@@ -42,7 +42,7 @@ This project is develop using the following:-
2. Change Powershell to Comand Prompt by pressing the plus(+) symbol on top right corner of the Terminal.
3. Activate the Virtual Environment (Venv) in Comand Prompt: `Venv\scripts\activate` (Windows) or `source/Venv/bin/activate` (Linux / MacOS).
4. Now you are in your Virtual environment, Next step is to start Rasa server by: `rasa run`.
5. Now it will show in console that Rasa server is up and running, It indicates that till now your rasa is running perfectly without any errors (You are very Lucky).
5. Now it will show in console that Rasa server is up and running, It indicates that till now your rasa is running perfectly without any errors (**You are very Lucky**).
6. If rasa server is running, Then Again open a new comand prompt from Terminal without closing the previous prompt.
7. Now it's time to Run the chatbot on Streamlit. For that enter : `streamlit run app.py` in new Comand Prompt.
8. This will direct you to a default Browser, where you will find our 'Airline Chatbot'.
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@@ -92,3 +92,13 @@ Here is the overview of the requests:
- Chatbot Implementation with RASA:
1. Intent: `domain.yml` & `nlu.yml` file where it gives the intent and according to that it show the responses for the user & nlu gives the examples according to the intent.
2. Actions: `actions.py` file contains the back-end data where it read the csv file and use class function to get passenger flight details from the csv file and generate the response to users.
3. Streamlit File: `app.py` file contains the front-end data, which communicate between the user and the chatbot.
4. stories.yml: `stories.yml` where it shows the process of conversation for the chatbot.
5. rules.yml: `rules.yml` where user says goodbye and conversation end.
6. endpoints.yml: `endpoints.yml` where things runs smoothly with the help of url.
7. train models.py: `train_models.py` where it contains two models for the prediciton of the data.
Each component has it's own where it has created user- friendly chatbot and gives predictions and analysis according to the data.
