Email chat bot enabled by Chat GPT API
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Post a project like this£10/hr(approx. $12/hr)
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Quick Graphic Designer + Animator + Video Editor + Photo Editor + Logo Designer + Autocad Designer
Sydney
PPH #1 Service Provider in Development & IT : Wordpress|Magento|React Native|Mobile App Development|Angular|Node.js
Mohali
64769069130405910915490407759010024895863688974678770368836059871697346658108128342
Description
Experience Level: Entry
the algorithm would need to perform the following steps:
Preprocessing: The algorithm should first preprocess the incoming message to remove any irrelevant information or formatting that could interfere with the ChatGPT API's analysis. This may include tasks such as removing HTML tags, stripping out non-text characters, and converting the message to lowercase.
Analysis: The preprocessed message should then be sent to the ChatGPT API for analysis. The API will use its natural language processing capabilities to understand the content of the message and generate an appropriate response.
Response generation: Once the ChatGPT API has generated a response, the algorithm should format the response and send it back to the originator. This may involve tasks such as adding salutations, formatting the response as a text message or email, and attaching any necessary files or links.
Ideal Tech Stack:
Programming Languages: Python or Node.js
Web Frameworks: Flask or Express.js
Natural Language Processing Library: spaCy or NLTK
Chatbot Framework: Rasa or Botpress
Database: MongoDB or PostgreSQL
API Integration: RESTful API
Cloud Platform: AWS or Google Cloud Platform
The ChatGPT API is already handling the natural language processing and response generation, so the application can focus on handling the incoming messages and formatting the responses. Python or Node.js can be used for the back-end development, and Flask or Express.js can be used as the web framework to handle incoming requests from messaging platforms such as WhatsApp or email. A chatbot framework such as Rasa or Botpress can be used to manage the message queuing and response generation. A database such as MongoDB or PostgreSQL can be used to store user data and track engagement metrics. RESTful API can be used to integrate the ChatGPT API with the back-end system. Finally, the application can be deployed on a cloud platform such as AWS or Google Cloud Platform to ensure scalability and reliability.
Preprocessing: The algorithm should first preprocess the incoming message to remove any irrelevant information or formatting that could interfere with the ChatGPT API's analysis. This may include tasks such as removing HTML tags, stripping out non-text characters, and converting the message to lowercase.
Analysis: The preprocessed message should then be sent to the ChatGPT API for analysis. The API will use its natural language processing capabilities to understand the content of the message and generate an appropriate response.
Response generation: Once the ChatGPT API has generated a response, the algorithm should format the response and send it back to the originator. This may involve tasks such as adding salutations, formatting the response as a text message or email, and attaching any necessary files or links.
Ideal Tech Stack:
Programming Languages: Python or Node.js
Web Frameworks: Flask or Express.js
Natural Language Processing Library: spaCy or NLTK
Chatbot Framework: Rasa or Botpress
Database: MongoDB or PostgreSQL
API Integration: RESTful API
Cloud Platform: AWS or Google Cloud Platform
The ChatGPT API is already handling the natural language processing and response generation, so the application can focus on handling the incoming messages and formatting the responses. Python or Node.js can be used for the back-end development, and Flask or Express.js can be used as the web framework to handle incoming requests from messaging platforms such as WhatsApp or email. A chatbot framework such as Rasa or Botpress can be used to manage the message queuing and response generation. A database such as MongoDB or PostgreSQL can be used to store user data and track engagement metrics. RESTful API can be used to integrate the ChatGPT API with the back-end system. Finally, the application can be deployed on a cloud platform such as AWS or Google Cloud Platform to ensure scalability and reliability.
Daniel H.
100% (55)Projects Completed
72
Freelancers worked with
69
Projects awarded
12%
Last project
15 Feb 2024
United Kingdom
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