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https://github.com/statute8234/ai_projects
Rating (7/10) The code is a chatbot that interacts with users based on article content. However, issues include incorrect sorting, typos, incorrect indentation, and lack of explanations for each part, potentially affecting the bot's robustness and understanding of context.
https://github.com/statute8234/ai_projects
newspaper nltk numpy python random sklearn string warnings
Last synced: about 1 month ago
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Rating (7/10) The code is a chatbot that interacts with users based on article content. However, issues include incorrect sorting, typos, incorrect indentation, and lack of explanations for each part, potentially affecting the bot's robustness and understanding of context.
- Host: GitHub
- URL: https://github.com/statute8234/ai_projects
- Owner: Statute8234
- Created: 2022-04-15T20:37:33.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-21T02:26:45.000Z (10 months ago)
- Last Synced: 2024-03-21T03:29:26.203Z (10 months ago)
- Topics: newspaper, nltk, numpy, python, random, sklearn, string, warnings
- Language: Python
- Homepage:
- Size: 35.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI_projects
The code is a chatbot that interacts with users based on article content. It imports libraries, downloads tokenization packages, retrieves articles, and generates responses. However, issues include incorrect sorting, typos in variable names, incorrect indentation of sentence lists, and insufficient context understanding. The bot response generation may not be robust, and the code lacks clear explanations for each part.
[](https://pypi.org/project/newspaper/)
[](https://pypi.org/project/random/)
[](https://pypi.org/project/string/)
[](https://pypi.org/project/nltk/)
[](https://pypi.org/project/sklearn/)
[](https://pypi.org/project/numpy/)
[](https://pypi.org/project/warnings/)
## Table of Contents
- [About](#about)
- [Features](#features)
- [Imports](#Imports)
- [Rating: 7/10](#Rating)
# About
The code is a chatbot that interacts with users based on an article's content. It imports libraries, downloads the NLTK punkt package for tokenization, retrieves an article, tokenizes the text, defines functions for sorting indices, and generates responses. The bot analyzes user input and responds with greetings or responses. However, there are several issues in the implementation, including an incorrect sorting function index_sort(), a typo in the variable name responce_flag, incorrect indentation of the sentence_list.remove(user_input) line, and a lack of comments explaining the purpose and logic behind each part. These issues could lead to unexpected behavior in finding the most relevant response.
# Features
The code describes a chatbot that can interact with users based on an article's content. It retrieves an article from a URL and uses the NLTK punkt package to tokenize the text into sentences, which are then used for generating responses to user queries. The code can analyze user input using NLP techniques like stemming, lemmatization, and cosine similarity to find the most relevant sentence from the article. It can also use pre-defined greetings and responses to handle common or generic queries.
The code defines a function called index_sort() that sorts a list of indices based on the values of another list. The code can use this function to sort the indices of sentences based on their similarity scores with the user input. A response flag is used to track whether the chatbot has found a suitable response or not. However, several issues in the implementation could affect the chatbot's performance.
The index_sort() function has a logical error in its implementation, as it uses a for loop to iterate over the list of indices. A better way to implement the sorting function is to use a built-in method like sorted(), which can sort a list based on a key function.
# Imports
newspaper, random, string, nltk, sklearn.feature_extraction.text, sklearn.metrics.pairwise, numpy, warnings
# Rating
Chatbot that extracts articles using the Newspaper3k library, preprocesses the text, and implements a basic chatbot based on cosine similarity. The code is organized into functions, making it easy to understand and maintain. It handles potential errors by providing responses even if the user input is not recognized or if the user chooses to exit the chat. The chatbot engages in conversation with the user, responding to greetings and providing relevant answers based on the input.
However, the code has some cons. The cosine similarity approach used for generating bot responses might not always provide accurate or contextually relevant answers, especially for longer conversations or more diverse inputs. The bot's response generation logic could be improved to handle a wider range of inputs and provide more diverse and contextually appropriate responses. The exit condition relies on specific exit keywords, which might not cover all possible variations or user intents for ending the conversation.
In addition to the code, there is no validation for the user's input when selecting articles. To improve readability and maintainability, it is suggested to use more sophisticated natural language processing techniques, diversify response sources, enhance the exit condition, validate article selection, and document the code. This will make it easier for other developers to understand and extend the codebase.