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chatbot.py
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70 lines (61 loc) · 2.21 KB
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# -*- coding: utf-8 -*-
"""
Created on Wed Dec 30 16:08:00 2020
@author: Saurav
"""
import streamlit as st
import random
import json
import pickle
import numpy as np
import nltk
nltk.download('punkt')
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer
from tensorflow.keras.models import load_model
st.set_page_config(page_title="souravs_bot")
lemmatizer = WordNetLemmatizer()
intents = json.loads(open('intents.json').read())
words = pickle.load(open('words.pkl','rb'))
classes = pickle.load(open('classes.pkl','rb'))
model = load_model('chatbotmodel.h5')
def clean_up_sentence(sentence):
sentence_words = nltk.word_tokenize(sentence)
sentence_words = [lemmatizer.lemmatize(word) for word in sentence_words]
return sentence_words
def bag_of_words(sentence):
sentence_words = clean_up_sentence(sentence)
bag = [0]*len(words)
for w in sentence_words:
for i, word in enumerate(words):
if word == w:
bag[i]=1
return np.array(bag)
def predict_class(sentence):
bow = bag_of_words(sentence)
res = model.predict(np.array([bow]))[0]
ERROR_THRESHOLD = 0.25
results = [[i, r] for i, r in enumerate(res) if r>ERROR_THRESHOLD]
results.sort(key=lambda x: x[1], reverse=True)
return_list = []
for r in results:
return_list.append({'intent':classes[r[0]], 'probability':str(r[1])})
return return_list
def get_response(intents_list,intents_json):
tag = intents_list[0]['intent']
list_of_intents = intents_json['intents']
for i in list_of_intents:
if i['tag'] == tag:
result = random.choice(i['responses'])
break
return result
st.markdown('<style>body{text-align:center;background-color:black;color:white;align-items:center;display:flex;flex-direction:column;}</style>', unsafe_allow_html=True)
st.title("College Admission Assistant")
st.markdown("@souravsing ")
st.image("https://media3.giphy.com/media/S60CrN9iMxFlyp7uM8/200w.webp?cid=ecf05e47938v1hebb1bs1m9uysov7g7iws0j52oqku2k0yua&rid=200w.webp")
st.markdown("Hi I'm Samonit")
#print("bot is live")
message = st.text_input("You can start chat below")
ints = predict_class(message)
res = get_response(ints,intents)
st.success("Bot :{}".format(res))