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| import gradio as gr | |
| import nltk | |
| nltk.download('popular') | |
| from nltk.stem import WordNetLemmatizer | |
| lemmatizer = WordNetLemmatizer() | |
| import pickle | |
| import numpy as np | |
| from tensorflow.keras.models import load_model | |
| model = load_model('model.h5') | |
| import json | |
| import random | |
| intents = json.loads(open('data.json').read()) | |
| words = pickle.load(open('texts.pkl', 'rb')) | |
| classes = pickle.load(open('labels.pkl', 'rb')) | |
| def clean_up_sentence(sentence): | |
| # tokenize the pattern - split words into array | |
| sentence_words = nltk.word_tokenize(sentence) | |
| # stem each word - create short form for word | |
| sentence_words = [lemmatizer.lemmatize(word.lower()) for word in sentence_words] | |
| return sentence_words | |
| # return bag of words array: 0 or 1 for each word in the bag that exists in the sentence | |
| def bow(sentence, words, show_details=True): | |
| # tokenize the pattern | |
| sentence_words = clean_up_sentence(sentence) | |
| # bag of words - matrix of N words, vocabulary matrix | |
| bag = [0] * len(words) | |
| for s in sentence_words: | |
| for i, w in enumerate(words): | |
| if w == s: | |
| # assign 1 if current word is in the vocabulary position | |
| bag[i] = 1 | |
| if show_details: | |
| print("found in bag: %s" % w) | |
| return (np.array(bag)) | |
| def predict_class(sentence, model): | |
| # filter out predictions below a threshold | |
| p = bow(sentence, words, show_details=False) | |
| res = model.predict(np.array([p]))[0] | |
| ERROR_THRESHOLD = 0.25 | |
| results = [[i, r] for i, r in enumerate(res) if r > ERROR_THRESHOLD] | |
| # sort by strength of probability | |
| results.sort(key=lambda x: x[1], reverse=True) | |
| return_list = [] | |
| for r in results: | |
| return_list.append({"intent": classes[r[0]], "probability": str(r[1])}) | |
| return return_list | |
| def getResponse(ints, intents_json): | |
| tag = ints[0]['intent'] | |
| list_of_intents = intents_json['intents'] | |
| for i in list_of_intents: | |
| if (i['tag'] == tag): | |
| result = random.choice(i['responses']) | |
| break | |
| return result | |
| def chatbot_response(msg, history): | |
| history = history or [] | |
| s = list(sum(history, ())) | |
| s.append(msg) | |
| inp = ' '.join(s) | |
| ints = predict_class(msg, model) | |
| res = getResponse(ints, intents) | |
| history.append((msg, res)) | |
| return history, history | |
| prompt = "Ask anything ..." | |
| block = gr.Blocks() | |
| with block: | |
| chatbot = gr.Chatbot() | |
| message = gr.Textbox(placeholder=prompt) | |
| state = gr.State() | |
| submit = gr.Button("SEND") | |
| submit.click(chatbot_response, inputs=[message, state], outputs=[chatbot, state]) | |
| block.launch(debug = True) |