Chatbot-Response-Endpoint

This is a simple ML Deployment using Flask framework. Load the Universal Sentence Encoder module, which has been pre-trained on a large corpus of text. Also, To efficiently store chatbot data, save the pre-encoded responses in a JSON format. Define routes and associate them with specific functions that will handle incoming requests and provide responses. These endpoints will allow users to interact with our chatbot and utilize the search bar.

Getting started

Run Locally

Clone the project

  git clone https://github.com/C23-DF02-DiskusAI-Dicoding-Indonesia/Chatbot-Response-Endpoint.git

Go to the project directory

  cd chatbot

Install the Required Packages

Don't forget to activate the virtual enviroment first before running this command

  pip Install -r requirement.txt

This will take a while because of the large size of Tensorflow

Start the server

  python app.py

if you can't run that command, try this:

  flask run

Open a web browser and visit http://localhost:5000 to access the main page of your app.

Screenshots