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import nltk |
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nltk.download('punkt') |
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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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from tensorflow.python.util.nest import is_sequence_or_composite |
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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 bag_of_words(s, words): |
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bag = [0 for _ in range(len(words))] |
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s_words = nltk.word_tokenize(s) |
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s_words = [stemmer.stem(word.lower()) for word in s_words] |
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for se in s_words: |
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for i, w in enumerate(words): |
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if w == se: |
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bag[i] = 1 |
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return np.array(bag) |
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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(label="Chat") |
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css = """ |
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footer {display:none !important} |
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.output-markdown{display:none !important} |
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.gr-button-primary { |
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z-index: 14; |
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height: 43px; |
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width: 130px; |
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left: 0px; |
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top: 0px; |
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padding: 0px; |
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cursor: pointer !important; |
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background: none rgb(17, 20, 45) !important; |
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border: none !important; |
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text-align: center !important; |
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font-family: Poppins !important; |
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font-size: 14px !important; |
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font-weight: 500 !important; |
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color: rgb(255, 255, 255) !important; |
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line-height: 1 !important; |
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border-radius: 12px !important; |
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transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; |
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box-shadow: none !important; |
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} |
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.gr-button-primary:hover{ |
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z-index: 14; |
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height: 43px; |
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width: 130px; |
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left: 0px; |
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top: 0px; |
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padding: 0px; |
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cursor: pointer !important; |
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background: none rgb(37, 56, 133) !important; |
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border: none !important; |
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text-align: center !important; |
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font-family: Poppins !important; |
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font-size: 14px !important; |
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font-weight: 500 !important; |
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color: rgb(255, 255, 255) !important; |
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line-height: 1 !important; |
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border-radius: 12px !important; |
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transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important; |
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box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important; |
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} |
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.hover\:bg-orange-50:hover { |
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--tw-bg-opacity: 1 !important; |
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background-color: rgb(229,225,255) !important; |
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} |
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div[data-testid="user"] { |
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background-color: #253885 !important; |
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} |
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.h-\[40vh\]{ |
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height: 70vh !important; |
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} |
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""" |
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demo = gr.Interface( |
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chat, |
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[gr.Textbox(lines=1, label="Message"), "state"], |
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[chatbot, "state"], |
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allow_flagging="never", |
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title="Mental Health Bot | Data Science Dojo", |
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css=css |
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) |
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if __name__ == "__main__": |
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demo.launch() |
