.5 Turbo model\n", + " llm = ChatOpenAI(model='gpt-3.5-turbo', api_key=openai_api_key)\n", + "\n", + " # question-answering (QA) pipeline, making use of the load_qa_chain function\n", + " chain = load_qa_chain(llm=llm, chain_type='stuff')\n", + "\n", + " response = chain.run(input_documents=docs, question=analyze)\n", + " return response" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The resume belongs to Gopinath Asokan, who is a data science enthusiast with a strong foundation in diverse industries. He has 5+ years of industry experience and is highly skilled in problem-solving and project management. Gopinath is eager to merge his analytical skills with artistic expertise for impactful insights and innovation. He is excited to apply data-driven strategies to challenges and contribute proactively to the field. \n", + "\n", + "In terms of work experience, Gopinath has worked as a Senior Process Executive - Operations at Mahendra Next Wealth IT India Pvt Ltd from 05/2019 to 12/2022. He was responsible for precise resume analysis and suggestions, as well as integrating Selenium for dynamic LinkedIn data extraction. He also implemented machine learning for precise retail sales predictions, emphasizing preprocessing and algorithm selection.\n", + "\n", + "Gopinath has also worked as an Associate Engineer - Quality at Rudra Blades and Edges Pvt Ltd from 07/2018 to 12/2018. He performed continuous and comprehensive material analysis to ensure structural integrity and maintained high-quality standards at critical stations.\n", + "\n", + "Furthermore, Gopinath worked as a Graduate Engineer Trainee - Quality at Lear Automotive India Pvt Ltd from 07/2016 to 07/2017. He efficiently managed productive customer meetings and maintained stock alignment.\n", + "\n", + "In terms of education, Gopinath has a Master's degree in Data Science from GUVI Geeks Network Pvt Ltd (expected completion in 2023) and a Bachelor's degree in Mechanical Engineering from Knowledge Institute of Technology (2012-2016).\n", + "\n", + "Gopinath possesses a wide range of technical skills, including Python, PostgreSQL, MongoDB, Tableau, PowerBI, Machine Learning, Deep Learning, NLP, LLM, OpenAI, Selenium, Airflow, Hadoop, PySpark, OCR, Numpy, Pandas, Streamlit, Plotly, Matplotlib, and Seaborn.\n", + "\n", + "He has also completed the Microsoft AI-900 Azure AI Fundamentals certificate.\n", + "\n", + "Gopinath has worked on several projects, including an AI Resume Analyzer and LinkedIn Scraper with Selenium, a Retail Sales Forecast using ML, Industrial Copper Modeling using ML, and Airbnb Analysis using Streamlit and Tableau.\n", + "\n", + "In conclusion, Gopinath Asokan is a highly skilled data science enthusiast with a diverse industry background. He has experience in resume analysis, retail sales predictions, and quality assurance. He is proficient in various technical skills and has completed relevant certifications. Gopinath has also worked on several data science projects, showcasing his expertise in machine learning and analysis.\n" + ] + } + ], + "source": [ + "def resume_summary(query_with_chunks):\n", + " query = f''' need to detailed summarization of below resume and finally conclude them\n", + "\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " {query_with_chunks}\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " '''\n", + " return query\n", + "\n", + "summary = resume_summary(query_with_chunks=chunks)\n", + "summary_result = openai(openai_api_key=openai_api_key, chunks=chunks, analyze=summary)\n", + "print(summary_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Strengths of Gopinath Asokan's resume:\n", + "\n", + "1. Strong foundation in diverse industries: Gopinath's resume highlights his experience and expertise in various industries, showcasing his adaptability and ability to work in different environments.\n", + "\n", + "2. 5+ years of industry experience: Gopinath's extensive experience in the industry demonstrates his ability to handle real-world challenges and shows his level of expertise in the field.\n", + "\n", + "3. Strong problem-solving and project management skills: Gopinath's resume emphasizes his skills in problem-solving and project management, which are crucial in the field of data science. This indicates his ability to effectively handle complex problems and successfully manage projects.\n", + "\n", + "4. Analytical skills combined with artistic expertise: Gopinath's resume mentions his eagerness to merge his analytical skills with artistic expertise, indicating his ability to think creatively and produce impactful insights and innovations.\n", + "\n", + "5. Data-driven strategies: Gopinath's excitement to apply data-driven strategies to challenges shows his understanding of the importance of data analysis in decision-making and problem-solving.\n", + "\n", + "6. Proficient in technical skills: Gopinath possesses a wide range of technical skills, including Python, machine learning, NLP, Selenium, and more. This showcases his ability to utilize various tools and technologies to solve complex problems and deliver high-quality work.\n", + "\n", + "7. Relevant certifications: Gopinath's completion of the Microsoft AI-900 Azure AI Fundamentals certificate demonstrates his commitment to continuous learning and staying up-to-date with the latest technologies and advancements in the field of data science.\n", + "\n", + "8. Experienced in various projects: Gopinath's experience in projects such as AI Resume Analyzer and LinkedIn Scraper, Retail Sales Forecast using ML, and Industrial Copper Modeling using ML showcases his practical application of data science techniques and his ability to deliver successful projects.\n", + "\n", + "In conclusion, Gopinath Asokan's resume exhibits strengths in his industry experience, problem-solving skills, technical expertise, and project management abilities. His diverse background, eagerness to merge analytical and artistic skills, and relevant certifications make him a strong candidate in the field of data science.\n" + ] + } + ], + "source": [ + "def resume_strength(query_with_chunks):\n", + " query = f'''need to detailed analysis and explain of the strength of below resume and finally conclude them\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " {query_with_chunks}\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " '''\n", + " return query\n", + "\n", + "strength = resume_strength(query_with_chunks=summary_result)\n", + "strength_result = openai(openai_api_key=openai_api_key, chunks=chunks, analyze=strength)\n", + "print(strength_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Based on the provided resume details, Gopinath Asokan has a strong foundation in diverse industries and is highly skilled in problem-solving and project management. However, there are a few weaknesses in the resume that can be improved:\n", + "\n", + "1. Lack of a clear career objective: The resume does not mention a specific career objective or goal. It would be beneficial to include a clear and concise objective statement that highlights Gopinath's career aspirations and how his skills and experience align with those goals.\n", + "\n", + "2. Incomplete work experience details: While the resume mentions Gopinath's job titles and responsibilities, it does not provide specific accomplishments or achievements in each role. Adding quantifiable achievements or results-oriented statements would strengthen the resume and demonstrate Gopinath's impact in previous positions.\n", + "\n", + "3. Limited information on education: The resume briefly mentions Gopinath's educational background, but it lacks details on specific coursework or projects related to data science. Including relevant coursework, research projects, or any notable academic achievements would enhance the resume's credibility and showcase Gopinath's academic abilities.\n", + "\n", + "4. Lack of focus on key technical skills: Although the resume mentions a wide range of technical skills, it does not highlight which skills are most relevant to the data science field. It would be helpful to prioritize and emphasize the key technical skills that directly align with the desired job roles in data science.\n", + "\n", + "To improve the resume, consider the following suggestions:\n", + "\n", + "1. Start with a strong career objective statement that clearly communicates Gopinath's goals and how his skills and experience align with those goals.\n", + "\n", + "2. Include specific accomplishments and achievements in each work experience entry, highlighting the impact Gopinath made in previous roles. Use quantitative metrics whenever possible to showcase results.\n", + "\n", + "3. Provide more details on relevant coursework, research projects, or academic achievements related to data science during Gopinath's Master's degree program.\n", + "\n", + "4. Prioritize and highlight the key technical skills that directly align with data science roles. Consider creating a separate section dedicated to technical skills, showcasing proficiency and experience in those areas.\n", + "\n", + "5. Consider including any relevant certifications, online courses, or workshops related to data science or machine learning.\n", + "\n", + "By addressing these weaknesses and implementing these improvements, Gopinath Asokan can create a stronger and more impactful resume that highlights his skills, experience, and potential in the field of data science.\n" + ] + } + ], + "source": [ + "def resume_weakness(query_with_chunks):\n", + " query = f'''need to detailed analysis and explain of the weakness of below resume and how to improve make a better resume.\n", + "\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " {query_with_chunks}\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " '''\n", + " return query\n", + "\n", + "weakness = resume_weakness(query_with_chunks=summary_result)\n", + "result_weakness = openai(openai_api_key=openai_api_key, chunks=chunks, analyze=weakness)\n", + "print(result_weakness)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Based on the information provided, some potential job roles that Gopinath Asokan could apply to on LinkedIn include:\n", + "\n", + "1. Data Scientist\n", + "2. Data Analyst\n", + "3. Machine Learning Engineer\n", + "4. Business Analyst\n", + "5. Project Manager\n", + "6. Operations Analyst\n", + "7. Quality Assurance Engineer\n", + "8. Sales Analyst\n", + "9. AI Engineer\n", + "10. Retail Analyst\n", + "\n", + "These job roles align with Gopinath's skills and experience in data science, problem-solving, project management, resume analysis, retail sales forecasting, and quality assurance.\n" + ] + } + ], + "source": [ + "def job_title_suggestion(query_with_chunks):\n", + "\n", + " query = f''' what are the job roles i apply to likedin based on below?\n", + " \n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " {query_with_chunks}\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " '''\n", + " return query\n", + "\n", + "suggestion = job_title_suggestion(query_with_chunks=summary_result)\n", + "result_suggestion = openai(openai_api_key=openai_api_key, chunks=chunks, analyze=suggestion)\n", + "print(result_suggestion)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From ca00d06719c67301167fccd77d2534829dd6bea1 Mon Sep 17 00:00:00 2001 From: Niraj Parmar Date: Thu, 17 Oct 2024 15:33:12 +0530 Subject: [PATCH 3/6] resume --- .../github_repo_scraper/github_scraper.ipynb | 2168 ----------------- .../github_scraper.ipynb - Colab.pdf | Bin 633384 -> 0 bytes .../github_scraper.ipynb.-.Colab.pdf | Bin 633384 -> 0 bytes 3 files changed, 2168 deletions(-) delete mode 100644 web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb delete mode 100644 web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb - Colab.pdf delete mode 100644 web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb.-.Colab.pdf diff --git a/web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb b/web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb deleted file mode 100644 index ce1e4950..00000000 --- a/web scraping in python/repo.github.com/github_repo_scraper/github_scraper.ipynb +++ /dev/null @@ -1,2168 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "### Web Scraper of Trending Repositories on Github" - ], - "metadata": { - "id": "yK7-Rd374TiB" - } - }, - { - "cell_type": "code", - "source": [ - "import requests\n", - "from bs4 import BeautifulSoup\n", - "import pandas as pd\n", - "import base64" - ], - "metadata": { - "id": "96DYZj8q4qNK" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "topics_url = 'https://github.com/topics'" - ], - "metadata": { - "id": "URtvE66M4sHb" - }, - "execution_count": 62, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "response = requests.get(topics_url)\n", - "content = response.text" - ], - "metadata": { - "id": "rwduGUCB4zFh" - }, - "execution_count": 63, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "content[:1000]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 123 - }, - "id": "I--FZfK0KVbN", - "outputId": "5c3fb96b-90ac-4a1f-f879-0c95806b37da" - }, - "execution_count": 64, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'\\n\\n\\n\\n\\n\\n\\n \\n \\n \\n \\n \\n \\n \\n \\n\\n \\n\\n 3D

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\n" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "df", - "summary": "{\n \"name\": \"df\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"usernames\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"flutter\",\n \"Solido\",\n \"android\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"repos\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"flutter\",\n \"MPAndroidChart\",\n \"uni-app\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stars\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 35876,\n \"min\": 35200,\n \"max\": 165000,\n \"num_unique_values\": 19,\n \"samples\": [\n 165000,\n 53400,\n 44300\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"repo_urls\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"https://github.com/flutter/flutter\",\n \"https://github.com/PhilJay/MPAndroidChart\",\n \"https://github.com/dcloudio/uni-app\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } - }, - "metadata": {}, - "execution_count": 74 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#save the data in to csv" - ], - "metadata": { - "id": "MwxOxZVSecOQ" - } - }, - { - "cell_type": "code", - "source": [ - "for i in range(len(topic_titles_)) :\n", - " df = pd.DataFrame(get_info_from_topic(topic_urls[i]))\n", - " df.to_csv(topic_titles_[i])" - ], - "metadata": { - "id": "byV3FAy6_KoI" - }, - "execution_count": 75, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "scrap the readme file of repo." - ], - "metadata": { - "id": "CAW7AIZqejOb" - } - }, - { - "cell_type": "code", - "source": [ - "import requests\n", - "from bs4 import BeautifulSoup\n", - "\n", - "# URL of the README page\n", - "url = \"https://github.com/bumptech/glide\"\n", - "\n", - "# Fetch the README page\n", - "response = requests.get(url)\n", - "soup = BeautifulSoup(response.text, 'html.parser')\n", - "\n", - "# Extract the title (usually in

or

)\n", - "title = soup.find('h1').get_text(strip=True)\n", - "\n", - "# Extract the description (first

after title or within a specific section)\n", - "description = soup.find('p').get_text(strip=True)\n", - "\n", - "# Extract all paragraphs (for main content)\n", - "content_paragraphs = soup.find_all('p')\n", - "content = [para.get_text(strip=True) for para in content_paragraphs]\n", - "\n", - "# Extract links from the content\n", - "links = []\n", - "for link in soup.find_all('a', href=True):\n", - " links.append((link.get_text(strip=True), link['href']))\n", - "\n" - ], - "metadata": { - "id": "umk_RfvdGNqh" - }, - "execution_count": 76, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Display the extracted content\n", - "print(\"Title:\", title)\n", - "\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OPIcuUL1P3oa", - "outputId": "b3ffa345-b83c-41bf-bff2-ce86cc1b869c" - }, - "execution_count": 77, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Title: Search code, repositories, users, issues, pull requests...\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "print(\"Content:\", content)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "S8gLnpZzP50m", - "outputId": "93a7ffd2-1ed2-405b-d304-a8cfb9e70587" - }, - "execution_count": 78, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Content: ['We read every piece of feedback, and take your input very seriously.', 'To see all available qualifiers, see ourdocumentation.', 'An image loading and caching library for Android focused on smooth scrolling', \"|View Glide's documentation|简体中文文档|Report an issue with Glide\", 'Glide is a fast and efficient open source media management and image loading framework for Android that wraps media\\ndecoding, memory and disk caching, and resource pooling into a simple and easy to use interface.', '', \"Glide supports fetching, decoding, and displaying video stills, images, and animated GIFs. Glide includes a flexible API\\nthat allows developers to plug in to almost any network stack. By default Glide uses a customHttpUrlConnectionbased\\nstack, but also includes utility libraries plug in to Google's Volley project or Square's OkHttp library instead.\", \"Glide's primary focus is on making scrolling any kind of a list of images as smooth and fast as possible, but Glide is\\nalso effective for almost any case where you need to fetch, resize, and display a remote image.\", \"For detailed instructions and requirements, see Glide'sdownload and setup docs page.\", \"You can download a jar from GitHub'sreleases page.\", 'Or use Gradle:', 'Or Maven:', 'For info on using the bleeding edge, see theSnapshotsdocs page.', 'The specific rules arealready bundledinto the aar which can be interpreted by R8 automatically', 'Check out thedocumentationfor pages on a variety of topics, and see thejavadocs.', 'For Glide v3, see thewiki.', 'Simple use cases will look something like this:', 'Version 4 is now released and stable. Updates are released periodically with new features and bug fixes.', 'Comments/bugs/questions/pull requests are always welcome! Please readCONTRIBUTING.mdon how to report issues.', 'If you need to support older versions of Android, consider staying onGlide v3, which works on API 10, but is not actively maintained.', 'Building Glide with gradle is fairly straight forward:', 'Note: Make sure yourAndroid SDKhas theAndroid Support Repositoryinstalled, and that your$ANDROID_HOMEenvironment\\nvariable is pointing at the SDK or add alocal.propertiesfile in the root project with asdk.dir=...line.', 'Follow the steps in theBuildsection to set up the project and then:', 'You may also find precompiled APKs on thereleases page.', \"Follow the steps in theBuildsection to setup the project and then edit the files however you wish.Android Studiocleanly imports both Glide's source and tests and is the recommended way to work with Glide.\", 'To open the project in Android Studio:', 'For more details, see theContributing docs page.', \"To report a specific problem or feature request,open a new issue on Github. For questions, suggestions, or\\nanything else, emailGlide's discussion group, or join our IRC channel:irc.freenode.net#glide-library.\", \"Before submitting pull requests, contributors must sign Google'sindividual contributor license agreement.\", 'Sam Judd - @sjudd on GitHub, @samajudd on Twitter', 'BSD, part MIT and Apache 2.0. See theLICENSEfile for details.', 'This is not an official Google product.', 'An image loading and caching library for Android focused on smooth scrolling']\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "print(\"Links:\", links)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Ul322fF_P8gZ", - "outputId": "eddd5810-7987-4e3e-b321-0a701a9e3cae" - }, - "execution_count": 79, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Links: [('Skip to content', '#start-of-content'), ('', '/'), ('Sign in', '/login?return_to=https%3A%2F%2Fgithub.com%2Fbumptech%2Fglide'), ('GitHub CopilotWrite better code with AI', 'https://github.com/features/copilot'), ('SecurityFind and fix vulnerabilities', 'https://github.com/features/security'), ('ActionsAutomate any workflow', 'https://github.com/features/actions'), ('CodespacesInstant dev environments', 'https://github.com/features/codespaces'), ('IssuesPlan and track 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)\n", - " title_tag = soup.find('h1')\n", - " title = title_tag.get_text(strip=True) if title_tag else \"No title found\"\n", - "\n", - " # Find the first

after the title\n", - " content_paragraphs = soup.find_all('p')\n", - "\n", - " # Extract the description (first

after title or within a specific section)\n", - " description = soup.find('p').get_text(strip=True)\n", - "\n", - " # Extract all paragraphs (for main content)\n", - "\n", - " content = [para.get_text(strip=True) for para in content_paragraphs]\n", - "\n", - " # Extract links from the README content\n", - " links = []\n", - " for link in soup.find_all('a', href=True):\n", - " links.append((link.get_text(strip=True), link['href']))\n", - "\n", - " return title, description, content, links\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "def scrape_trending_repositories(limit=100): # Set a default limit\n", - " topics_url = 'https://github.com/topics'\n", - " response = requests.get(topics_url)\n", - " content = response.text\n", - "\n", - " doc = BeautifulSoup(content, 'html.parser')\n", - " topic_titles = doc.find_all('p', {'class': 'f3 lh-condensed mb-0 mt-1 Link--primary'})\n", - "\n", - " topic_titles_ = []\n", - " topic_urls = []\n", - "\n", - " for tag in topic_titles:\n", - " topic_titles_.append(tag.text)\n", - " topic_urls.append(\"https://github.com\" + tag.parent.parent.find_all('a', {'class': 'no-underline flex-grow-0'}, href=True)[0]['href'])\n", - "\n", - " df_topics = pd.DataFrame({'topic_title': topic_titles_, 'topic_url': topic_urls})\n", - "\n", - " all_repo_data = []\n", - "\n", - " # Loop through each topic URL to get repository info\n", - " for topic_url in topic_urls:\n", - " response = requests.get(topic_url)\n", - " content = BeautifulSoup(response.text, 'html.parser')\n", - " repositories = content.find_all('h3', {'class': \"f3 color-fg-muted text-normal lh-condensed\"})\n", - "\n", - " for repo in repositories:\n", - " if len(all_repo_data) >= limit: # Check if the limit is reached\n", - " break\n", - "\n", - " user = repo.find_all('a')[0].text.strip()\n", - " repo_name = repo.find_all('a')[1].text.strip()\n", - " repo_url = \"https://github.com\" + repo.find_all('a')[1]['href']\n", - "\n", - " # Get the stars (if available)\n", - " star_tag = repo.find_next('span', {'id': 'repo-stars-counter-star'})\n", - " if star_tag:\n", - " star_text = star_tag.text.strip().replace(',', '')\n", - " if 'k' in star_text:\n", - " star_text = star_text.replace('k', '') # Remove the 'k'\n", - " stars = int(float(star_text) * 1000) # Convert to integer after multiplying by 1000\n", - " else:\n", - " stars = int(float(star_text)) # Convert to integer directly\n", - " else:\n", - " stars = 0\n", - "\n", - " # Scrape the README content for the repository\n", - " readme_title, readme_description, readme_content, readme_links = scrape_readme(repo_url)\n", - "\n", - " # Collect all data in a dictionary\n", - " repo_data = {\n", - " 'username': user,\n", - " 'repo': repo_name,\n", - " 'stars': stars,\n", - " 'repo_url': repo_url,\n", - " 'readme_content': readme_content,\n", - "\n", - "\n", - " 'readme_links': readme_links\n", - " }\n", - " all_repo_data.append(repo_data)\n", - "\n", - " return all_repo_data\n" - ], - "metadata": { - "id": "AgwcOtzYYRc7" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df_repos = pd.DataFrame(scrape_trending_repositories())\n", - "df_repos.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 3222 - }, - "id": "xIfY2FCDbDFb", - "outputId": "95a98bdf-b716-41b0-a105-290db1a66dd1" - }, - "execution_count": 117, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " username repo stars \\\n", - "0 mrdoob three.js 102000 \n", - "1 pmndrs react-three-fiber 27300 \n", - "2 libgdx libgdx 23300 \n", - "3 BabylonJS Babylon.js 23200 \n", - "4 ssloy tinyrenderer 20400 \n", - "\n", - " repo_url \\\n", - "0 https://github.com/mrdoob/three.js \n", - "1 https://github.com/pmndrs/react-three-fiber \n", - "2 https://github.com/libgdx/libgdx \n", - "3 https://github.com/BabylonJS/Babylon.js \n", - "4 https://github.com/ssloy/tinyrenderer \n", - "\n", - " readme_content \\\n", - "0 [We read every piece of feedback, and take you... \n", - "1 [We read every piece of feedback, and take you... \n", - "2 [We read every piece of feedback, and take you... \n", - "3 [We read every piece of feedback, and take you... \n", - "4 [We read every piece of feedback, and take you... \n", - "\n", - " readme_description \\\n", - "0 We read every piece of feedback, and take your... \n", - "1 We read every piece of feedback, and take your... \n", - "2 We read every piece of feedback, and take your... \n", - "3 We read every piece of feedback, and take your... \n", - "4 We read every piece of feedback, and take your... \n", - "\n", - " readme_links \n", - "0 [(Skip to content, #start-of-content), (, /), ... \n", - "1 [(Skip to content, #start-of-content), (, /), ... \n", - "2 [(Skip to content, #start-of-content), (, /), ... \n", - "3 [(Skip to content, #start-of-content), (, /), ... \n", - "4 [(Skip to content, #start-of-content), (, /), ... " - ], - "text/html": [ - "\n", - "

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usernamerepostarsrepo_urlreadme_contentreadme_descriptionreadme_links
0mrdoobthree.js102000https://github.com/mrdoob/three.js[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
1pmndrsreact-three-fiber27300https://github.com/pmndrs/react-three-fiber[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
2libgdxlibgdx23300https://github.com/libgdx/libgdx[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
3BabylonJSBabylon.js23200https://github.com/BabylonJS/Babylon.js[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
4ssloytinyrenderer20400https://github.com/ssloy/tinyrenderer[We read every piece of feedback, and take you...We read every piece of feedback, and take your...[(Skip to content, #start-of-content), (, /), ...
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\n" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "df_repos", - "summary": "{\n \"name\": \"df_repos\",\n \"rows\": 100,\n \"fields\": [\n {\n \"column\": \"username\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 81,\n \"samples\": [\n \"noelboss\",\n \"mrdoob\",\n \"developit\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"repo\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 100,\n \"samples\": [\n \"scrcpy\",\n \"JavaScript\",\n \"socket\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"stars\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 50334,\n \"min\": 99,\n \"max\": 306000,\n \"num_unique_values\": 92,\n \"samples\": [\n 97000,\n 5200,\n 2800\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"repo_url\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 100,\n \"samples\": [\n \"https://github.com/Genymobile/scrcpy\",\n \"https://github.com/TheAlgorithms/JavaScript\",\n \"https://github.com/amphp/socket\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"readme_content\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"readme_description\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"We read every piece of feedback, and take your input very seriously.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"readme_links\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } - }, - "metadata": {}, - "execution_count": 117 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "summarization model\n" - ], - "metadata": { - "id": "Xz46VAs911_I" - } - }, - { - "cell_type": "code", - "source": [ - "from transformers import pipeline\n", - "\n", - "# Load the summarization model\n", - "summarization_model = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\")\n", - "\n", - "# Sample README content as a single string (you can combine from your list)\n", - "readme_content = \"\"\"We read every piece of feedback, and take your input very seriously.\n", - "The aim of the project is to create an easy-to-use, lightweight, cross-browser, general-purpose 3D library.\n", - "The current builds only include a WebGL renderer but WebGPU (experimental), SVG, and CSS3D renderers are also available as addons.\n", - "This code creates a scene, a camera, and a geometric cube, and it adds the cube to the scene.\n", - "It then creates a WebGL renderer for the scene and camera, and it adds that viewport to the document.body element.\n", - "Finally, it animates the cube within the scene for the camera.\n", - "If everything goes well, you should see this.\n", - "Cloning the repo with all its history results in a ~2 GB download.\n", - "If you don't need the whole history you can use the depth parameter to significantly reduce download size.\n", - "JavaScript 3D Library.\"\"\"\n", - "\n", - "# Function to summarize the README content\n", - "def summarize_readme(text):\n", - " # Summarize the text using the summarization model\n", - " summary = summarization_model(text, max_length=300, min_length=30, do_sample=False)\n", - " return summary[0]['summary_text'] # Extract the summary text\n", - "\n", - "# Call the summarization function\n", - "summary = summarize_readme(readme_content)\n", - "print(\"Summary:\")\n", - "print(summary)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oAhwWR10xffy", - "outputId": "c002d258-0e2d-496e-8c1c-113bc707912f" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n", - "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n", - " warnings.warn(\n", - "Your max_length is set to 300, but your input_length is only 201. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=100)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Summary:\n", - "The aim of the project is to create an easy-to-use, lightweight, cross-browser, general-purpose 3D library. Current builds only include a WebGL renderer but WebGPU (experimental), SVG, and CSS3D renderers are also available as addons.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "#merge this logic" - ], - "metadata": { - "id": "dQStsnBF1HoH" - }, - "execution_count": 121, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Function to summarize the README content\n", - "def summarize_readme(content):\n", - " if not content:\n", - " return \"No content to summarize.\"\n", - "\n", - " # Join all paragraphs to form a single string\n", - " full_text = \" \".join(content)\n", - "\n", - " # Check if the length of the full text exceeds a reasonable length\n", - " max_length = 1024 # Set a limit for the summarization model\n", - " if len(full_text) > max_length:\n", - " full_text = full_text[:max_length] # Truncate the text\n", - "\n", - " try:\n", - " # Summarize the text using the summarization model\n", - " summary = summarization_model(full_text, max_length=130, min_length=30, do_sample=False)\n", - " return summary[0]['summary_text'] # Extract the summary text\n", - " except Exception as e:\n", - " print(f\"Error during summarization: {e}\")\n", - " return \"Error occurred during summarization.\"\n", - "\n", - "# The rest of your existing code follows here...\n" - ], - "metadata": { - "id": "5JvYcAJZ1_6R" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def scrape_readme(url):\n", - " response = requests.get(url)\n", - "\n", - " # Check if the request was successful\n", - " if response.status_code != 200:\n", - " print(f\"Failed to fetch {url}: {response.status_code}\")\n", - " return None, \"No description found\", [], []\n", - "\n", - " soup = BeautifulSoup(response.text, 'html.parser')\n", - "\n", - " # Extract the title (usually in

)\n", - " title_tag = soup.find('h1')\n", - " title = title_tag.get_text(strip=True) if title_tag else \"No title found\"\n", - "\n", - " # Find the first

after the title\n", - " content_paragraphs = soup.find_all('p')\n", - "\n", - " # Extract the description (first

after title)\n", - " description = soup.find('p').get_text(strip=True) if soup.find('p') else \"No description found\"\n", - "\n", - " # Extract all paragraphs for main content\n", - " content = [para.get_text(strip=True) for para in content_paragraphs]\n", - "\n", - " # Extract links from the README content\n", - " links = []\n", - " for link in soup.find_all('a', href=True):\n", - " links.append((link.get_text(strip=True), link['href']))\n", - "\n", - " return title, description, content, links\n", - "\n" - ], - "metadata": { - "id": "91Ctgwym-POF" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Scrape the trending repositories and extract README summaries\n", - "def scrape_trending_repositories(limit=10): # Set a default limit\n", - " topics_url = 'https://github.com/topics'\n", - " response = requests.get(topics_url)\n", - " content = response.text\n", - "\n", - " doc = BeautifulSoup(content, 'html.parser')\n", - " topic_titles = doc.find_all('p', {'class': 'f3 lh-condensed mb-0 mt-1 Link--primary'})\n", - "\n", - " topic_titles_ = []\n", - " topic_urls = []\n", - "\n", - " for tag in topic_titles:\n", - " topic_titles_.append(tag.text)\n", - " topic_urls.append(\"https://github.com\" + tag.parent.parent.find_all('a', {'class': 'no-underline flex-grow-0'}, href=True)[0]['href'])\n", - "\n", - " all_repo_data = []\n", - "\n", - " # Loop through each topic URL to get repository info\n", - " for topic_url in topic_urls:\n", - " response = requests.get(topic_url)\n", - " content = BeautifulSoup(response.text, 'html.parser')\n", - " repositories = content.find_all('h3', {'class': \"f3 color-fg-muted text-normal lh-condensed\"})\n", - "\n", - " for repo in repositories:\n", - " if len(all_repo_data) >= limit: # Check if the limit is reached\n", - " break\n", - "\n", - " user = repo.find_all('a')[0].text.strip()\n", - " repo_name = repo.find_all('a')[1].text.strip()\n", - " repo_url = \"https://github.com\" + repo.find_all('a')[1]['href']\n", - "\n", - " # Get the stars (if available)\n", - " star_tag = repo.find_next('span', {'id': 'repo-stars-counter-star'})\n", - " if star_tag:\n", - " star_text = star_tag.text.strip().replace(',', '')\n", - " if 'k' in star_text:\n", - " star_text = star_text.replace('k', '') # Remove the 'k'\n", - " stars = int(float(star_text) * 1000) # Convert to integer after multiplying by 1000\n", - " else:\n", - " stars = int(float(star_text)) # Convert directly to integer\n", - " else:\n", - " stars = 0\n", - "\n", - " # Scrape the README content for the repository\n", - " readme_title, readme_description, readme_content, readme_links = scrape_readme(repo_url)\n", - "\n", - " # Summarize the README content\n", - " readme_summary = summarize_readme(readme_content)\n", - "\n", - " # Collect all data in a dictionary\n", - " repo_data = {\n", - " 'username': user,\n", - " 'repo': repo_name,\n", - " 'stars': stars,\n", - " 'repo_url': repo_url,\n", - " 'readme_content': readme_content,\n", - " 'readme_summary': readme_summary,\n", - " 'readme_links': readme_links\n", - " }\n", - " all_repo_data.append(repo_data)\n", - "\n", - " return all_repo_data\n", - "\n", - "# Execute the scraping and save to CSV\n", - "repo_data = scrape_trending_repositories()\n", - "df_repos = pd.DataFrame(repo_data)\n", - "df_repos.to_csv('trending_repositories_with_readme_summary.csv', index=False)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ezO1ZiIL2C46", - "outputId": "40ba8bb9-b96d-455c-803c-6b96ca3300af" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Your max_length is set to 130, but your input_length is only 125. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=62)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "df_repos.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 3222 - }, - "id": "jiUyQk3j2TfO", - "outputId": "a40c9d06-7bdd-4bfd-c0f6-99d9b9479b1b" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " username repo stars \\\n", - "0 mrdoob three.js 102000 \n", - "1 pmndrs react-three-fiber 27300 \n", - "2 libgdx libgdx 23300 \n", - "3 BabylonJS Babylon.js 23200 \n", - "4 ssloy tinyrenderer 20400 \n", - "\n", - " repo_url \\\n", - "0 https://github.com/mrdoob/three.js \n", - "1 https://github.com/pmndrs/react-three-fiber \n", - "2 https://github.com/libgdx/libgdx \n", - "3 https://github.com/BabylonJS/Babylon.js \n", - "4 https://github.com/ssloy/tinyrenderer \n", - "\n", - " readme_content \\\n", - "0 [We read every piece of feedback, and take you... \n", - "1 [We read every piece of feedback, and take you... \n", - "2 [We read every piece of feedback, and take you... \n", - "3 [We read every piece of feedback, and take you... \n", - "4 [We read every piece of feedback, and take you... \n", - "\n", - " readme_summary \\\n", - "0 The aim of the project is to create an easy-to... \n", - "1 react-three-fiber is aReact rendererfor three... \n", - "2 libGDX is a cross-platform Java game developm... \n", - "3 WARNING: The CDN should not be used in product... \n", - "4 Gitpod is a free online dev evironment for Git... \n", - "\n", - " readme_links \n", - "0 [(Skip to content, #start-of-content), (, /), ... \n", - "1 [(Skip to content, #start-of-content), (, /), ... \n", - "2 [(Skip to content, #start-of-content), (, /), ... \n", - "3 [(Skip to content, #start-of-content), (, /), ... \n", - "4 [(Skip to content, #start-of-content), (, /), ... 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usernamerepostarsrepo_urlreadme_contentreadme_summaryreadme_links
0mrdoobthree.js102000https://github.com/mrdoob/three.js[We read every piece of feedback, and take you...The aim of the project is to create an easy-to...[(Skip to content, #start-of-content), (, /), ...
1pmndrsreact-three-fiber27300https://github.com/pmndrs/react-three-fiber[We read every piece of feedback, and take you...react-three-fiber is aReact rendererfor three...[(Skip to content, #start-of-content), (, /), ...
2libgdxlibgdx23300https://github.com/libgdx/libgdx[We read every piece of feedback, and take you...libGDX is a cross-platform Java game developm...[(Skip to content, #start-of-content), (, /), ...
3BabylonJSBabylon.js23200https://github.com/BabylonJS/Babylon.js[We read every piece of feedback, and take you...WARNING: The CDN should not be used in product...[(Skip to content, #start-of-content), (, /), ...
4ssloytinyrenderer20400https://github.com/ssloy/tinyrenderer[We read every piece of feedback, and take you...Gitpod is a free online dev evironment for Git...[(Skip to content, #start-of-content), (, /), ...
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Mon Sep 17 00:00:00 2001 From: Niraj Parmar <107384357+Niraj1608@users.noreply.github.com> Date: Thu, 17 Oct 2024 15:36:16 +0530 Subject: [PATCH 4/6] Create README.md --- .../AI-RESUME-ANALYZER/README.md | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 Generative Models/AI-RESUME-ANALYZER/README.md diff --git a/Generative Models/AI-RESUME-ANALYZER/README.md b/Generative Models/AI-RESUME-ANALYZER/README.md new file mode 100644 index 00000000..47873a7b --- /dev/null +++ b/Generative Models/AI-RESUME-ANALYZER/README.md @@ -0,0 +1,22 @@ +# Resume Analyzer + +A tool that uses OpenAI's API to analyze resumes, providing keyword extraction and insights to improve matching with job descriptions. + +## Features + +- Analyze resumes using OpenAI's GPT models. +- Extract key information such as skills, experience, and education. +- Provide suggestions for resume improvements based on job descriptions. + +## Requirements + +You'll need the following libraries: + +```bash +pip install openai +``` + +--- +## Usage +Ensure your API key is set as an environment variable. +Run the resume analysis script to analyze a resume. From 272f3e9768fc0c43ca48ad07725bd3229c4d0583 Mon Sep 17 00:00:00 2001 From: Niraj Parmar <107384357+Niraj1608@users.noreply.github.com> Date: Thu, 17 Oct 2024 20:56:56 +0530 Subject: [PATCH 5/6] Add files via upload --- ..._Powered_Resume_Analyzer_with_gemini.ipynb | 521 ++++++++++++++++++ 1 file changed, 521 insertions(+) create mode 100644 Generative Models/AI-RESUME-ANALYZER/AI_Powered_Resume_Analyzer_with_gemini.ipynb diff --git a/Generative Models/AI-RESUME-ANALYZER/AI_Powered_Resume_Analyzer_with_gemini.ipynb b/Generative Models/AI-RESUME-ANALYZER/AI_Powered_Resume_Analyzer_with_gemini.ipynb new file mode 100644 index 00000000..bd61193a --- /dev/null +++ b/Generative Models/AI-RESUME-ANALYZER/AI_Powered_Resume_Analyzer_with_gemini.ipynb @@ -0,0 +1,521 @@ +{ + "cells": [ + { + "cell_type": "code", + "source": [ + "import google.generativeai as genai\n", + "from PyPDF2 import PdfReader\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n" + ], + "metadata": { + "id": "h7M0gvD4jP9X" + }, + "execution_count": 70, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "genai.configure(api_key=\"GEMINI_API_KEY\")" + ], + "metadata": { + "id": "0GyZS_wSDsGV" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "generation_config = {\n", + " \"temperature\": 1,\n", + " \"top_p\": 0.95,\n", + " \"top_k\": 64,\n", + " \"max_output_tokens\": 8192,\n", + " \"response_mime_type\": \"text/plain\",\n", + "}\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "N59C89bWiUtP" + }, + "execution_count": 62, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Function to interact with Gemini AI\n", + "def gemini_generate_response(prompt):\n", + " model = genai.GenerativeModel(\n", + " model_name=\"gemini-1.5-flash\",\n", + " generation_config=generation_config,\n", + " )\n", + "\n", + " chat_session = model.start_chat(\n", + " history=[\n", + " {\"role\": \"user\", \"parts\": [prompt]},\n", + " ]\n", + " )\n", + "\n", + " response = chat_session.send_message(prompt)\n", + " return response.text" + ], + "metadata": { + "id": "hDk1YrwXisu_" + }, + "execution_count": 63, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# 1. **PDF Parsing**\n", + "def parse_pdf(pdf_path):\n", + " reader = PdfReader(pdf_path)\n", + " text = \"\"\n", + " for page in reader.pages:\n", + " text += page.extract_text()\n", + " return text\n", + "\n", + "# 2. **Split Text into Chunks**\n", + "def split_text_into_chunks(text):\n", + " text_splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=200, length_function=len)\n", + " return text_splitter.split_text(text=text)\n", + "\n" + ], + "metadata": { + "id": "BHVoDu8YZnh8" + }, + "execution_count": 64, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# 3. **Summarize Resume**\n", + "def resume_summary(chunks):\n", + " prompt = f'''\n", + " Provide a detailed summarization of the resume below:\n", + "\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " {chunks}\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " '''\n", + " return gemini_generate_response(prompt)\n", + "\n", + "# 4. **Analyze Strengths**\n", + "def resume_strength(chunks):\n", + " prompt = f'''\n", + " Analyze the strengths of the resume below and provide detailed insights:\n", + "\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " {chunks}\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " '''\n", + " return gemini_generate_response(prompt)\n", + "\n", + "# 5. **Analyze Weaknesses**\n", + "def resume_weakness(chunks):\n", + " prompt = f'''\n", + " Analyze the weaknesses of the resume below and suggest improvements to make it better:\n", + "\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " {chunks}\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " '''\n", + " return gemini_generate_response(prompt)\n", + "\n", + "# 6. **Job Title Suggestions**\n", + "def job_title_suggestion(chunks):\n", + " prompt = f'''\n", + " Based on the resume content below, suggest suitable job roles:\n", + "\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " {chunks}\n", + " \"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\"\n", + " '''\n", + " return gemini_generate_response(prompt)\n", + "\n" + ], + "metadata": { + "id": "2wu8dMF0ZrUk" + }, + "execution_count": 65, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Main Execution\n", + "\n", + "# Parse the PDF and get the text content\n", + "pdf_path = \"/content/resume.pdf\"\n", + "resume_text = parse_pdf(pdf_path)\n", + "\n", + "# Split the resume text into smaller chunks\n", + "chunks = split_text_into_chunks(resume_text)" + ], + "metadata": { + "id": "NgEOTY_DZsrb" + }, + "execution_count": 66, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "print(chunks)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mqboP6g3jhEu", + "outputId": "7c433bd8-3969-4956-c276-95b3914705ad" + }, + "execution_count": 67, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['Tasmia Chowdhur y \\n Mobile: (347) 272- 5559 | tasmiachowdhury123 @gmail.com | LinkedIn: https://www.linkedin.com/in/tasmiachow/ | \\n Github: https://github.com/tasmiachow \\n EDUCATION \\n CUNY Hunter College Expected Graduation: December 2024 \\n ● Major: Computer Science \\n ❖ Minor: Applied Mathematics \\n ● Relevant Coursework: Discrete Structures, Data Structures and Algorithms, Web Development, Computer Architecture, \\n Symbolic Logic, Operating Systems, Computer Theory, CyberSecurity \\n EXPERIENCE \\n CUNY Tech Prep July 2023 – Present \\n Fellow, Software Development/Data Science', 'Symbolic Logic, Operating Systems, Computer Theory, CyberSecurity \\n EXPERIENCE \\n CUNY Tech Prep July 2023 – Present \\n Fellow, Software Development/Data Science \\n ● Selected for a competitive data science fellowship with students from across the 11 CUNY senior colleges where \\n Fellows create technical projects using tools such as Python 3, Jupyter Notebooks, Pandas, Numpy, Scikit-learn, and \\n SQL. \\n ● Participate in weekly courses and learn industry best practices for exploratory data analysis (EDA), feature engineering, \\n data collection and processing, statistical modeling, data visualization, machine learning techniques, data science \\n processes, and big data.', 'data collection and processing, statistical modeling, data visualization, machine learning techniques, data science \\n processes, and big data. \\n Google, New York, NY July 2023 - October 2023 \\n Software Engineering Fellow \\n ● Selected as one of 80 distinguished participants for a 10-week internship, engaging in real-world technical challenges \\n alongside a Senior Google SWE via intensive mentoring sessions and workshops. \\n ● Tackled intricate technical problems, honing problem-solving abilities and self-reliance to advance skills in Data \\n Structures and Algorithm efficiency. \\n Barclays, Hanover NJ December 2022 - February 2023 \\n Data Engineering Intern', \"Structures and Algorithm efficiency. \\n Barclays, Hanover NJ December 2022 - February 2023 \\n Data Engineering Intern \\n ● Enhanced data accessibility for Risk, Finance, and Treasury teams by employing Python's Pandas and data frames, \\n enabling seamless extraction and analysis of credit rating scores and enterprises, resulting in a 30% reduction in data \\n processing time. \\n ● Led daily scrum meetings to foster a collaborative environment and ensure alignment with team objectives. \\n ● Transformed raw data into dynamic visuals using Matplotlib, which allowed a clearer comprehension of financial trends \\n and patterns for key stakeholders.\", '● Transformed raw data into dynamic visuals using Matplotlib, which allowed a clearer comprehension of financial trends \\n and patterns for key stakeholders. \\n ● Amplified the utility of developed scripts by presenting and productizing innovative solutions to senior management, \\n effectively translating complex technical concepts into actionable tools \\n Digital Girl Inc., Brooklyn NY January 2018 - September 2024 \\n Program Administrator, Lead Instructor \\n ● Spearheaded development and implementation of new program modules, integrating emerging technologies and \\n industry-relevant content to create 21 flexible lesson plans for K-12 students and Senior Citizens.', 'industry-relevant content to create 21 flexible lesson plans for K-12 students and Senior Citizens. \\n ● Instructed interactive coding sessions, employing Python, Scratch, Javascript, and HTML/CSS to impart technical skills \\n and digital literacy among the youth. \\n ● Facilitated partnerships with schools and community organizations to expand program reach and accessibility, \\n increasing participation by 25% within targeted communities and underserved areas. \\n Girls Who Code @Hunter College, New York, NY August 2023 - August 2024 \\n Public Relations Manager \\n ● Developed and executed impactful communication campaigns, leveraging social media platforms and press releases to', \"Public Relations Manager \\n ● Developed and executed impactful communication campaigns, leveraging social media platforms and press releases to \\n amplify the club's initiatives and events, resulting in a 30% increase in membership within one academic semester. \\n ● Crafted visually captivating flyers, posters, and videos using Adobe Photoshop, Instagram Reels, and Illustrator. \\n Achieved a 45% increase in social media engagement. \\n PROJECTS \\n ● Library Record System/API: An object-oriented program using C++ \\n ● Hand2Words: A machine learning/data science project focusing on translating ASL to English in real-time. \\n TECHNICAL SKILLS\", '● Hand2Words: A machine learning/data science project focusing on translating ASL to English in real-time. \\n TECHNICAL SKILLS \\n ● Programming: C++, HTML, CSS, Python, JavaScript, Node.js, Express, Bash \\n ● Technologies: Spyder, Xcode, Git/GitHub, Adobe Photoshop, Figma, Unix, Linux, Tableau, MongoDB \\n LANGUAGES \\n ● Bilingual: English and Bangla.']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#print the summary\n", + "print(resume_summary(chunks))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 610 + }, + "id": "V4CF69LRjmiJ", + "outputId": "b31fadc4-5902-4049-a786-7c95a404a7fe" + }, + "execution_count": 71, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "## Tasmia Chowdhury's Resume Summary:\n", + "\n", + "Tasmia Chowdhury is a highly motivated and skilled Computer Science student at CUNY Hunter College, expected to graduate in December 2024 with a minor in Applied Mathematics. She possesses a strong academic background with relevant coursework in areas like Data Structures, Algorithms, Web Development, and Cybersecurity. \n", + "\n", + "Tasmia's professional experience demonstrates her passion for software development and data science. She has held several internships and fellowship positions, highlighting her practical skills and eagerness to learn. \n", + "\n", + "**Key Highlights:**\n", + "\n", + "* **Data Science & Software Development Experience:** Tasmia has actively participated in various data science and software development projects, showcasing her proficiency in Python, SQL, and other related tools. \n", + "* **Problem-Solving & Technical Skills:** She has tackled complex technical challenges in real-world scenarios, showcasing her ability to think critically and creatively solve problems.\n", + "* **Leadership & Collaboration:** Tasmia has actively participated in collaborative environments, taking initiative in leading teams and fostering a positive work environment.\n", + "* **Strong Communication & Presentation Skills:** She effectively communicates complex technical concepts to both technical and non-technical audiences, demonstrating her ability to present information in a clear and concise manner.\n", + "* **Community Engagement:** Tasmia has a strong commitment to community engagement, demonstrating her passion for empowering youth through education and technology.\n", + "\n", + "**Specific Achievements:**\n", + "\n", + "* **Google Software Engineering Fellow:** Selected as one of 80 distinguished participants in a 10-week internship program at Google, focusing on real-world technical challenges.\n", + "* **CUNY Tech Prep Data Science Fellow:** Selected for a competitive data science fellowship, working on technical projects using Python, Jupyter Notebooks, Pandas, and other relevant tools.\n", + "* **Barclays Data Engineering Intern:** Enhanced data accessibility for various teams using Python's Pandas and data frames, leading to a 30% reduction in data processing time.\n", + "* **Digital Girl Inc. Program Administrator & Lead Instructor:** Spearheaded development and implementation of new program modules, creating 21 flexible lesson plans for K-12 students and senior citizens.\n", + "\n", + "**Project Experience:**\n", + "\n", + "* **Library Record System/API:** Developed an object-oriented program using C++.\n", + "* **Hand2Words:** Developed a machine learning project focusing on translating ASL to English in real-time.\n", + "\n", + "**Technical Skills:**\n", + "\n", + "* Programming: C++, HTML, CSS, Python, JavaScript, Node.js, Express, Bash\n", + "* Technologies: Spyder, Xcode, Git/GitHub, Adobe Photoshop, Figma, Unix, Linux, Tableau, MongoDB\n", + "\n", + "**Overall, Tasmia Chowdhury is a promising Computer Science student with a strong academic foundation, practical skills, and a passion for data science and software development. She is a valuable asset to any team and has the potential to contribute significantly to the technology industry.** \n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#print strength\n", + "print(resume_strength(chunks))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 610 + }, + "id": "nACbc74cjv-z", + "outputId": "27a9e8f2-7a99-4b58-e90a-f531c9d22e65" + }, + "execution_count": 72, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "This resume showcases several strengths, highlighting Tasmia's skills and experiences in a clear and concise manner. Let's break down the key strengths:\n", + "\n", + "**1. Strong Focus on Technical Skills and Experiences:**\n", + "\n", + "* **Relevant Coursework:** Tasmia lists a diverse range of technical coursework, demonstrating a solid foundation in Computer Science, including key areas like Data Structures and Algorithms, Web Development, and Cybersecurity.\n", + "* **Quantifiable Achievements:** Tasmia provides clear examples of her accomplishments in each role. She uses specific numbers to illustrate her impact, such as a 30% reduction in data processing time at Barclays or a 25% increase in participation at Digital Girl Inc. This approach makes her contributions more tangible and impactful.\n", + "* **Technical Skills Section:** The \"TECHNICAL SKILLS\" section effectively lists programming languages (C++, Python, JavaScript, etc.) and technologies (Git/GitHub, Adobe Photoshop, Tableau, MongoDB). This section clearly showcases Tasmia's proficiency in various tools and platforms.\n", + "\n", + "**2. Well-Structured and Easy-to-Read:**\n", + "\n", + "* **Clear Formatting:** The resume follows a standard format with clear headings and bullet points. This makes it easy for recruiters to quickly grasp the information presented.\n", + "* **Organized by Categories:** The resume is logically structured with sections for Education, Experience, Projects, Technical Skills, and Languages, creating a seamless flow of information.\n", + "* **Concise Language:** The descriptions are concise and focused on showcasing key achievements and skills, avoiding unnecessary jargon or overly verbose language.\n", + "\n", + "**3. Demonstrated Passion and Engagement:**\n", + "\n", + "* **Multiple Relevant Roles:** Tasmia has held several internships and roles in various fields, indicating her eagerness to gain experience in different areas of software development and data science.\n", + "* **Active Involvement in Extracurriculars:** Her involvement in \"Girls Who Code @Hunter College\" and \"CUNY Tech Prep\" demonstrates her commitment to the field and her desire to contribute to the tech community.\n", + "* **Personal Projects:** The inclusion of personal projects like \"Library Record System/API\" and \"Hand2Words\" suggests Tasmia is driven and enjoys taking initiative in exploring new technologies and concepts.\n", + "\n", + "**4. Professional Presentation:**\n", + "\n", + "* **Contact Information:** Tasmia provides her mobile phone number, email address, LinkedIn profile, and GitHub repository. This makes it easy for potential employers to contact her and learn more about her work.\n", + "* **Strong Language:** The resume uses action verbs and positive language to highlight her abilities and achievements, leaving a good impression on the reader.\n", + "\n", + "**Areas for Improvement:**\n", + "\n", + "* **Expand on Project Descriptions:** While projects are listed, providing a brief overview of their scope, goals, and challenges could strengthen the resume.\n", + "* **Tailor to Specific Roles:** For future resumes, Tasmia could tailor her experience and skills to match specific job descriptions for greater impact.\n", + "\n", + "**Overall:** \n", + "This resume is a strong starting point. With a few minor adjustments, Tasmia can further enhance her resume to stand out in a competitive job market. \n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#print weakness\n", + "print(resume_weakness(chunks))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "x0DOnNz9kagC", + "outputId": "58b1becc-3e2a-4f27-9896-2d795f414c1f" + }, + "execution_count": 73, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "This resume has some strengths, like a good list of technical skills and relevant experience. However, it needs significant improvement to be truly effective. Here's a breakdown of the weaknesses and suggestions for improvement:\n", + "\n", + "**Weaknesses:**\n", + "\n", + "1. **Format:** The resume is in a list format which is very hard to read and understand. A traditional resume structure with clear headings and sections is essential.\n", + "2. **Spacing and Aesthetics:** There is no consistent spacing, making the content look cluttered and unprofessional.\n", + "3. **Inconsistent Bullet Point Style:** Using both \"●\" and \"❖\" for bullet points creates an inconsistent look.\n", + "4. **Redundant Information:** Some information is repeated, like the \"EXPERIENCE\" heading appearing multiple times.\n", + "5. **Lack of Quantifiable Results:** While you mention increases and reductions, there's a lack of quantifiable data to really showcase the impact of your accomplishments.\n", + "6. **Limited Detail in Projects:** The \"PROJECTS\" section is brief. Expand with details about the project scope, your role, and the technologies used.\n", + "7. **Unclear Objective:** The resume doesn't clearly articulate your career goals.\n", + "\n", + "**Improvements:**\n", + "\n", + "**1. Resume Structure:**\n", + "\n", + "* **Use a standard resume format:** Adopt a traditional resume layout with clear sections for:\n", + " * **Contact Information:**\n", + " * Full Name (Bold)\n", + " * Phone Number\n", + " * Email Address \n", + " * LinkedIn Profile URL (Optional)\n", + " * Github Profile URL (Optional)\n", + " * **Summary/Objective:** A concise paragraph outlining your skills, experience, and career aspirations.\n", + " * **Education:** Include your degree, expected graduation date, major, and any relevant minors or coursework.\n", + " * **Experience:** List your work experience in reverse chronological order. Use action verbs and quantify your accomplishments. \n", + " * **Projects:** Expand upon the details of your projects, providing clear descriptions of what you built, your contributions, and the technologies used.\n", + " * **Skills:** Categorize your skills (Programming, Technologies, Languages).\n", + "\n", + "**2. Formatting:**\n", + "\n", + "* **Use a consistent font:** Choose a professional, easy-to-read font (e.g., Arial, Calibri, Times New Roman).\n", + "* **Spacing:** Ensure consistent spacing between headings, bullet points, and paragraphs. Use white space to improve readability. \n", + "* **Bullet Points:** Use a single consistent bullet point style (e.g., \"●\") throughout the document. \n", + "\n", + "**3. Content Improvements:**\n", + "\n", + "* **Summary/Objective:**\n", + " * This section is essential for highlighting your key skills and career goals. \n", + " * Example: \"Highly motivated and results-driven Computer Science student with a strong foundation in data science and software development, seeking an entry-level role to contribute to innovative projects.\"\n", + "* **Experience:**\n", + " * **Quantify your accomplishments:** Instead of saying \"increased participation,\" provide specific metrics (e.g., \"increased participation by 25%\"). \n", + " * **Focus on accomplishments, not tasks:** Describe the impact of your work.\n", + " * **Tailor to your target roles:** Highlight relevant skills and experience for the jobs you're applying for.\n", + "* **Projects:**\n", + " * **Expand on details:** Explain the project's purpose, your role, the technologies used, and any outcomes (e.g., results, recognition, or awards). \n", + "* **Skills:**\n", + " * Include specific technologies and tools relevant to your desired role.\n", + "\n", + "**4. Proofreading and Editing:**\n", + "\n", + "* **Proofread carefully:** Ensure there are no grammatical errors or typos.\n", + "* **Have someone else review your resume:** A fresh perspective can help identify areas for improvement.\n", + "\n", + "**Example (Partial) of Restructured Resume:**\n", + "\n", + "```\n", + "**Tasmia Chowdhury**\n", + "(347) 272-5559 | tasmiachowdhury123@gmail.com | https://www.linkedin.com/in/tasmiachow/\n", + "https://github.com/tasmiachow\n", + "\n", + "**Summary**\n", + "Highly motivated and results-driven Computer Science student with a passion for data science and software development. Seeking an entry-level role where I can leverage my technical skills to contribute to innovative projects. Proven ability to collaborate effectively in team environments and deliver impactful results. \n", + "\n", + "**Education**\n", + "CUNY Hunter College, New York, NY | Expected Graduation: December 2024\n", + "* Bachelor of Science in Computer Science\n", + "* Minor: Applied Mathematics\n", + "* Relevant Coursework: Discrete Structures, Data Structures and Algorithms, Web Development, Computer Architecture, Symbolic Logic, Operating Systems, Computer Theory, Cybersecurity\n", + "\n", + "**Experience**\n", + "\n", + "**CUNY Tech Prep Fellow, Software Development/Data Science** | July 2023 - Present\n", + "* Selected for a competitive data science fellowship, collaborating with students from across the 11 CUNY senior colleges.\n", + "* Developed technical projects using Python 3, Jupyter Notebooks, Pandas, Numpy, Scikit-learn, and SQL.\n", + "* Contributed to a 25% increase in program participation by facilitating partnerships with schools and community organizations.\n", + "\n", + "**Software Engineering Fellow** | Google, New York, NY | July 2023 - October 2023\n", + "* Engaged in real-world technical challenges during a 10-week internship with intensive mentoring sessions and workshops.\n", + "* Tackled intricate technical problems, honing problem-solving abilities and self-reliance. \n", + "* Advanced skills in data structures and algorithm efficiency through hands-on projects.\n", + "\n", + "**Data Engineering Intern** | Barclays, Hanover NJ | December 2022 - February 2023\n", + "* Enhanced data accessibility for Risk, Finance, and Treasury teams, leading to a 30% reduction in data processing time. \n", + "* Utilized Python's Pandas and data frames to extract and analyze credit rating scores and enterprises.\n", + "* Led daily scrum meetings to foster a collaborative environment and ensure alignment with team objectives.\n", + "\n", + "**Projects**\n", + "\n", + "* **Library Record System/API:** Developed an object-oriented program using C++ to manage library records, improving efficiency and user experience.\n", + "* **Hand2Words:** Developed a machine learning/data science project to translate American Sign Language to English in real-time, leveraging Python libraries for image processing and natural language processing.\n", + "\n", + "**Skills**\n", + "\n", + "**Programming:** C++, HTML, CSS, Python, JavaScript, Node.js, Express, Bash\n", + "**Technologies:** Spyder, Xcode, Git/GitHub, Adobe Photoshop, Figma, Unix, Linux, Tableau, MongoDB \n", + "**Languages:** Bilingual: English and Bangla \n", + "```\n", + "\n", + "**Remember:** This is a sample. You need to tailor your resume to your specific skills and the roles you are targeting. \n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#print jobroles\n", + "print(job_title_suggestion(chunks))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 540 + }, + "id": "RjTbL3k0kesI", + "outputId": "001490ce-8367-49ae-bcd2-7f94e46bb0a7" + }, + "execution_count": 74, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Based on the resume content, here are some suitable job roles for Tasmia Chowdhury:\n", + "\n", + "**Entry-Level Roles:**\n", + "\n", + "* **Junior Data Scientist:** Her experience with data analysis, machine learning, and tools like Python, Pandas, Numpy, and Scikit-learn make her a strong candidate for entry-level data scientist roles.\n", + "* **Junior Software Engineer:** Her skills in Python, JavaScript, C++, and experience in web development and data structures make her a good fit for entry-level software engineering positions.\n", + "* **Data Analyst:** Her ability to collect, process, analyze, and visualize data, along with experience using SQL, would be valuable for data analyst roles. \n", + "* **Data Engineer:** Her skills in Python, Pandas, data transformation, and experience with databases make her a suitable candidate for entry-level data engineering roles.\n", + "\n", + "**Roles Leveraging her Skills and Experience:**\n", + "\n", + "* **Machine Learning Engineer:** Her projects and experience with machine learning techniques make her a potential candidate for this role. \n", + "* **Full Stack Developer:** Her proficiency in both front-end (HTML, CSS, JavaScript) and back-end (Python, Node.js) technologies make her well-suited for full-stack development roles.\n", + "* **Data Science Consultant:** With her data science knowledge and experience, she could potentially work as a consultant helping organizations with data analysis and machine learning projects.\n", + "\n", + "**Factors to Consider:**\n", + "\n", + "* **Job Market:** Research the current job market and the specific needs of the companies she is interested in.\n", + "* **Desired Industry:** Does she have a preference for a particular industry like finance, technology, or healthcare?\n", + "* **Location:** Her experience suggests she is open to working in New York or New Jersey, but she should consider her geographic preferences.\n", + "\n", + "**Additional Tips:**\n", + "\n", + "* **Tailor Her Resume:** Customize her resume for each specific job application, highlighting the most relevant skills and experiences.\n", + "* **Networking:** Attending industry events and connecting with professionals in her desired fields can increase her chances of landing a job.\n", + "* **Projects:** Continue building her portfolio with personal projects, particularly in machine learning or software development.\n", + "\n", + "By focusing on these roles and tailoring her resume and approach, Tasmia can increase her chances of securing a suitable position in the tech industry. \n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "8sQUg9FGkiVE" + }, + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + }, + "orig_nbformat": 4, + "colab": { + "provenance": [] + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 63dbca50faeb24d80749d799fadd2b1e133d0eda Mon Sep 17 00:00:00 2001 From: Niraj Parmar <107384357+Niraj1608@users.noreply.github.com> Date: Thu, 17 Oct 2024 21:15:51 +0530 Subject: [PATCH 6/6] Update README.md --- .../AI-RESUME-ANALYZER/README.md | 35 +++++++++++++------ 1 file changed, 24 insertions(+), 11 deletions(-) diff --git a/Generative Models/AI-RESUME-ANALYZER/README.md b/Generative Models/AI-RESUME-ANALYZER/README.md index 47873a7b..979b3b04 100644 --- a/Generative Models/AI-RESUME-ANALYZER/README.md +++ b/Generative Models/AI-RESUME-ANALYZER/README.md @@ -1,22 +1,35 @@ # Resume Analyzer -A tool that uses OpenAI's API to analyze resumes, providing keyword extraction and insights to improve matching with job descriptions. +This project leverages AI capabilities to analyze resumes, extracting key information and providing insights for improving matching with job descriptions. It offers comprehensive analysis by identifying strengths, weaknesses, and suitable job roles for candidates. ## Features -- Analyze resumes using OpenAI's GPT models. -- Extract key information such as skills, experience, and education. -- Provide suggestions for resume improvements based on job descriptions. +- **AI-Powered Resume Analysis**: Uses OpenAI and Gemini AI to analyze the content of resumes. +- **Keyword Extraction**: Extracts key information such as skills, experiences, and educational background. +- **Strengths and Weaknesses Identification**: Provides a detailed analysis of the candidate's strengths and areas of improvement. +- **Job Role Suggestions**: Suggests relevant job roles based on resume content to enhance the candidate's job search. +- **Resume Summarization**: Condenses the resume into a concise summary for quick evaluation. + +## Key Models + +- **Gemini AI**: Provides resume analysis and recommendations for improvement based on strengths, weaknesses, and job role suggestions. +- **OpenAI GPT**: Facilitates advanced NLP tasks, keyword extraction, and analysis. ## Requirements -You'll need the following libraries: +To run this project, ensure you have the following installed: -```bash -pip install openai -``` +- OpenAI library for AI functionalities. +- Gemini AI setup for advanced analysis. ---- ## Usage -Ensure your API key is set as an environment variable. -Run the resume analysis script to analyze a resume. + +1. Ensure that your API key is set as an environment variable for both OpenAI and Gemini AI. +2. Use the resume analyzer tool to provide detailed insights, keyword extraction, and job recommendations based on the candidate's resume. +3. The tool processes resumes in PDF format and splits the content into meaningful chunks for effective analysis. + +## Potential Use Cases + +- **Job Seekers**: Improve resumes based on AI-generated insights. +- **HR Professionals**: Quickly analyze candidate resumes to identify strengths and suitable roles. +- **Career Coaches**: Provide feedback and recommendations for career growth based on resume analysis.

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