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import nltk |
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nltk.download('punkt') |
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import nltk |
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from nltk.stem.lancaster import LancasterStemmer |
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import numpy as np |
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import tflearn |
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import tensorflow |
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import random |
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import json |
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import pandas as pd |
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import pickle |
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import gradio as gr |
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stemmer = LancasterStemmer() |
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with open("intents.json") as file: |
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data = json.load(file) |
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with open("data.pickle", "rb") as f: |
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words, labels, training, output = pickle.load(f) |
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net = tflearn.input_data(shape=[None, len(training[0])]) |
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net = tflearn.fully_connected(net, 8) |
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net = tflearn.fully_connected(net, 8) |
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net = tflearn.fully_connected(net, len(output[0]), activation="softmax") |
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net = tflearn.regression(net) |
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model = tflearn.DNN(net) |
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model.load("MentalHealthChatBotmodel.tflearn") |
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def chat(message, history): |
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history = history or [] |
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message = message.lower() |
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results = model.predict([bag_of_words(message, words)]) |
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results_index = np.argmax(results) |
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tag = labels[results_index] |
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for tg in data["intents"]: |
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if tg['tag'] == tag: |
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responses = tg['responses'] |
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response = random.choice(responses) |
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history.append((message, response)) |
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return history, history |
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chatbot = gr.Chatbot().style(color_map=("green", "pink")) |
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demo = gr.Interface( |
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chat, |
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["text", "state"], |
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[chatbot, "state"], |
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allow_flagging="never", |
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) |
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if __name__ == "__main__": |
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demo.launch() |
