Semantic similarity chatbots from plain-text files https://gist.github.com/aparrish/92c25d4f57eb7186670e03105a991508

from-plain-text.py

#!/usr/bin/env python3.6
# -*- coding: utf-8 -*-
# coding: utf-8

# # Semantic similarity chatbots from plain-text files
#
# By [Allison Parrish](http://www.decontextualize.com/)
#
# This needs copy, sorry :( :( :( But it works!

input_file = "gutenberg_conversations.txt"

get_ipython().system('pip install https://github.com/aparrish/semanticsimilaritychatbot/archive/master.zip')

from semanticsimilaritychatbot import SemanticSimilarityChatbot
import spacy
nlp = spacy.load('en_core_web_lg')

chatbot = SemanticSimilarityChatbot(nlp, 300)

lastline = None
for line in open(input_file):
    line = line.strip()
    # empty lines mean "end of conversation"
    if line == "":
        lastline = None
        continue
    if lastline is not None:
        chatbot.add_pair(lastline, line)
    lastline = line

chatbot.build()

chatbot.response_for("Hello there.")

get_ipython().system('pip install flask')

chatbot_html = """
<style type="text/css">#log p { margin: 5px; font-family: sans-serif; }</style>
<div id="log"
     style="box-sizing: border-box;
            width: 600px;
            height: 32em;
            border: 1px grey solid;
            padding: 2px;
            overflow: scroll;">
</div>
<input type="text" id="typehere" placeholder="type here!"
       style="box-sizing: border-box;
              width: 600px;
              margin-top: 5px;">
<script>
function paraWithText(t) {
    let tn = document.createTextNode(t);
    let ptag = document.createElement('p');
    ptag.appendChild(tn);
    return ptag;
}
document.querySelector('#typehere').onchange = async function() {
    let inputField = document.querySelector('#typehere');
    let val = inputField.value;
    inputField.value = "";
    let resp = await getResp(val);
    let objDiv = document.getElementById("log");
    objDiv.appendChild(paraWithText('😀: ' + val));
    objDiv.appendChild(paraWithText('🤖: ' + resp));
    objDiv.scrollTop = objDiv.scrollHeight;
};
async function getResp(val) {
    let resp = await fetch("/response.json?sentence=" +
        encodeURIComponent(val));
    let data = await resp.json();
    return data['result'];
}
</script>
"""
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route("/response.json")
def response():
    sentence = request.args['sentence']
    return jsonify(
        {'result': chatbot.response_for(sentence)})
@app.route("/")
def home():
    return chatbot_html
app.run()