Q and A interface and code update - Screen bids taken as final bid - complete info here

I need help to update and add new features to the existing code written in Python. Specifically, I'm looking to create a Q and A interface that allows me to quickly get answers to basic questions. The bids you provide will be taken as the final acceptable bid and should detail exactly what will be done in order to fulfill the project requirements. Task 1: Update current Q & A code with Note style ready 3 columns and looping to get thew highest confidence answer: Notebook code explanation. Your code imports the necessary libraries, loads data from a CSV file, and defines functions for searching the data based on a user's question. The code also creates an interactive user interface using Jupyter widgets to input the question and the number of answers to display. Here's a step-by-step explanation of the code: Import necessary libraries like pandas, [login to view URL], ipywidgets, and nltk. Load the CSV data into a pandas DataFrame and fill missing values with empty strings. Define a search_data function that takes a question, a dataframe, and the number of results to return as input. This function tokenizes the question, removes stopwords, and searches the dataframe for matching keywords, similes, and answers. It returns the top matching answers based on the input question. Define an on_button_click function to handle the search button click event. This function clears the output widget, gets the input question and number of results from the input box and slider, calls the search_data function, and displays the results in the output widget. Attach the on_button_click function to the search button click event. Display the input box, number of answers slider, search button, and output widget. When you run this code in a Jupyter Notebook, you'll see an interactive user interface for searching the data based on a question. The user can input a question, select the number of answers to display, and click the "Search" button to view the matching answers. Task 2: Overview of the code: Import necessary libraries and modules. Initialize the Flask app Define global variables and settings, such as roles, upload folder path, and NLTK-related settings. Define helper functions for the chatbot, including: Functions for searching answers and generating a CSV file. Define Flask routes and corresponding functions: '/pythonlogin/process_file': Processes the uploaded file and other form data, saves the information to the database, generates answers for the questions in the file, and sends the result as a CSV file. '/process_description': Generates answers for user queries based on the dataset and returns the results as a JSON object. Run the Flask app. This application allows users to submit questions and receive answers based on the dataset for a specific product. The dataset contains questions, answers, keywords, and similes related to the product. The chatbot uses TF-IDF and cosine similarity to find the most relevant answer for a given question. Task 3: extend code to creating functional UI from previous Q & A code that can do the following UI example included The Task extend UI and update current code System would upload a csv file in questions in the first column text box will show hox many cells in CSV and with number this question is output of total. The system would auto populate the 2nd column with the Answers. The UI would parse the csv with a question text box and the initial auto-populate best answers. Only 1 question per page. On the right side of the screen are optional answers in an array with a check box next to each. If the box is checked in an optional answer, that answer swaps places initial answer. User can edit the field manually. Buttons include: 'Next Question' this is save of is the current answer text field. And move to next answer. 'Save' this will save current work to this point. 'Download' It will give a popup asking yes or no if wish to save before downloading than download csv in current state on completion.