import nltk

# nltk.download("punkt")
# nltk.download("wordnet")
# nltk.download("popular")
from nltk.stem import WordNetLemmatizer

nltk.data.path.append("./nltkd/")

lemmatizer = WordNetLemmatizer()
import pickle
import numpy as np

from keras.models import load_model

model = load_model("F:\KanhaSays-main\Deployment\model.h5")
import json
import random

intents = json.loads(open("F:/KanhaSays-main/Deployment/gita_intents.json", encoding="utf-8").read())
words = pickle.load(open("F:/KanhaSays-main/Deployment/texts.pkl", "rb"))
classes = pickle.load(open("F:/KanhaSays-main/Deployment/labels.pkl", "rb"))


def clean_up_sentence(sentence):
    # tokenize the pattern - split words into array
    sentence_words = nltk.word_tokenize(sentence)
    # stem each word - create short form for word
    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):
    global result
    try:
        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
    except:
        result = "I cannot understand this statement. Perhaps rephrase it or type it differently?"
    return result


def chatbot_response(msg):
    ints = predict_class(msg, model)
    res = getResponse(ints, intents)
    return res


# s = "how to attain moksha?"
# print(chatbot_response(s))

from flask import Flask, render_template, request, flash

# import pymysql

# # MySQL configuration
# db = pymysql.connect(
#     host="localhost",
#     user="root",
#     password="",
#     database="feedback"
# )


app = Flask(__name__)
app.secret_key = "5636-d7b6-d647-dd45-e434-8551-f27b-680d"
# app.static_folder = 'static'


@app.route("/")
def home():
    return render_template("/index.html")


@app.route("/ask", methods=["POST", "GET"])
def get_bot_response():
    que = request.form["question"]
    ans = chatbot_response(que)
    return render_template("/index.html", answer=ans, question=que)

# @app.route('/ask',methods=["POST","GET"])
# def feedback():
#     return render_template('/error.html')

# @app.route('/submit-feedback', methods=['POST'])
# def submit_feedback():
#     name = request.form['name']
#     email = request.form['email']
#     feedback = request.form['feedback']

#     # Insert feedback into database
#     cursor = db.cursor()
#     sql = "INSERT INTO feedback (name, email, feedback) VALUES (%s, %s, %s)"
#     values = (name, email, feedback)
#     cursor.execute(sql, values)
#     db.commit()

#     # Redirect user back to index page with success message
#     flash('Thank you for your feedback!')
#     return render_template('/index.html')

if __name__ == "__main__":
    app.debug = True
    app.run()