371 skills found · Page 6 of 13
lucaslattari / Whatsapp BotRepositório do projeto apresentado no vídeo "Envio e Recebimento Automático de Mensagens no Whatsapp com Selenium - Python faz pra Mim 05" do canal Universo Discreto
Eviltr0N / Make AI Clone Of YourselfCloning Yourself using your whatsapp chat history and training a model on it.
Akshayjyoti / WhatsApp Spam BotPython code for a WhatsApp spam bot
Abdul1028 / Whatsapp RealityA comprehensive Python library for analyzing WhatsApp chat exports. This library provides tools for preprocessing WhatsApp chat data and performing various analyses including sentiment analysis, user activity patterns, conversation patterns, and more.
shhubhxm / Skin Disease Detection Team TechnophileWe are team technophiles and participated in 48hrs hackathon organized by Nirma University in collabration with Binghamton University. Our Problem Definition : To develop a solution, the first step is to understand the problem. The problem here is to develop an Application Programming Interface which can be easily integrated with Android and IOS to detect the skin disease without any physical interaction with a Dermatologist. The detected skin disease should be sent through whatsapp to a particular patient and doctor. Our college name: Pandit Deendayal Energy University Team Members: Rushabh Thakkar, Divy Patel, Denish Kalariya, Yug Thakkar, and Shubham Vyas. Project Details: We made an application which classifies the skin diseases into these given types healthy, lupus, ringworm and scalp_infections How did we make? The data given was analysed first. We came to conclusion that the data given was not enough so we searched for new datasets. We got these datasets: https://ieee-dataport.org/documents/image-dataset-various-skin-conditions-and-rashes https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T We segregated the datasets of harvard. Combined all the datasets and trained the tensorflow image classification model multiple times. Accuracy was not satisfying. Augmented the data to unbaised the model and the dataset would be balanced. Data Augmentation was done on the data given . We generated 800 images per disease. Again we had trained the model. Accuracy was good. Exported the .tflite and label.txt file. We imported the files into android studio We have used three python codes: data_removal.py This code is used to remove data randomly from the folder if there are more number of images than required. We just need to change total_files_req variable in the code to number of files required after deletion. data_augmentation.py This code is used to augment the data randomly from the folder if there are less number of images than required. We just need to change total_files_req variable in the code to number of files required after augmentation. We change various parameters of images like clearity, rotation, brightness, etc. image_classification_code.py This is the main code in which we have trained the model and exported it to run on the app Models we tried: efficientnet-lite0(USED in our project) efficientnet-lite1 efficientnet-lite2 efficientnet-lite3 efficientnet-lite4 API: TensorFlowLite Used Android studio for App development . Used Language = java We sync all the grade files. Changed the model files and update it with the new model Working model file name is model.tflite Tflite classifier working java files are CameraActivity.java CamerConnectionFragment.java ClasssifierActivity.java LegacyCameraConnectionFreagment.java Dataset: Uploaded on Github WORKING MODEL LINK: https://drive.google.com/file/d/1BnqfFInFkJJDkYDlmdj9VB601f7PjTdj/view?usp=sharing
karanprasadgupta / WhatsAppChatAnalzyerWhatsApp Chat Analyzer is a tool built using python that allows you to analyze and extract insights from WhatsApp chat exports.
r000t1ng / Reverse Shell WhatsappDemonstration of a critical vulnerability in WhatsApp that allows automatic execution of malicious .pyz (Python) files, leading to a reverse shell and privilege escalation on Windows systems. This exploit bypasses security checks in Windows Defender, UAC, antivirus software, and WhatsApp itself.
gendonholaholo / Python Starter Kit FastAPI WhatsApp AI ChatbotNo description available
t0mer / Ma Nishma-nish is an unofficial python wrapper for Whatsapp cloud api
judeleonard / WhatsapChat Analyzer AppThis is an analytical project done using python to process and extract valuable insights from WhatsApp text file, deployed as a webapp using Streamlit that takes in a WhatsApp chat text file as input for generating visual dashboards
abhishekbuild / Analyse Whatsapp ChatWhatsApp Chat Analyzer is a Python-based project that allows you to analyze WhatsApp chats, whether they are individual or group chats.
droidlife / PyWhatsappPython script to control whatsapp web using terminal
MrR0b0t19 / WhatsAppWebSpammerScript creado en python para envio masivos de whatsapp para SPAM
FahimFBA / Automate Text BombingA python 🐍 code script which can be used as a text bomber in any place, whether it can be messenger, WhatsApp, Signal or Telegram and so on.
adam-saudagar / Supbot2Simplest WhatsApp automation Library for Python
Alessiop01 / ForensicWace ServerEditionForensic Wace is a python tool that allows you to analyse the data contained within the WhatsApp database for iOS. It allows you to explore the data in the database and generate detailed, anti-tamper reports. This tool supports multi-threading and can be installed on a central server in your company network and use it from employees PCs
diveshlunker / WhatsallPython Library to automate messaging on whatsapp to unlimited people without having to actually save the number of the particular person
Sandreke / WhatsApp Bulk And Customized Messages Without Saving ContactsAutomate bulk and customized WhatsApp messages to unsaved numbers using Python
ZhanNexus / LilithWhatsApp Bot Built With Python
AnubhavChaturvedi-GitHub / WhatsApp Automation SuitePython-based automation for WhatsApp tasks: bulk messaging, contact management, status updates, and workflow scripting—streamlining communication for business and personal users.
