app.py

app = Flask(__name__)
@app.route("/")
def home():
    return render_template("index.html")
  @app.route("/get", methods=["POST"])
def chatbot_response():
    msg = request.form["msg"]
    if msg.startswith('items'):
        name = msg[11:]
        ints = predict_class(msg, model)
        res1 = getResponse(ints, intents)
        res =res1.replace("{n}",name)
    elif msg.startswith('hi my name is'):
        name = msg[14:]
        ints = predict_class(msg, model)
        res1 = getResponse(ints, intents)
        res =res1.replace("{n}",name)
    else:
        ints = predict_class(msg, model)
        res = getResponse(ints, intents)
    return res
  # chat functionalities
def clean_up_sentence(sentence):
    sentence_words = nltk.word_tokenize(sentence)
    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
  if __name__ == "__main__":
    app.run()