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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() |