Code Refactor and Improvement – Dr. Trippers Stuff

check this and give it a greade and suggestions for improvement. I notice there is not LLM to send to? import os
from tkinter import *
from gtts import gTTS
from playsound import playsound
import pyttsx3
import speech_recognition as sr

# Initialize the pyttsx3 engine
engine = pyttsx3.init()

# Define a Recognizer that uses Google to convert audio to text
r = sr.Recognizer()

# Set up Tkinter for GUI
root = Tk()

text_speech = Text(root, height=2, width =16) # Define the text widget for displaying assistant’s response.
text_speech.pack()

def submit(): # This function is activated when user clicks on ‘Submit’. It gets the user input and sends it to LLM via OpenAI client.
text_input = text_entry.get()
audio = gTTS(text=text_input, lang=’en’) # Using gtts convert text into speech and save as mp3
filename = ‘voice.mp3’
audio.save(filename)
playsound.playsound(“voice.mp3″, True) # Plays the sound
os.remove(filename)

speech_text = engine.recognize_google(file=”voice.mp3″) # Get the result of LLM API and convert it into string
text1 = r.listenTo(speech_text,timeout=4) # Make sure you wait for a minute to get the response from Google API
try:
result = r.recognize(text1)
if result[‘transcript’] == ”:
raise StopIteration
else:
text2 = r.listenTo(result) # Make sure you wait for a minute to get the response from Google API
try:
result2 = r.recognize(text2)
if result2[‘transcript’] == ”:
raise StopIteration
else:
text3 = r.listenTo(result2) # Make sure you wait for a minute to get the response from Google API
text_output = result3[‘transcript’] + “:” + result4[‘transcript’]
except StopIteration:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
text_output = ‘ ‘.join(text_output.split()) # Convert the list of strings into a string
engine.say(text_output) # Make sure you use the transcript from LLM API
except StopIteration:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
engine.say(text_output) # Make sure you use the transcript from LLM API

def send_text():
text = text_entry.get()
text1 = r.listenTo(text,timeout=4) # Make sure you wait for a minute to get the response from Google API
try:
result = r.recognize(text1)
if result[‘transcript’] == ”:
raise StopIteration
else:
text2 = r.listenTo(result) # Make sure you wait for a minute to get the response from Google API
try:
result2 = r.recognize(text2)
if result2[‘transcript’] == ”:
raise StopIteration
else:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
except StopIteration:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
engine.say(text_output) # Make sure you use the transcript from LLM API
except StopIteration:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
engine.say(text_output) # Make sure you use the transcript from LLM API
### Instruct your user to enter text by typing in the text entry field
### The following function will convert the entered string into a tts object
### and send it to the Google Speech recognizer. This way, we can make use of the
### TTS module as well as our own transcription functions.
def tts_speak(text):

# Set up the pyttsx3 engine
engine = pyttsx3.init()
# engine = pyttsx3.Engine(‘voices/Alisa-Hungarian-12k.wav’)

# Set up the tts object and send it to Google Speech recognizer
engine.setProperty(“rate”, 160)
text_output = r.listenTo(text,timeout=4) # Make sure you wait for a minute to get the response from Google API
try:
result = r.recognize(text1)
if result[‘transcript’] == ”:
raise StopIteration
else:
text2 = r.listenTo(result) # Make sure you wait for a minute to get the response from Google API
try:
result2 = r.recognize(text2)
if result2[‘transcript’] == ”:
raise StopIteration
else:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
except StopIteration:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
except StopIteration:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
engine.say(text_output) # Make sure you use the transcription module as well as TTS

def tts_speak2():
text = ‘Hey Google, what is the weather?’

# Send your text to Google Speech recognizer and get a result of transcription
result = r.recognize(text1)
if result[‘transcript’] == ”:
raise StopIteration
else:
text2 = r.listenTo(result) # Make sure you wait for a minute to get the response from Google API
try:
result2 = r.recognize(text2)
if result2[‘transcript’] == ”:
raise StopIteration
else:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
except StopIteration:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
engine.say(text_output) # Make sure you use the transcription module as well as TTS
# Send your text to Google Speech recognizer and get a result of transcription
result = r.recognize(text1)
if result[‘transcript’] == ”:
raise StopIteration
else:
text2 = r.listenTo(result) # Make sure you wait for a minute to get the response from Google API
try:
result2 = r.recognize(text2)
if result2[‘transcript’] == ”:
raise StopIteration
else:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
except StopIteration:
text_output = result3[‘transcript’] + “:” + result4[‘transcript’] # You don’t need to wait for a minute
engine.say(text_output) # Make sure you use the transcription module as well as TTS
### END OF TESTING

Grade: D

Suggestions for improvement:

1. **Organization and Structure:**
– The code lacks proper organization and indentation, making it challenging to follow. Make sure to use consistent indentation (usually four spaces) for better readability.
– There are duplicated code blocks and unclear sections, such as the multiple occurrences of similar code snippets.

2. **Comments and Documentation:**
– Add comments to explain the purpose of each function and section of code. This will make it easier for others (and yourself) to understand the logic and functionality.
– Remove commented-out code that is not providing valuable information. It clutters the code and can be confusing.

3. **Variable Naming:**
– Some variable names are unclear or inconsistent. For example, `text1`, `result`, `result2`, and `text_output` are used without clear context. Choose more descriptive and consistent names to enhance code readability.

4. **Unused Code:**
– There are sections of code that appear to be unused or duplicated, such as the testing section at the end. Remove redundant and unused code to improve clarity.

5. **Exception Handling:**
– Exception handling is not well-implemented. The `StopIteration` exception is raised but not caught or handled appropriately. Consider using a more standard approach for exception handling, and make sure to handle exceptions where they occur.

6. **Logical Errors:**
– There are logical errors in the code, such as the use of undefined variables like `result3` and `result4`. Ensure that variables are properly defined before use.

7. **Unused Imports:**
– There are some imported libraries that are not used, like `pyttsx3` and `Tkinter`. Remove any unnecessary imports to declutter the code.

8. **Overall Design:**
– The overall design of the code is unclear. Refactor the code into smaller, well-defined functions with a clear purpose. This will make the code more modular and easier to maintain.

9. **Speech Recognition and TTS:**
– The speech recognition and text-to-speech sections seem to be mixed up and unclear. Clearly define the workflow for speech recognition and TTS, and ensure that the code follows a logical sequence.

10. **Testing Section:**
– The testing section is not well-organized and seems to contain duplicated code. Consider restructuring this section or removing unnecessary parts.

Remember to thoroughly test your code after making changes to ensure that it behaves as expected.
The bot is just arguing with me about how great it codes evben with your grade. Can you fix the code?
Certainly! I’ll provide a revised version of the code addressing the issues mentioned:

“`python
import os
import tkinter as tk
from gtts import gTTS
from playsound import playsound
import speech_recognition as sr

# Initialize the pyttsx3 engine
engine = sr.Recognizer()

# Set up Tkinter for GUI
root = tk.Tk()

text_entry = tk.Entry(root, width=50) # Use Entry instead of Text for user input
text_entry.pack()

def submit():
text_input = text_entry.get()
audio = gTTS(text=text_input, lang=’en’)
filename = ‘voice.mp3′
audio.save(filename)
playsound(filename, True)
os.remove(filename)

with sr.AudioFile(“voice.mp3”) as source:
try:
speech_text = engine.record(source)
result = engine.recognize_google(speech_text)
engine.say(result)
except sr.UnknownValueError:
engine.say(“Sorry, I couldn’t understand the audio.”)

def tts_speak(text):
engine.say(text)
engine.runAndWait()

# Set up a button to trigger the submit function
submit_button = tk.Button(root, text=”Submit”, command=submit)
submit_button.pack()

# Set up a button to trigger the TTS function
tts_button = tk.Button(root, text=”Text to Speech”, command=lambda: tts_speak(text_entry.get()))
tts_button.pack()

root.mainloop()
“`

Changes made:

1. Used `Entry` for user input instead of `Text` since it seems like a single-line input.
2. Fixed the speech recognition part using `record` method and catching `UnknownValueError` for handling unrecognized audio.
3. Simplified the code for text-to-speech (`tts_speak` function).
4. Added buttons to trigger the submit and text-to-speech functions for better user interaction.

Make sure to thoroughly test the code to ensure it meets your requirements.
It is supposed to send to : def send_text_to_llm(cleaned_text):
# Placeholder URL for the LLM API
llm_api_url = “http://127.0.0.1:5151/v1/”
# Send the cleaned text to the LLM and get the response
response = requests.post(llm_api_url, json={“prompt”: cleaned_text})
return response.json()
OK. So this will speak what I type. So who cares? I need ti to speak what the llm returns from a quesiton: import os
import tkinter as tk
from gtts import gTTS
from playsound import playsound
import speech_recognition as sr
import requests

# Initialize the speech recognition engine
engine = sr.Recognizer()

# Set up Tkinter for GUI
root = tk.Tk()

text_entry = tk.Entry(root, width=50)
text_entry.pack()

def submit():
text_input = text_entry.get()
audio = gTTS(text=text_input, lang=’en’)
filename = ‘voice.mp3′
audio.save(filename)
playsound(filename, True)
os.remove(filename)

# Load the original recorded audio file
with sr.Microphone() as source:
try:
speech_audio = engine.listen(source)

# Perform speech recognition on the recorded audio
result = engine.recognize_google(speech_audio)

# Pass the recognized text to the LLM
send_text_to_llm(result)
except sr.UnknownValueError:
print(“Sorry, I didn’t catch that.”)

def tts_speak(text):
audio = gTTS(text=text, lang=’en’)
filename = ‘output.mp3’
audio.save(filename)
playsound(filename, True)
os.remove(filename)

def send_text_to_llm(cleaned_text):
# Placeholder URL for the LLM API
llm_api_url = “http://127.0.0.1:5151/v1/”
# Send the cleaned text to the LLM and get the response
response = requests.post(llm_api_url, json={“prompt”: cleaned_text})
return response.json()

# Send POST request to LLM API
response = requests.request(‘POST’, url=llm_api_url, headers=headers, data=payload)
if response.status_code == 200:
llm_response = response.content[‘generated_text’]
tts_speak(llm_response)
else:
print(“An error occurred during communication with the LLM.”)

# Set up buttons to trigger functions
submit_button = tk.Button(root, text=”Submit”, command=submit)
submit_button.pack()
tts_button = tk.Button(root, text=”Text to Speech”, command=lambda: tts_speak(text_entry.get()))
tts_button.pack()

root.mainloop()
File “D:SarahOneGPTEgpt-engineernscript.py”, line 52, in send_text_to_llm
llm_response = response.json()[‘generated_text’]
KeyError: ‘generated_text’
It looks like there is an issue with accessing the ‘generated_text’ key in the JSON response. To handle this, you should check if the key exists before attempting to access it. Here’s an updated version of your `send_text_to_llm` function:

“`python
def send_text_to_llm(cleaned_text):
# Placeholder URL for the LLM API
llm_api_url = “http://127.0.0.1:5151/v1/”

# Send the cleaned text to the LLM and get the response
response = requests.post(llm_api_url, json={“prompt”: cleaned_text})

if response.status_code == 200:
llm_response = response.json()

# Check if ‘generated_text’ key exists in the response
if ‘generated_text’ in llm_response:
tts_speak(llm_response[‘generated_text’])
else:
print(“The key ‘generated_text’ is not present in the LLM response.”)
else:
print(“An error occurred during communication with the LLM. Status code:”, response.status_code)
“`

This modification includes a check for the existence of the ‘generated_text’ key before attempting to access it. This should resolve the ‘KeyError’ you encountered.
Not working, but here is the example code for the conntection.
# Example: reuse your existing OpenAI setup
from openai import OpenAI

# Point to the local server
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”not-needed”)

completion = client.chat.completions.create(
model=”local-model”, # this field is currently unused
messages=[
{“role”: “system”, “content”: “Always answer in rhymes.”},
{“role”: “user”, “content”: “Introduce yourself.”}
],
temperature=0.7,
)

print(completion.choices[0].message)

It seems that your code for connecting to the local LLM API is using the `openai` library, specifically the `OpenAI` class and the `chat.completions.create` method. However, in your provided code, you’re using the `requests` library directly.

If you want to use the `openai` library, you need to make sure it’s installed (`pip install openai`). Here’s an example of how you could modify your code to use the `openai` library:

“`python
import openai

# Set up OpenAI with the local server
openai.api_key = “not-needed”
openai.base_url = “http://localhost:5151/v1”

def send_text_to_llm(cleaned_text):
# Define the system and user messages for the chat completion
messages = [
{“role”: “system”, “content”: “Always answer in rhymes.”},
{“role”: “user”, “content”: cleaned_text}
]

# Request chat completion from the local LLM API
completion = openai.ChatCompletion.create(
model=”local-model”, # this field is currently unused
messages=messages,
temperature=0.7,
)

# Access the generated text from the response
generated_text = completion.choices[0].message[‘content’]
tts_speak(generated_text)
“`

Make sure to adjust the model name (`”local-model”`) and other parameters as needed based on your setup.

If you prefer to continue using the `requests` library, please provide the specific structure of the response JSON from your local LLM API, so I can assist you in extracting the generated text correctly.