• Create a virtual environment of python-3.
• Fork and Clone the GitHub repository onto your local machine.
• Install the dependencies as specified here.
• Load the pre-trained model by following the steps specified here.
• Run and test the Cakechat Server as specified here

• Check this website to have an idea of what IBM Tone Analyzer API would be doing.
• Create an account on IBM Cloud (It's free)
• Enable the Tone Analyzer Service for your account from here.
• Try running the Python code for analyzing the conversation from here and don't forget to replace {apikey} and {url} with the apikey and url you received by enabling Tone Analyser Service for your account.

• Create API account on Last.fm from here and get the API_KEY
• Test the following API's features with the help of your API_KEY
  • Getting top 5 songs of a particular tag
  • Getting similar songs related to a song

• Let's start the conversation with a good mood tone:

  Hey there!! What's up? How's the day?

• Now let's send this text for Emotional Analysis. And we get the following response:

  {
    "document_tone": {
      "tones": [
        {
          "score": 0.66525,
          "tone_id": "joy",
          "tone_name": "Joy"
        }
      ]
    },
    "sentences_tone": [
      {
        "sentence_id": 0,
        "text": "Hey there!! What's up?",
        "tones": []
      },
      {
        "sentence_id": 1,
        "text": "How's the day?",
        "tones": []
      }
    ]
  }

• Now send the request to the chatbot server with the emotion of chatbot using the following mapping:

  {
      "anger": "anger",
      "fear":"sadness",
      "joy":"joy",
      "sadness":"sadness",
      "analytical":"neutral",
      "confident":"neutral",
      "tentative":"neutral"
  }

Why this mapping? Because the Cakechat chatbot supports conversation in limited emotions only.

Just wondering... how can you have an analytical conversation? ... but still, you can have an analytical tone ... Therefore we are mapping Analytical tone to Neutral conversation. 

So, using the above mapping we would be talking to the server.

• Now, send the request to the cakechat server with the conversation and the emotion of the chat.

  Request:

  {
      "context": [
          "Hey there!! What's up? How's the day?"
      ],
      "emotion": "joy"
  } 
  

  Response:

  {
      "response": "It's been good. How about you?"
  }
  

  As you can see we got the response. So, you can continue further with the conversation appended in the context variable.

• Now, it's time to get a song recommendation. Have a look and analyze the number of songs of a particular tag using last.fm tag.gettoptracks API.

  {
      "tracks": {
          "track": [
              {...},
              {...}
          ],
          "@attr": {
              "tag": "analytical",
              "page": "1",
              "perPage": "5",
              "totalPages": "1",
              "total": "2"
          }
      }
  }
  

  You would have seen here also that we were only able to find 2 songs corresponding to the analytical tag. So, instead of using the tags returned by the Emotional API, we can map them using the mapping defined earlier above.

• Now, once a user would click on a particular song, we also need to recommend similar songs to that particular song. This can also be done using the last.fm's track.getsimilar API using the track and the artist of the song.

• Building the following API's

  • /tone
    • input:
      • the context required for the chatbot
    • output: the tone-analysis of the conversation
  • /response
    • input:
      • the context required for the chatbot
      • the tone from /tone
    • output: the chatbot response
  • /songs
    • input:
      • the tag for getting songs from last.fm API
    • output:
      • list of songs
  • /simmilarsongs
    • input:
      • the song name
      • the artist name
    • output:
      • list of songs

• We can make a template inside your Django/flask app which has a page similar to this:

Here, on the left, I have the songs recommended to the user, and on the right-hand side are the songs which were recommended based on one of the songs which I clicked on.

Further, the emotion of the chat is shown at the top