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- "readme_text": "# LauzHack2023\nAmazon Review Tools - by Ali S., Alberto L. and Lorenzo C.\n\n## BRIEF DESCRIPTION\nOur project is designed to offer Amazon sellers an accessible and immediate interface for summarizing user reviews ¬on ¬specific products. \nThe main goal of this interface is to identify the most pressing issues associated with a selected product, significantly simplifying the reading of reviews. \nThis streamlined approach lets sellers make simpler impactful decisions to enhance the overall customer experience. \n\n## INSPIRATION \nEach day the world of e-commerce is flooded with millions of feedbacks. We believe there’s a practical way to avoid drowning in them.\nWhen you decide to inspect a product, it shouldn’t take more than a couple of minutes to understand the harshest complaints or its most appreciated features. \nWith this in mind, we designed an efficient algorithm and an especially user-friendly environment to make this a reality. \nFor the scope the hackathon we agreed to focus on making the tools for checking the reviews of a single product at a time.\n\n## WHAT IT DOES\nOur solution can allow companies selling on amazon to have an easy way to take care of the customer reviews of a product of their choice. \nThe interface transmits highly concentrated information through a pie chart that divides customer reviews in a few categories chosen by our algorithm. \nIn this way it is immediate to spot what the majority of the feedback focuses on. \nMoreover, each slice of the pie chart is colour based on the percentage of positivity of reviews in that category coming from a sentiment analysis.\n\n## CHALLENGES WE RAN INTO\n-\tLimited use of LLM credits\n-\tCut in production features (LDA)\n-\tComputational times \n\n## ACCOMPLISHMENTS THAT WE’RE PROUD OF\n- Working demo (back-end and front-end implementation)\n- Learned a lot from first hackathon\n\n## HOW WE BUILT IT\nWe paired id to reviews,\nused text2vec, \nused PCA to “standardize”, \nclustered the vectors through k means (silhouette method to choose optimal k).\nSplit the vectors in 5 macro-categories. We identified the sub-categories of clusters (lemmatization). \nWe also differentiated the data based on a sentiment analysis (NLTK with vader lexicon).\n\n\n## WHAT’S NEXT FOR \"AMAZON REVIEW TOOLS\"\n-\tSimultaneous multiple product checking\n-\tIncrease of accuracy \n-\tAmazon-like UI (seller and vendor)\n\n## CODE STRUCTURE\n```apiserver```: Serves the csv data to the frontend app\n```aws-review```: frontend app written in nextjs\n\n## Screenshot\n![screenshot](https://github.com/AliEmreSenel/LauzHack2023/blob/master/Screenshot_20231203_115457.png?raw=true)\n##",
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- "readme_text": "# OpenLogsLauz\nUnleash the power of AI and let it cook (foundue, if possible) to brew the answers to all of your questions.\n\n\n\n![Telegram Chat Demo](imgs/screenshot.png)\n\nThis is a project made in LauzHack 2023 at EPFL, Lausane, Switzerland\n\n## Inspiration\nWe realized that most of our daily programming issues can be identified by looking carefully at the logs. But what a better way to automatize this boring and tedious task. \n\n## What it does\nWe have built a Telegram chatbot that lets you upload the log file you want to analyze and what would you like to know about it. It will return the answer to your question. A good example is: \n\n\\*Uploads the log file\\*\n\nIN: ` Did the user 'jonny' remove any program in his computer?`\n\nOUT: ` Yes! The user jonny deleted the 'google-chrome-stable' on Nov 27 at 23:57:23`\n\n\n\n## How we built\nWe splited the project into two main parts: the gptCore stuff, and the Telegram API, both programmed in Python3. \n\n## How to run\n\nInstall Python3, git and venv.\n\nIf you are using a Debian/ Debian-based OSs you can:\n```sh\nsu root\napt install git\napt install Python3\napt install python-dotenv\n```\n\nGet a telegram bot API key and GPT API key. \n You can follow these instructions to create a Telegram Bot: https://core.telegram.org/bots#how-do-i-create-a-bot.\n\n\nClone our repo by:\n\n```sh\ngit clone https://github.com/EncryptEx/LauzHack23\n```\nCreate a virtualenviroment in python by:\n```sh\npython3 -m venv env\n```\nThen, enter it and install the requirements\n\n```sh\nsource env/bin/activate\npip install -r requirements.txt\n```\nCreate a .venv file using the template in the .env.example file (or just delete the \".example\" in the file) and add you API keyes.\n\nAnd finally, just run the main file!\n\n```sh\npython3 chatbot.py\n```\n\nStart chatting with the bot at [Telegram](https://t.me/openlogslauz_bot).\n\n> PROTIP: Use our generated authentication file (from /var/log/auth.log)\n\n> You can generate system log files at [see Other System Log Siles](###Other-System-Log-Files) (bottom of readme)\n\n\n\nCongrats!! The bot is ready to use.\n\n## Challenges we ran into\n\nWe used LLMs (ChatGPT) to cerate a answer taking into account the log file we needed to send a request with a lot of tokens. We started by using chatgpt3.5-turbo with 4k tokens. As a quick fix, we upgraded the version to chatgpt4-turbo-preview with aproximentely 128k tokens of context, meaning that we could upload all the file without choping it in pices. This worked quite well with the downside of being quite expensive (1€/request). Because of that, we had to dongrade the version to chatgpt3.5-16k that is a bit slower but has 4 times the amount context tokens (16k context tokens). With that, we couldn't treat large log files so we opted to chop the files in pices and paralelize the ChatGPT API requests, losing some context but being sustantialy cheaper.\n\n## Accomplishments that we made\nWe learned how to implement and use LLMs, as well as integrating them to Python scripts, and consequently, the Telegram APIs. \nWe also discovered the world of LLM models, since we thought there wouldn't exist that much. We also learned by the economical way the price of GPT4-turbo xd\n\n## What we learned\nWe learned quite a lot about Telegram bots and Chat GPT API. We learned about context in LLMs and system-logs workflow analisys.\n\n## What's next\nWe'd like to use a all-in-one model that can read a single file and ask questions in the same context/conversation, because we ran out of context window tokens because of the log file size. If you want to know how we managed to solve this issue: we opted to chop and split paraelly the data into multiple GPT3.5-turbo conversations.\n\n\n### Other System Log Siles\n| File Path | Description | Use Cases |\n|--------------------------------------------|-------------------------------------------------------|---------------------------------------------------------------|\n| `/var/log/syslog` | Primary system log file. | General system issues, service logs. |\n| `/var/log/dmesg` | Kernel ring buffer messages. | Hardware, driver issues. |\n| `/var/log/auth.log` | Authentication logs. | Security auditing, login issues. |\n| `/var/log/kern.log` | Kernel logs. | Linux kernel issues. |\n| `/var/log/boot.log` | System boot process logs. | Boot-related problems. |\n| `/var/log/dpkg.log` | Package installation and removal activity. | Package management activities. |\n| `/var/log/apache2/access.log` | Apache server access logs. | Web server access information (if Apache is used). |\n| `/var/log/apache2/error.log` | Apache server error logs. | Web server error information (if Apache is used). |\n| `/var/log/mysql.log` | MySQL general log. | MySQL database operations (if MySQL is used). |\n| `/var/log/mysql.err` | MySQL error log. | MySQL database errors (if MySQL is used). |\n| `/var/log/mail.log` | Mail server logs. | Mail server operations. |\n| `/var/log/unattended-upgrades.log` | Automatic software updates logs. | Unattended software updates. |\n| `/var/log/Xorg.0.log` | X.Org server logs. | Graphical display issues. |\n| `/var/log/ufw.log` | Uncomplicated Firewall (UFW) logs. | Firewall operations and issues. |\n| `/var/log/alternatives.log` | Update-alternatives mechanism logs. | Default command changes. |\n| `/var/log/apt/history.log` | APT command history. | Software package management history. |\n| `/var/log/apt/term.log` | Terminal output of APT operations. | Detailed view of APT operations. |\n| `/var/log/cron.log` | Cron job scheduler logs. | Scheduled task operations. |\n| `/var/log/user.log` | User-level application logs. | Application-specific issues. |\n| `/var/log/debug` | Debug messages from system and applications. | In-depth troubleshooting. |\n| `/var/log/faillog` | Failed login attempts log. | Security analysis for unauthorized access attempts. |\n| `/var/log/lastlog` | Record of last logins of all users. | User activity tracking. |\n| `/var/log/wtmp` and `/var/log/btmp` | Login/logout history and bad login attempts. | User login history and unauthorized login attempts. |\n| `/var/log/journal/` | Systemd's journal files. | Comprehensive system logging (if systemd is used). |\n| `/var/log/lightdm/lightdm.log` | LightDM display manager logs. | Desktop display manager operations (if LightDM is used). |\n| `/var/log/sysstat/` | Sysstat package logs for performance monitoring. | System performance analysis (if sysstat is used). |\n| `/var/log/anaconda.log` | Anaconda installer logs. | Installation process details (if Anaconda is used). |\n| `~/.bash_history` | User-specific command history in Bash shell. | Review and audit of user-executed commands in Bash. |\n\n\n\n## Aknowledgements\nThanks for LauzHack for hosting the event, EPFL for the venue and OpenSystems for bringing us an amazing challenge",
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