كيفية إنشاء بوت دردشة بالذكاء الاصطناعي باستخدام بايثون في 5 دقائق

pip install chatterbot
pip install tensorflow

import chatterbot
import tensorflow as tf
import numpy as np
import json

# Load the dataset
with open('movie_lines.txt', 'r') as file:
    data = file.readlines()

# Preprocess the data
text = []
for line in data:
    text.append(line.strip().split(' +++$+++ ')[-1])

# Create a tokenizer to preprocess the text data
tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=10000)
tokenizer.fit_on_texts(text)

# Convert the text data to sequences
sequences = tokenizer.texts_to_sequences(text)

# Pad the sequences to make them all the same length
padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, padding='post')

# Split the data into training and test sets
train_data = padded_sequences[:30000]
test_data = padded_sequences[30000:]

# Create the model
model = tf.keras.Sequential()
model.add(tf.keras.layers.Embedding(10000, 64, input_length=padded_sequences.shape[1]))
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)))
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(10000, activation='softmax'))

# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
model.fit(train_data, train_data, epochs=10, batch_size=64)

# Evaluate the model
test_loss, test_accuracy = model.evaluate(test_data, test_data)
print('Test Loss: {}'.format(test_loss))
print('Test Accuracy: {}'.format(test_accuracy))

# Function to encode the input text
def encode_text(text, tokenizer):
    sequences = tokenizer.texts_to_sequences([text])
    padded_sequences = tf.keras.preprocessing.sequence.pad_sequences(sequences, padding='post')
    return padded_sequences

# Function to generate a response from the model
def generate_response(model, text, tokenizer):
    encoded_text = encode_text(text, tokenizer)
    prediction = model.predict(encoded_text)
    response = tokenizer.sequences_to_texts(prediction.argmax(axis=-1))[0]
    return response

# Example usage
input_text = "Hello, how are you today?"
response = generate_response(model, input_text, tokenizer)
print('Response: {}'.format(response))