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# libraries
import random
import numpy as np
import pickle
import json
from flask import Flask, render_template, request
from flask_ngrok import run_with_ngrok
import nltk
from keras.models import load_model
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()
# chat initialization
model = load_model("chatbot_model.h5")
# intents = json.loads(open("intents.json").read())
data_file = open("F:\\Data Science Course - IIITB\\NLP\\Chatbot\\AI Chatbot\\An-AI-Chatbot-in-Python-and-Flask-main\\intents.json").read()
words = pickle.load(open("words.pkl", "rb"))
classes = pickle.load(open("classes.pkl", "rb"))
app = Flask(__name__)
# run_with_ngrok(app)
@app.route("/")
def home():
return render_template("index.html")
# @app.route("/get", methods=["POST"])
# def chatbot_response():
# msg = request.form["msg"]
# if msg.startswith('my name is'):
# name = msg[11:]
# ints = predict_class(msg, model)
# res1 = getResponse(ints, intents)
# res =res1.replace("{n}",name)
# elif msg.startswith('hi my name is'):
# name = msg[14:]
# ints = predict_class(msg, model)
# res1 = getResponse(ints, intents)
# res =res1.replace("{n}",name)
# else:
# ints = predict_class(msg, model)
# res = getResponse(ints, intents)
# return res
#Updated
# ... Your previous code ...
@app.route("/get", methods=["POST"])
def chatbot_response():
msg = request.form["msg"]
# Load and process the intents JSON file
data_file = open("F:\\Data Science Course - IIITB\\NLP\\Chatbot\\AI Chatbot\\An-AI-Chatbot-in-Python-and-Flask-main\\intents.json").read()
intents = json.loads(data_file)
# Rest of your existing code
if msg.startswith('my name is'):
name = msg[11:]
ints = predict_class(msg, model)
res1 = getResponse(ints, intents)
res = res1.replace("{n}", name)
elif msg.startswith('hi my name is'):
name = msg[14:]
ints = predict_class(msg, model)
res1 = getResponse(ints, intents)
res = res1.replace("{n}", name)
else:
ints = predict_class(msg, model)
res = getResponse(ints, intents)
return res
# chat functionalities
def clean_up_sentence(sentence):
sentence_words = nltk.word_tokenize(sentence)
sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words]
return sentence_words
# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
def bow(sentence, words, show_details=True):
# tokenize the pattern
sentence_words = clean_up_sentence(sentence)
# bag of words - matrix of N words, vocabulary matrix
bag = [0] * len(words)
for s in sentence_words:
for i, w in enumerate(words):
if w == s:
# assign 1 if current word is in the vocabulary position
bag[i] = 1
if show_details:
print("found in bag: %s" % w)
return np.array(bag)
def predict_class(sentence, model):
# filter out predictions below a threshold
p = bow(sentence, words, show_details=False)
res = model.predict(np.array([p]))[0]
ERROR_THRESHOLD = 0.25
results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD]
# sort by strength of probability
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 getResponse(ints, intents_json):
tag = ints[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
if __name__ == "__main__":
app.run()
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