An intent-based chatbot in python with tflearn and tensorflow. It can be trained for a specific purpose and works well within that specific scope. The intents.json file can be updated based on purpose and even if statement pattern given from the user varies from the patterns on which model is trained, the model still will give accurate results. The model gives probabilities for different tags based on the input and then appropriate response corresponding to that tag is returned. That response is also piped out with pyttx3 which is a text-to-speech conversion library.

Clone the repo to your local machine:

https://github.com/Adiprogrammer7/intent_based_chatbot.git

Install dependencies:

pip install -r requirements.txt

You can run main.py directly as model is already trained on some sample data. But you can change the scope of chatbot by updating intents.json file and then training the model by running training_chatbot.ipynb.

You can see here, the inputs given by the user are not identical to the patterns mentioned in intents.json file, still model is efficient enough to predict for the trained scope.