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""" |
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Created on Wed Jan 18 23:30:18 2023 |
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@author: AADITYA |
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""" |
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import nltk |
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from nltk.stem.lancaster import LancasterStemmer |
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stemmer = LancasterStemmer() |
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import numpy |
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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 pickle |
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with open("intents.json") as file: |
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data = json.load(file) |
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try: |
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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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except: |
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words = [] |
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labels = [] |
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docs_x = [] |
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docs_y = [] |
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for intent in data["intents"]: |
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for pattern in intent["patterns"]: |
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wrds = nltk.word_tokenize(pattern) |
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words.extend(wrds) |
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docs_x.append(wrds) |
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docs_y.append(intent["tag"]) |
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if intent["tag"] not in labels: |
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labels.append(intent["tag"]) |
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words = [stemmer.stem(w.lower()) for w in words if w not in "?"] |
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words = sorted(list(set(words))) |
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labels = sorted(labels) |
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training = [] |
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output = [] |
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out_empty = [0 for _ in range(len(labels))] |
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for x, doc in enumerate(docs_x): |
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bag = [] |
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wrds = [stemmer.stem(w) for w in doc] |
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for w in words: |
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if w in wrds: |
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bag.append(1) |
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else: |
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bag.append(0) |
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output_row = out_empty[:] |
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output_row[labels.index(docs_y[x])] = 1 |
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training.append(bag) |
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output.append(output_row) |
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training = numpy.array(training) |
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output = numpy.array(output) |
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with open("data.pickle", "wb") as f: |
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pickle.dump((words,labels, training, output), f) |
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tensorflow.compat.v1.reset_default_graph() |
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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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try: |
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model.load("model.tflearn") |
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except: |
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model.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True) |
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model.save("model.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 numpy.array(bag) |
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def chat(): |
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print("start talking with the bot (type quit to stop)!") |
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while True: |
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inp = input("you: ") |
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if inp.lower() == "quit": |
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break |
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results = model.predict([bag_of_words(inp, words)])[0] |
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results_index = numpy.argmax(results) |
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tag = labels[results_index] |
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if results[results_index] > 0.7: |
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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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print(random.choice(responses)) |
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else: |
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print("I didn't get that, try again") |
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chat() |
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