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- Deploy within Flask app with jinja2 template
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- Serve only the Flask prediction API. The used html and javascript files can be included in any Frontend application (with only a slight modification) and can run completely separate from the Flask App then.
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This repo currently contains the starter files.
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```
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$ git clone https://github.com/python-engineer/chatbot-deployment.git
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$ cd chatbot-deployment
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$ python3 -m venv venv
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$ . venv/bin/activate
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```
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Install dependencies
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```
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$ (venv) pip install Flask torch torchvision nltk
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```
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Install nltk package
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```
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$ (venv) python
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>>> import nltk
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>>> nltk.download('punkt')
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```
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Modify `intents.json` with different intents and responses for your Chatbot
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Run
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```
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$ (venv) python train.py
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```
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This will dump data.pth file. And then run
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the following command to test it in the console.
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```
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$ (venv) python chat.py
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```
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Now for deployment follow my tutorial to implement `app.py` and `app.js`.
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## Watch the Tutorial
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[](https://youtu.be/a37BL0stIuM)
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[https://youtu.be/a37BL0stIuM](https://youtu.be/a37BL0stIuM)
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## Note
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In the video we implement the first approach using jinja2 templates within our Flask app. Only slight modifications are needed to run the frontend separately. I put the final frontend code for a standalone frontend application in the [standalone-frontend](/standalone-frontend) folder.
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## Credits:
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This repo was used for the frontend code:
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https://github.com/hitchcliff/front-end-chatjs
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1. Create a virtual environment using python -m venv venv
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2. Go to the venv using the command venv\Scripts\activate
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3. Install the required packages
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4. Train the model using python train.py
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5. Use python app.py to check in localhost
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