import nltk
nltk.download('punkt')

import nltk
from nltk.stem.lancaster import LancasterStemmer
import numpy as np
import tflearn
import tensorflow
import random
import json
import pandas as pd
import pickle
import gradio as gr

stemmer = LancasterStemmer()

with open("intents.json") as file:
    data = json.load(file)

with open("data.pickle", "rb") as f:
  words, labels, training, output = pickle.load(f)

net = tflearn.input_data(shape=[None, len(training[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(output[0]), activation="softmax")
net = tflearn.regression(net)

model = tflearn.DNN(net)
model.load("MentalHealthChatBotmodel.tflearn")
# print('model loaded successfully')


def chat(message, history):
    history = history or []
    message = message.lower()
    results = model.predict([bag_of_words(message, words)])
    results_index = np.argmax(results)
    tag = labels[results_index]

    for tg in data["intents"]:
      if tg['tag'] == tag:
        responses = tg['responses']

        # print(random.choice(responses))
        response = random.choice(responses)
  
    history.append((message, response))
    return history, history

chatbot = gr.Chatbot().style(color_map=("green", "pink"))
demo = gr.Interface(
    chat,
    ["text", "state"],
    [chatbot, "state"],
    allow_flagging="never",
)
if __name__ == "__main__":
    demo.launch()