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Python project advanced: Chatbot using NLP

Python project advanced: Here’s an advanced Python project with code. This project involves building a chatbot using Natural Language Processing (NLP) techniques.

Objective of Python project advanced: Chatbot using NLP

To build a chatbot that can understand and respond to user queries in natural language.

Tools and Libraries Used

  • Python 3.6+
  • Natural Language Toolkit (NLTK)
  • TensorFlow 2.0+
  • Keras
  • Flask

Steps of python project advanced

  1. Install the necessary libraries by running the following commands in your terminal:
pip install nltk tensorflow keras flask
  1. Import the required libraries and download the NLTK data:
import nltk'punkt')
  1. Preprocess the data by tokenizing and stemming the text:
from import LancasterStemmer
stemmer = LancasterStemmer()

def preprocess(sentence):
    sentence_words = nltk.word_tokenize(sentence)
    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
    return sentence_words
  1. Load the training data and preprocess it:
import json

with open('intents.json') as file:
    data = json.load(file)

training_data = []
output_data = []

for intent in data['intents']:
    for example in intent['examples']:
  1. Create a bag of words model:
import numpy as np

words = []
for sentence in training_data:

words = sorted(list(set(words)))

output_classes = sorted(list(set(output_data)))

training = []
output = []

for i, sentence in enumerate(training_data):
    bag = [1 if word in sentence else 0 for word in words]
    output_bag = np.zeros(len(output_classes))
    output_bag[output_classes.index(output_data[i])] = 1

training = np.array(training)
output = np.array(output)
  1. Define the neural network model:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(8, input_shape=(len(training[0]),), activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(len(output_classes), activation='softmax'))
  1. Train the model:
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']), output, epochs=1000, batch_size=8)
  1. Create a function to generate responses:
def generate_response(sentence):
    sentence_words = preprocess(sentence)
    bag = [1 if word in sentence_words else 0 for word in words]
    prediction = model.predict(np.array([bag]))[0]
    max_index = np.argmax(prediction)
    if prediction[max_index] > 0.7:
        return output_classes[max_index]
        return 'unknown'
  1. Create a Flask app to host the chatbot:
from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/chatbot', methods=['POST'])
def chatbot():
    data = request.get_json()
    message = data['message']
    response = generate_response(message)
    return jsonify({'message': response})

if __name__ == '__main__':
  1. Create a JSON file to store the intents:
  "intents": [
      "intent": "greeting",
      "examples": [

What is next

Once you have completed the project, you can further improve it by:

  1. Adding more intents and examples to the training data to increase the chatbot’s accuracy.
  2. Implementing a more sophisticated NLP model such as a transformer-based model like BERT or GPT-3.
  3. Integrating the chatbot with a voice interface using a speech recognition library like Google’s Speech Recognition or Sphinx.
  4. Creating a user interface for the chatbot using a web development framework like React or Angular.

You can also explore other advanced Python projects such as building a recommendation system, image recognition, or sentiment analysis.

Categories: Python


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