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| import nltk | |
| import numpy as np | |
| import tflearn | |
| import tensorflow as tf | |
| import random | |
| import json | |
| import pickle | |
| import gradio as gr | |
| from nltk.stem.lancaster import LancasterStemmer | |
| nltk.download('punkt') | |
| stemmer = LancasterStemmer() | |
| # Load intents file | |
| with open("intents.json") as file: | |
| data = json.load(file) | |
| # Data preprocessing | |
| words, labels, docs_x, docs_y = [], [], [], [] | |
| for intent in data["intents"]: | |
| for pattern in intent["patterns"]: | |
| wrds = nltk.word_tokenize(pattern) | |
| words.extend(wrds) | |
| docs_x.append(wrds) | |
| docs_y.append(intent["tag"]) | |
| if intent["tag"] not in labels: | |
| labels.append(intent["tag"]) | |
| # Stem and sort words | |
| words = sorted(set(stemmer.stem(w.lower()) for w in words if w not in ["?", ".", ",", "!"])) | |
| labels = sorted(labels) | |
| # Create training data | |
| training, output = [], [] | |
| out_empty = [0] * len(labels) | |
| for x, doc in enumerate(docs_x): | |
| bag = [1 if stemmer.stem(w.lower()) in [stemmer.stem(word) for word in doc] else 0 for w in words] | |
| output_row = out_empty[:] | |
| output_row[labels.index(docs_y[x])] = 1 | |
| training.append(bag) | |
| output.append(output_row) | |
| training, output = np.array(training), np.array(output) | |
| # Build and train the model | |
| tf.compat.v1.reset_default_graph() | |
| 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) | |
| try: | |
| model.load("MentalHealthChatBotmodel.tflearn") | |
| except FileNotFoundError: | |
| model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True) | |
| model.save("MentalHealthChatBotmodel.tflearn") | |
| # Function to preprocess user input | |
| def bag_of_words(s, words): | |
| bag = [0 for _ in range(len(words))] | |
| s_words = nltk.word_tokenize(s) | |
| s_words = [stemmer.stem(word.lower()) for word in s_words] | |
| for se in s_words: | |
| for i, w in enumerate(words): | |
| if w == se: | |
| bag[i] = 1 | |
| return np.array(bag) | |
| # Chat function | |
| def chat(message, history=None): | |
| history = history or [] | |
| try: | |
| bag = bag_of_words(message, words) | |
| results = model.predict([bag]) | |
| results_index = np.argmax(results) | |
| tag = labels[results_index] | |
| for tg in data["intents"]: | |
| if tg['tag'] == tag: | |
| response = random.choice(tg['responses']) | |
| break | |
| else: | |
| response = "I'm sorry, I don't have a response for that." | |
| except Exception as e: | |
| response = "I'm sorry, I couldn't understand your message." | |
| history.append((message, response)) | |
| return history, history | |
| # Gradio Interface | |
| demo = gr.Interface( | |
| fn=chat, | |
| inputs=[gr.Textbox(lines=1, label="Message"), gr.State()], | |
| outputs=[gr.Chatbot(label="Chat"), gr.State()], | |
| allow_flagging="never", | |
| title="Wellbeing for All | Generative AI Enthusiasts" | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |