interact.py

print("Initializing...")
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import pickle
import re
import webbrowser as wb
import tensorflow as tf
import numpy as np
import speech_recognition as sr
import keyboard


# Define the function to preprocess the input text
def preprocess_text(text):
    # Convert the text to lowercase
    text = text.lower()
    # Replace any non-alphanumeric characters with a space
    text = re.sub(r'[^a-zA-Z0-9\'+\-*/\s]', ' ', text)
    # Replace the word 'plus' with the '+' symbol
    text = text.replace('plus', '+')
    # Replace the word 'minus' with the '-' symbol
    text = text.replace('minus', '-')
    # Replace the word 'times' with the '*' symbol
    text = text.replace('times', '*')
    # Replace the word 'divided by' with the '/' symbol
    text = text.replace('divided by', '/')
    # Replace the characters * / - + with a space and the character itself
    text = text.replace('*', ' * ').replace('/', ' / ').replace('-', ' - ').replace('+', ' + ').replace('.', ' . ')
    # Replace all numbers with a special token
    text = re.sub(r'\d+', '<num>', text)
    # Replace tokens that are not present in the tokenizer with a special token
    text = text.split()
    text = [word if word in vocab.word_index else '<oov>' for word in text]
    text = ' '.join(text)
    # Remove any extra whitespace
    text = re.sub(r'\s+', ' ', text)
    return text.strip()


def listen_and_convert():
    recognizer = sr.Recognizer()

    with sr.Microphone() as source:
        print("Recording... Speak something:")
        audio = recognizer.listen(source)

        print("Processing...")
        try:
            text = recognizer.recognize_google(audio)
            print(text)
            process_input(text)  # Process the converted text using the existing function
        except sr.UnknownValueError:
            print("Google Speech Recognition could not understand the audio.")

        except sr.RequestError as e:
            print(f"Could not request results from Google Speech Recognition service; {e}")


def site():
    # Define a regex pattern to find URL
    url_pattern = r'(?:http://|https://)?(\S+\.(?:com|org|net|edu|gov|me|ai|io))'
    matches = re.findall(url_pattern, input_text)
    url = None

    if matches:
        url = "https://" + matches[0]
    elif "chatgpt" in input_text:
        url = "chat.openai.com"
    elif "email" in input_text:
        url = "mail.google.com"
    elif "gmail" in input_text:
        url = "mail.google.com"
    elif "youtube" in input_text:
        url = "youtube.com"
    elif "pandora" in input_text:
        url = "pandora.com"
    elif "spotify" in input_text:
        url = "spotify.com"

    if url is not None:
        print("opened " + url)
        wb.get('windows-default').open(url)
    else:
        print("Somethings wrong, check your input, if your input is good then create a bug report with the following: "
              f"category: open website, input: {input_text}")


def search():
    # Remove common words that don't contribute to search terms
    stop_words = {'search', 'for', 'find', 'on', 'the', 'web', 'do', 'do a', 'google'}
    words = input_text.split()
    filtered_words = [word for word in words if word.lower() not in stop_words]

    # Extract the remaining words as search terms
    search_terms = ' '.join(filtered_words)

    print("searched for " + search_terms)
    search_url = f"https://duckduckgo.com/?q={search_terms}"
    wb.get('windows-default').open(search_url)


def math():
    # Define a regex pattern to match basic math expressions (e.g., 2 + 3, 4 * 5, etc.)
    math_pattern = r'(\d+(\.\d+)?(\s*(\+|-|\*|\/)\s*\d+(\.\d+)?)+)'
    matches = re.findall(math_pattern, input_text)

    return matches[0][0] if matches else None


def process_input(text):
    if text == "/quit":
        return False

    processed_input_text = preprocess_text(text)
    test_data = [processed_input_text]  # Wrap the input data in a list
    test_encoded = vocab.texts_to_sequences(test_data)
    test_padded = tf.keras.preprocessing.sequence.pad_sequences(test_encoded, maxlen=15, padding='post')
    predictions = model.predict(test_padded)
    predicted_category_number = np.argmax(predictions[0])
    predicted_category = "report this as logic error 1 and with steps to reproduce."

    if predicted_category_number == 0:
        predicted_category = "math"
        math()
    elif predicted_category_number == 1:
        predicted_category = "web search"
        search()
    elif predicted_category_number == 2:
        predicted_category = "open website"
        site()
    print(f"Predicted Function: {predicted_category}")

    return True


# Load the saved model
model = tf.keras.models.load_model('main.h5')

# Load the tokenizer using pickle
with open('tokenizer.pkl', 'rb') as file:
    vocab = pickle.load(file)
    print()

# Enter into an interactive loop to test the model
while True:
    input_text = input("user: ")

    # Check for the hotkey press
    if keyboard.is_pressed('ctrl'):
        listen_and_convert()  # If hotkey pressed, start listening and convert speech to text

    if not process_input(input_text):
        break