Chatbot - Summer Internship Project 2019

Project description

A generalised chatbot which can intelligently respond to conversation using named entity recognition (NER), sentiment analysis and an implementation of a decision system for follow up mechanism using a Seq2seq model which uses Bidirectional RNNs and Attention models.

Encoder-Decoder Model

Instructions

Create an IBM Cloud account. Follow the steps mentioned in the following link- https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/get-started-wdp.html

Using the dialogflow api https://cloud.google.com/dialogflow/docs/quickstart-api

Installation

To run the chatbot program you must install the following libraries -

1. Install spacy

pip install spacy

2. Install tensorflow

pip install tensorflow

3. Install dialogflow

pip install dialogflow

4. Install textblob

pip install textblob

5. Install tqdm

pip install tqdm

6. Install tensorlayer

pip install tensorlayer

7. Install wikipedia

pip install wikipedia

8. Install Watson NLU

pip install ibm_watson

9. Install Spacy's English Model

python -m spacy download en_core_web_md

10. Install numpy

pip install numpy

11. Install pandas

pip install pandas

12. Install tensorlayer

pip install tensorlayer

API's used -

1. MediaWiki
2. Dialogflow
3. IBM Watson NLU (for sentiment analysis)

Training data

We have trained our model on three different datasets (SQuAD , Cornell Movie Dialogue Corpus and a Trump Tweets Dataset) to bring about generalisation. They are included in the 'data' folder and are included through an import of the folder.

Usage

Note - First make sure that all the files required are saved in the same directory and all the required libraries are installed before running the program. For Windows -

To run it on squad

python model.py squad

To run on cornell_corpus

python model.py cornell_corpus

To run on twitter

python model.py twitter

For Linux (For Python3+ versions) -

To run it on squad

python3 model.py squad

To run on cornell_corpus

python3 model.py cornell_corpus

To run on twitter

python3 model.py twitter

The model can be trained on the user's system or the pretrained weights (.hdf5 files) included in the respective folders can be loaded to view the output.

Results

The results of the chatbot (including the follow up) are shown below -

References

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