Having problems with this script:: import warnings
import queue
import keyboard
import sounddevice as sd
import numpy as np
import ffmpeg
from openai import OpenAI
import pyttsx3
import whisper

# Role message for the AI assistant
ai_role_message = “””
You are Dr. Tripper’s assistant, Sarah. You provide helpful, accurate, and concise responses. Your tasks include information synthesis, executing complex tasks, issue resolution, personal engagement, and continuous learning.
“””

# Initialize text-to-speech engine
tts_engine = pyttsx3.init(driverName=’sapi5′)

# Suppress the FP16 warning
warnings.filterwarnings(“ignore”, message=”FP16 is not supported on CPU; using FP32 instead”)

# Constants
SAMPLE_RATE = 16000 # Sample rate for recording
CHANNELS = 1 # Number of audio channels
CHUNK_SIZE = 100 # Number of characters per chunk

# Initialize OpenAI client
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”lm-studio”)

# Queue to hold recorded audio data
audio_queue = queue.Queue()

# Function to record audio
def record_audio():
def callback(indata, frames, time, status):
audio_queue.put(indata.copy())

with sd.InputStream(samplerate=SAMPLE_RATE, channels=CHANNELS, callback=callback):
print(“Recording… Press Left Ctrl to stop.”)
keyboard.wait(‘left ctrl’)
print(“Stopped recording.”)

# Function to convert recorded audio to a format suitable for Whisper
def convert_audio_to_wav():
audio_path = ‘temp_audio.wav’
wav_data = np.concatenate(list(audio_queue.queue))
process = (
ffmpeg
.input(‘pipe:0′, format=’f32le’, ac=CHANNELS, ar=SAMPLE_RATE)
.output(audio_path)
.overwrite_output()
.run_async(pipe_stdin=True)
)
process.communicate(input=wav_data.tobytes())
return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
model = whisper.load_model(“tiny.en”)
result = model.transcribe(audio_path)
return result[‘text’]

# Function to get completion from OpenAI
def get_completion(history):
completion = client.chat.completions.create(
model=”tinyllama”,
messages=history, # Pass the history list as the messages argument
temperature=0.7,
stream=True,

)

new_message = {“role”: “assistant”, “content”: “”}
response_text = “”

for chunk in completion:
if ‘delta’ in chunk[‘choices’][0]:
delta_content = chunk[‘choices’][0][‘delta’][‘content’]
response_text += delta_content
print(delta_content, end=””, flush=True)

print(“nResponse Text:”, response_text)
speak(response_text) # Call speak with the entire response text

new_message[“content”] = response_text
return new_message

# Function to speak text using TTS
def speak(text):
tts_engine.say(text)
tts_engine.runAndWait()

def main():
history = [
{“role”: “system”, “content”: ai_role_message},
]

while True:
# Record audio
record_audio()

# Convert recorded audio to WAV format
audio_path = convert_audio_to_wav()

# Transcribe audio using Whisper
transcription = transcribe_audio(audio_path)
print(f”Transcription: {transcription}”) # Log transcription

# Get completion from OpenAI
history.append({“role”: “user”, “content”: transcription})
new_message = get_completion(history)
history.append(new_message)

if __name__ == “__main__”:
main()
: File “D:EveBusinessVoiceTTSchtgpt.py”, line 78, in get_completion
if ‘delta’ in chunk[‘choices’][0]:
TypeError: ‘ChatCompletionChunk’ object is not subscriptable
Let’s break down and resolve the error in your script step-by-step. The error message indicates that `chunk[‘choices’][0]` is not subscriptable, meaning the `chunk` object doesn’t support indexing with `[]`. This usually happens when an object is not a dictionary but is being treated as one.

First, let’s review the relevant portion of the code where the error occurs:

“`python
def get_completion(history):
completion = client.chat.completions.create(
model=”tinyllama”,
messages=history, # Pass the history list as the messages argument
temperature=0.7,
stream=True,
)

new_message = {“role”: “assistant”, “content”: “”}
response_text = “”

for chunk in completion:
if ‘delta’ in chunk[‘choices’][0]:
delta_content = chunk[‘choices’][0][‘delta’][‘content’]
response_text += delta_content
print(delta_content, end=””, flush=True)

print(“nResponse Text:”, response_text)
speak(response_text) # Call speak with the entire response text

new_message[“content”] = response_text
return new_message
“`

To solve this, we need to ensure the `chunk` object is correctly handled. This involves understanding the structure of the response from the OpenAI API. Based on typical OpenAI API responses, the `chunk` in a streaming response should be a dictionary with a `choices` key.

Let’s proceed with the following steps:

1. **Check the structure of the `chunk` object**: We need to ensure we’re accessing the correct part of the response.
2. **Handle the chunk data appropriately**: Ensure the data is correctly extracted from the `chunk`.

Here is the fixed code:

“`python
import warnings
import queue
import keyboard
import sounddevice as sd
import numpy as np
import ffmpeg
from openai import OpenAI
import pyttsx3
import whisper

# Role message for the AI assistant
ai_role_message = “””
You are Dr. Tripper’s assistant, Sarah. You provide helpful, accurate, and concise responses. Your tasks include information synthesis, executing complex tasks, issue resolution, personal engagement, and continuous learning.
“””

# Initialize text-to-speech engine
tts_engine = pyttsx3.init(driverName=’sapi5′)

# Suppress the FP16 warning
warnings.filterwarnings(“ignore”, message=”FP16 is not supported on CPU; using FP32 instead”)

# Constants
SAMPLE_RATE = 16000 # Sample rate for recording
CHANNELS = 1 # Number of audio channels
CHUNK_SIZE = 100 # Number of characters per chunk

# Initialize OpenAI client
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”lm-studio”)

# Queue to hold recorded audio data
audio_queue = queue.Queue()

# Function to record audio
def record_audio():
def callback(indata, frames, time, status):
audio_queue.put(indata.copy())

with sd.InputStream(samplerate=SAMPLE_RATE, channels=CHANNELS, callback=callback):
print(“Recording… Press Left Ctrl to stop.”)
keyboard.wait(‘left ctrl’)
print(“Stopped recording.”)

# Function to convert recorded audio to a format suitable for Whisper
def convert_audio_to_wav():
audio_path = ‘temp_audio.wav’
wav_data = np.concatenate(list(audio_queue.queue))
process = (
ffmpeg
.input(‘pipe:0′, format=’f32le’, ac=CHANNELS, ar=SAMPLE_RATE)
.output(audio_path)
.overwrite_output()
.run_async(pipe_stdin=True)
)
process.communicate(input=wav_data.tobytes())
return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
model = whisper.load_model(“tiny.en”)
result = model.transcribe(audio_path)
return result[‘text’]

# Function to get completion from OpenAI
def get_completion(history):
completion = client.chat.completions.create(
model=”tinyllama”,
messages=history,
temperature=0.7,
stream=True,
)

new_message = {“role”: “assistant”, “content”: “”}
response_text = “”

for chunk in completion:
choices = chunk.get(‘choices’, [])
if choices and ‘delta’ in choices[0]:
delta_content = choices[0][‘delta’][‘content’]
response_text += delta_content
print(delta_content, end=””, flush=True)

print(“nResponse Text:”, response_text)
speak(response_text) # Call speak with the entire response text

new_message[“content”] = response_text
return new_message

# Function to speak text using TTS
def speak(text):
tts_engine.say(text)
tts_engine.runAndWait()

def main():
history = [
{“role”: “system”, “content”: ai_role_message},
]

while True:
# Record audio
record_audio()

# Convert recorded audio to WAV format
audio_path = convert_audio_to_wav()

# Transcribe audio using Whisper
transcription = transcribe_audio(audio_path)
print(f”Transcription: {transcription}”) # Log transcription

# Get completion from OpenAI
history.append({“role”: “user”, “content”: transcription})
new_message = get_completion(history)
history.append(new_message)

if __name__ == “__main__”:
main()
“`

### Key Changes:
– **Check if `choices` exist**: `choices = chunk.get(‘choices’, [])` ensures we don’t run into an error if `choices` key is missing.
– **Access `delta` correctly**: Added a check to see if `delta` exists in the first element of `choices`.

### Next Steps:
**a.** Run the script to verify it resolves the issue.
**b.** Add unit tests for individual functions to ensure reliability and maintainability.
ribute {item!r}’) from exc
AttributeError: ‘ChatCompletionChunk’ object has no attribute ‘get’

The `AttributeError: ‘ChatCompletionChunk’ object has no attribute ‘get’` suggests that the `chunk` object does not support the `get` method. This indicates that the `chunk` object might be an instance of a class rather than a dictionary. Let’s print out the `chunk` to understand its structure and correctly access its data.

Here are the steps to debug and fix this:

1. **Print the chunk structure**: Print the `chunk` to understand its structure.
2. **Correctly access chunk data**: Access the data based on the actual structure of the `chunk`.

Let’s implement these steps:

“`python
import warnings
import queue
import keyboard
import sounddevice as sd
import numpy as np
import ffmpeg
from openai import OpenAI
import pyttsx3
import whisper

# Role message for the AI assistant
ai_role_message = “””
You are Dr. Tripper’s assistant, Sarah. You provide helpful, accurate, and concise responses. Your tasks include information synthesis, executing complex tasks, issue resolution, personal engagement, and continuous learning.
“””

# Initialize text-to-speech engine
tts_engine = pyttsx3.init(driverName=’sapi5’)

# Suppress the FP16 warning
warnings.filterwarnings(“ignore”, message=”FP16 is not supported on CPU; using FP32 instead”)

# Constants
SAMPLE_RATE = 16000 # Sample rate for recording
CHANNELS = 1 # Number of audio channels
CHUNK_SIZE = 100 # Number of characters per chunk

# Initialize OpenAI client
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”lm-studio”)

# Queue to hold recorded audio data
audio_queue = queue.Queue()

# Function to record audio
def record_audio():
def callback(indata, frames, time, status):
audio_queue.put(indata.copy())

with sd.InputStream(samplerate=SAMPLE_RATE, channels=CHANNELS, callback=callback):
print(“Recording… Press Left Ctrl to stop.”)
keyboard.wait(‘left ctrl’)
print(“Stopped recording.”)

# Function to convert recorded audio to a format suitable for Whisper
def convert_audio_to_wav():
audio_path = ‘temp_audio.wav’
wav_data = np.concatenate(list(audio_queue.queue))
process = (
ffmpeg
.input(‘pipe:0′, format=’f32le’, ac=CHANNELS, ar=SAMPLE_RATE)
.output(audio_path)
.overwrite_output()
.run_async(pipe_stdin=True)
)
process.communicate(input=wav_data.tobytes())
return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
model = whisper.load_model(“tiny.en”)
result = model.transcribe(audio_path)
return result[‘text’]

# Function to get completion from OpenAI
def get_completion(history):
completion = client.chat.completions.create(
model=”tinyllama”,
messages=history,
temperature=0.7,
stream=True,
)

new_message = {“role”: “assistant”, “content”: “”}
response_text = “”

for chunk in completion:
print(chunk) # Print the chunk to understand its structure
# Access the choices based on the actual structure of the chunk
if hasattr(chunk, ‘choices’) and chunk.choices and hasattr(chunk.choices[0], ‘delta’):
delta_content = chunk.choices[0].delta.get(‘content’, ”)
response_text += delta_content
print(delta_content, end=””, flush=True)

print(“nResponse Text:”, response_text)
speak(response_text) # Call speak with the entire response text

new_message[“content”] = response_text
return new_message

# Function to speak text using TTS
def speak(text):
tts_engine.say(text)
tts_engine.runAndWait()

def main():
history = [
{“role”: “system”, “content”: ai_role_message},
]

while True:
# Record audio
record_audio()

# Convert recorded audio to WAV format
audio_path = convert_audio_to_wav()

# Transcribe audio using Whisper
transcription = transcribe_audio(audio_path)
print(f”Transcription: {transcription}”) # Log transcription

# Get completion from OpenAI
history.append({“role”: “user”, “content”: transcription})
new_message = get_completion(history)
history.append(new_message)

if __name__ == “__main__”:
main()
“`

### Key Changes:
– **Print the `chunk` object**: Added `print(chunk)` to inspect the structure of the `chunk` object.
– **Access attributes using `hasattr`**: Safely accessed attributes using `hasattr` to avoid errors.

### Next Steps:
**a.** Run the script and check the printed output to confirm the structure of the `chunk`.
**b.** Based on the output, refine the code to correctly handle the `chunk` object and access the necessary data.
#Groq rewrite of mostly working code
import warnings
import queue
import keyboard
import sounddevice as sd
import numpy as np
import ffmpeg
from openai import OpenAI
import pyttsx3
import whisper
from langchain_groq import ChatGroq

# Role message for the AI assistant
ai_role_message = “””
You are Dr. Tripper’s assistant, Sarah. You provide helpful, accurate, and concise responses. Your tasks include information synthesis, executing complex tasks, issue resolution, personal engagement, and continuous learning.
“””

# Initialize text-to-speech engine
tts_engine = pyttsx3.init(driverName=’sapi5′)

# Suppress the FP16 warning
warnings.filterwarnings(“ignore”, message=”FP16 is not supported on CPU; using FP32 instead”)

# Constants
SAMPLE_RATE = 16000 # Sample rate for recording
CHANNELS = 1 # Number of audio channels
CHUNK_SIZE = 100 # Number of characters per chunk

# Initialize OpenAI client
#client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”lm-studio”)
client = ChatGroq(
temperature=0,
groq_api_key=”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”,
model_name=’llama3-8b-8192′
)

# Queue to hold recorded audio data
audio_queue = queue.Queue()

# Function to record audio
def record_audio():
def callback(indata, frames, time, status):
audio_queue.put(indata.copy())

with sd.InputStream(samplerate=SAMPLE_RATE, channels=CHANNELS, callback=callback):
print(“Recording… Press Left Ctrl to stop.”)
keyboard.wait(‘left ctrl’)
print(“Stopped recording.”)

# Function to convert recorded audio to a format suitable for Whisper
def convert_audio_to_wav():
audio_path = ‘temp_audio.wav’
wav_data = np.concatenate(list(audio_queue.queue))
ffmpeg.input(‘pipe:0′, format=’f32le’, ac=CHANNELS, ar=SAMPLE_RATE).output(audio_path).run(input=wav_data.tobytes(), overwrite_output=True)
return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
model = whisper.load_model(“tiny.en”)
result = model.transcribe(audio_path)
return result[‘text’]

# Function to get completion from OpenAI
def get_completion(history):
completion = client.on_text(input=history, temperature=0.7)
return completion.text

new_message = {“role”: “assistant”, “content”: “”}
response_text = “Default”
for chunk in completion:
if ‘delta’ in chunk.choices[0]:
response_text += chunk.choices[0].delta.content
print(chunk.choices[0].delta.content, end=””, flush=True)
print(“Response Text:”, response_text)
speak(response_text) # Call speak with the entire response text

new_message[“content”] = response_text
return new_message

# Function to speak the response text
def speak(text):
tts_engine = pyttsx3.init(driverName=’sapi5′)
tts_engine.say(text)
tts_engine.runAndWait()

# Function to print the response text
def print_response_text(response_text):
print(“Response Text:”, response_text)

# Main function
def main():
history = [
{“role”: “system”, “content”: ai_role_message},
]

while True:
# Record audio
record_audio()

# Convert recorded audio to WAV format
audio_path = convert_audio_to_wav()

# Transcribe audio using Whisper
transcription = transcribe_audio(audio_path)
print(f”Transcription: {transcription}”) # Log transcription

# Get completion from OpenAI
history.append({“role”: “user”, “content”: transcription})
new_message = get_completion(history)
history.append(new_message)

# Print the response text
print_response_text(new_message[“content”])

if __name__ == “__main__”:
main()
Let’s refine the code by addressing some issues and ensuring that it correctly integrates with the `ChatGroq` client. The `get_completion` function should be revised to handle the response from `ChatGroq` properly. We’ll also make sure the code is structured and clear.

Here is the revised code:

“`python
import warnings
import queue
import keyboard
import sounddevice as sd
import numpy as np
import ffmpeg
import pyttsx3
import whisper
from langchain_groq import ChatGroq

# Role message for the AI assistant
ai_role_message = “””
You are Dr. Tripper’s assistant, Sarah. You provide helpful, accurate, and concise responses. Your tasks include information synthesis, executing complex tasks, issue resolution, personal engagement, and continuous learning.
“””

# Initialize text-to-speech engine
tts_engine = pyttsx3.init(driverName=’sapi5′)

# Suppress the FP16 warning
warnings.filterwarnings(“ignore”, message=”FP16 is not supported on CPU; using FP32 instead”)

# Constants
SAMPLE_RATE = 16000 # Sample rate for recording
CHANNELS = 1 # Number of audio channels
CHUNK_SIZE = 100 # Number of characters per chunk

# Initialize ChatGroq client
client = ChatGroq(
temperature=0,
groq_api_key=”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”,
model_name=’llama3-8b-8192′
)

# Queue to hold recorded audio data
audio_queue = queue.Queue()

# Function to record audio
def record_audio():
def callback(indata, frames, time, status):
audio_queue.put(indata.copy())

with sd.InputStream(samplerate=SAMPLE_RATE, channels=CHANNELS, callback=callback):
print(“Recording… Press Left Ctrl to stop.”)
keyboard.wait(‘left ctrl’)
print(“Stopped recording.”)

# Function to convert recorded audio to a format suitable for Whisper
def convert_audio_to_wav():
audio_path = ‘temp_audio.wav’
wav_data = np.concatenate(list(audio_queue.queue))
ffmpeg.input(‘pipe:0′, format=’f32le’, ac=CHANNELS, ar=SAMPLE_RATE).output(audio_path).run(input=wav_data.tobytes(), overwrite_output=True)
return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
model = whisper.load_model(“tiny.en”)
result = model.transcribe(audio_path)
return result[‘text’]

# Function to get completion from ChatGroq
def get_completion(history):
response = client.on_text(input=history, temperature=0.7)
return response.text

# Function to speak the response text
def speak(text):
tts_engine.say(text)
tts_engine.runAndWait()

# Function to print the response text
def print_response_text(response_text):
print(“Response Text:”, response_text)

# Main function
def main():
history = [
{“role”: “system”, “content”: ai_role_message},
]

while True:
# Record audio
record_audio()

# Convert recorded audio to WAV format
audio_path = convert_audio_to_wav()

# Transcribe audio using Whisper
transcription = transcribe_audio(audio_path)
print(f”Transcription: {transcription}”) # Log transcription

# Get completion from ChatGroq
history.append({“role”: “user”, “content”: transcription})
new_message = {“role”: “assistant”, “content”: get_completion(history)}
history.append(new_message)

# Print the response text
print_response_text(new_message[“content”])

# Speak the response text
speak(new_message[“content”])

if __name__ == “__main__”:
main()
“`

### Key Changes:
1. **Removed Unused Imports and Functions**: Removed the unused import for `OpenAI` and the unnecessary commented-out code.
2. **Simplified `get_completion`**: Streamlined the `get_completion` function to correctly handle the response from `ChatGroq`.
3. **Refactored `speak` Function**: Ensured the `tts_engine` is used consistently within the `speak` function.
4. **Updated `main` Logic**: Simplified the main loop and ensured the new message is correctly handled and spoken.

### Next Steps:
**a.** Run the script to verify it functions correctly with the `ChatGroq` client.
**b.** Add error handling to ensure robustness (e.g., handling potential API errors, audio recording issues, etc.).
AttributeError: ‘ChatGroq’ object has no attribute ‘on_text
The error `AttributeError: ‘ChatGroq’ object has no attribute ‘on_text’` suggests that the method to interact with the `ChatGroq` object is not `on_text`. To resolve this, we need to refer to the correct method that the `ChatGroq` class provides for generating text completions. Since this method likely functions similarly to the OpenAI `completions.create` method, we’ll adjust accordingly.

Assuming the `ChatGroq` client has a similar interface to the OpenAI API, here is the corrected approach:

“`python
import warnings
import queue
import keyboard
import sounddevice as sd
import numpy as np
import ffmpeg
import pyttsx3
import whisper
from langchain_groq import ChatGroq

# Role message for the AI assistant
ai_role_message = “””
You are Dr. Tripper’s assistant, Sarah. You provide helpful, accurate, and concise responses. Your tasks include information synthesis, executing complex tasks, issue resolution, personal engagement, and continuous learning.
“””

# Initialize text-to-speech engine
tts_engine = pyttsx3.init(driverName=’sapi5′)

# Suppress the FP16 warning
warnings.filterwarnings(“ignore”, message=”FP16 is not supported on CPU; using FP32 instead”)

# Constants
SAMPLE_RATE = 16000 # Sample rate for recording
CHANNELS = 1 # Number of audio channels
CHUNK_SIZE = 100 # Number of characters per chunk

# Initialize ChatGroq client
client = ChatGroq(
temperature=0,
groq_api_key=”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”,
model_name=’llama3-8b-8192’
)

# Queue to hold recorded audio data
audio_queue = queue.Queue()

# Function to record audio
def record_audio():
def callback(indata, frames, time, status):
audio_queue.put(indata.copy())

with sd.InputStream(samplerate=SAMPLE_RATE, channels=CHANNELS, callback=callback):
print(“Recording… Press Left Ctrl to stop.”)
keyboard.wait(‘left ctrl’)
print(“Stopped recording.”)

# Function to convert recorded audio to a format suitable for Whisper
def convert_audio_to_wav():
audio_path = ‘temp_audio.wav’
wav_data = np.concatenate(list(audio_queue.queue))
ffmpeg.input(‘pipe:0′, format=’f32le’, ac=CHANNELS, ar=SAMPLE_RATE).output(audio_path).run(input=wav_data.tobytes(), overwrite_output=True)
return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
model = whisper.load_model(“tiny.en”)
result = model.transcribe(audio_path)
return result[‘text’]

# Function to get completion from ChatGroq
def get_completion(history):
completion = client.completion(
model_name=’llama3-8b-8192′,
messages=history,
temperature=0.7,
)
return completion[‘text’]

# Function to speak the response text
def speak(text):
tts_engine.say(text)
tts_engine.runAndWait()

# Function to print the response text
def print_response_text(response_text):
print(“Response Text:”, response_text)

# Main function
def main():
history = [
{“role”: “system”, “content”: ai_role_message},
]

while True:
# Record audio
record_audio()

# Convert recorded audio to WAV format
audio_path = convert_audio_to_wav()

# Transcribe audio using Whisper
transcription = transcribe_audio(audio_path)
print(f”Transcription: {transcription}”) # Log transcription

# Get completion from ChatGroq
history.append({“role”: “user”, “content”: transcription})
new_message = {“role”: “assistant”, “content”: get_completion(history)}
history.append(new_message)

# Print the response text
print_response_text(new_message[“content”])

# Speak the response text
speak(new_message[“content”])

if __name__ == “__main__”:
main()
“`

### Key Adjustments:
1. **Updated method call for ChatGroq**: Changed `on_text` to `completion` based on the likely method structure for generating completions.
2. **Ensured correct parameter passing**: Passed `model_name`, `messages`, and `temperature` to the `completion` method to align with typical API usage.

### Next Steps:
**a.** Run the script to verify it interacts correctly with the `ChatGroq` client.
**b.** Add error handling to ensure robustness and manage any potential API errors or issues during the recording and transcription process.
this is the code example for chatgroq::import { ChatGroq } from “@langchain/groq”;
import { ChatPromptTemplate } from “@langchain/core/prompts”;

const model = new ChatGroq({
apiKey: process.env.GROQ_API_KEY,
});
const prompt = ChatPromptTemplate.fromMessages([
[“system”, “You are a helpful assistant”],
[“human”, “{input}”],
]);
const chain = prompt.pipe(model);
const response = await chain.invoke({
input: “Hello”,
});
console.log(“response”, response);
/**
response AIMessage {
content: “Hello! I’m happy to assist you in any way I can. Is there something specific you need help with or a question you have?”,
}
*/
Based on the provided example for `ChatGroq`, it seems that the interaction involves creating a prompt template and invoking it with specific input. We’ll refactor the Python script to align with this approach.

Here’s the revised code incorporating the `ChatGroq` interaction correctly:

“`python
import warnings
import queue
import keyboard
import sounddevice as sd
import numpy as np
import ffmpeg
import pyttsx3
import whisper
from langchain_groq import ChatGroq, ChatPromptTemplate

# Role message for the AI assistant
ai_role_message = “””
You are Dr. Tripper’s assistant, Sarah. You provide helpful, accurate, and concise responses. Your tasks include information synthesis, executing complex tasks, issue resolution, personal engagement, and continuous learning.
“””

# Initialize text-to-speech engine
tts_engine = pyttsx3.init(driverName=’sapi5′)

# Suppress the FP16 warning
warnings.filterwarnings(“ignore”, message=”FP16 is not supported on CPU; using FP32 instead”)

# Constants
SAMPLE_RATE = 16000 # Sample rate for recording
CHANNELS = 1 # Number of audio channels
CHUNK_SIZE = 100 # Number of characters per chunk

# Initialize ChatGroq client
client = ChatGroq(
groq_api_key=”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”
)

# Queue to hold recorded audio data
audio_queue = queue.Queue()

# Function to record audio
def record_audio():
def callback(indata, frames, time, status):
audio_queue.put(indata.copy())

with sd.InputStream(samplerate=SAMPLE_RATE, channels=CHANNELS, callback=callback):
print(“Recording… Press Left Ctrl to stop.”)
keyboard.wait(‘left ctrl’)
print(“Stopped recording.”)

# Function to convert recorded audio to a format suitable for Whisper
def convert_audio_to_wav():
audio_path = ‘temp_audio.wav’
wav_data = np.concatenate(list(audio_queue.queue))
ffmpeg.input(‘pipe:0′, format=’f32le’, ac=CHANNELS, ar=SAMPLE_RATE).output(audio_path).run(input=wav_data.tobytes(), overwrite_output=True)
return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
model = whisper.load_model(“tiny.en”)
result = model.transcribe(audio_path)
return result[‘text’]

# Function to get completion from ChatGroq
def get_completion(history):
prompt = ChatPromptTemplate.from_messages(history)
chain = prompt.pipe(client)
response = chain.invoke({})
return response[‘content’]

# Function to speak the response text
def speak(text):
tts_engine.say(text)
tts_engine.runAndWait()

# Function to print the response text
def print_response_text(response_text):
print(“Response Text:”, response_text)

# Main function
def main():
history = [
[“system”, ai_role_message],
]

while True:
# Record audio
record_audio()

# Convert recorded audio to WAV format
audio_path = convert_audio_to_wav()

# Transcribe audio using Whisper
transcription = transcribe_audio(audio_path)
print(f”Transcription: {transcription}”) # Log transcription

# Get completion from ChatGroq
history.append([“human”, transcription])
response_content = get_completion(history)
new_message = [“assistant”, response_content]
history.append(new_message)

# Print the response text
print_response_text(response_content)

# Speak the response text
speak(response_content)

if __name__ == “__main__”:
main()
“`

### Key Adjustments:
1. **Using `ChatPromptTemplate`**: Created a prompt template using `ChatPromptTemplate.from_messages`.
2. **Piping the prompt to the model**: Piped the prompt to the `ChatGroq` model and invoked it.
3. **Simplified `get_completion`**: Simplified the function to use the chain approach demonstrated in the provided example.

### Next Steps:
**a.** Run the script to verify it functions correctly with the `ChatGroq` client.
**b.** Add error handling to ensure robustness and manage potential API errors or issues during the recording and transcription process.
want to try a crack at it? import warnings
import queue
import keyboard
import sounddevice as sd
import numpy as np
import ffmpeg
import pyttsx3
import whisper
from langchain_groq import ChatGroq

# Role message for the AI assistant
ai_role_message = “””
You are Dr. Tripper’s assistant, Sarah. You provide helpful, accurate, and concise responses. Your tasks include information synthesis, executing complex tasks, issue resolution, personal engagement, and continuous learning.
“””

# Initialize text-to-speech engine
tts_engine = pyttsx3.init(driverName=’sapi5′)

# Suppress the FP16 warning
warnings.filterwarnings(“ignore”, message=”FP16 is not supported on CPU; using FP32 instead”)

# Constants
SAMPLE_RATE = 16000 # Sample rate for recording
CHANNELS = 1 # Number of audio channels
CHUNK_SIZE = 100 # Number of characters per chunk

# Initialize ChatGroq client
client = ChatGroq(
groq_api_key=”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”
)

# Queue to hold recorded audio data
audio_queue = queue.Queue()

# Function to record audio
def record_audio():
def callback(indata, frames, time, status):
audio_queue.put(indata.copy())

with sd.InputStream(samplerate=SAMPLE_RATE, channels=CHANNELS, callback=callback):
print(“Recording… Press Left Ctrl to stop.”)
keyboard.wait(‘left ctrl’)
print(“Stopped recording.”)

# Function to convert recorded audio to a format suitable for Whisper
def convert_audio_to_wav():
audio_path = ‘temp_audio.wav’
wav_data = np.concatenate(list(audio_queue.queue))
ffmpeg.input(‘pipe:0′, format=’f32le’, ac=CHANNELS, ar=SAMPLE_RATE).output(audio_path).run(input=wav_data.tobytes(), overwrite_output=True)
return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
model = whisper.load_model(“tiny.en”)
result = model.transcribe(audio_path)
return result[‘text’]

# Function to get completion from ChatGroq
def get_completion(history):
prompt = text(history)
chain = prompt.pipe(client)
response = chain.invoke({})
return response[‘content’]

# Function to speak the response text
def speak(text):
tts_engine.say(text)
tts_engine.runAndWait()

# Function to print the response text
def print_response_text(response_text):
print(“Response Text:”, response_text)

# Main function
def main():
history = [
[“system”, ai_role_message],
]

while True:
# Record audio
record_audio()

# Convert recorded audio to WAV format
audio_path = convert_audio_to_wav()

# Transcribe audio using Whisper
transcription = transcribe_audio(audio_path)
print(f”Transcription: {transcription}”) # Log transcription

# Get completion from ChatGroq
history.append([“human”, transcription])
response_content = get_completion(history)
new_message = [“assistant”, response_content]
history.append(new_message)

# Print the response text
print_response_text(response_content)

# Speak the response text
speak(response_content)

if __name__ == “__main__”:
main()

Let’s correct the code to properly interface with the `ChatGroq` and `ChatPromptTemplate` as per the example provided. Specifically, we need to adjust the `get_completion` function to properly create the prompt and use the `ChatGroq` client.

Here is the revised code:

“`python
import warnings
import queue
import keyboard
import sounddevice as sd
import numpy as np
import ffmpeg
import pyttsx3
import whisper
from langchain_groq import ChatGroq, ChatPromptTemplate

# Role message for the AI assistant
ai_role_message = “””
You are Dr. Tripper’s assistant, Sarah. You provide helpful, accurate, and concise responses. Your tasks include information synthesis, executing complex tasks, issue resolution, personal engagement, and continuous learning.
“””

# Initialize text-to-speech engine
tts_engine = pyttsx3.init(driverName=’sapi5′)

# Suppress the FP16 warning
warnings.filterwarnings(“ignore”, message=”FP16 is not supported on CPU; using FP32 instead”)

# Constants
SAMPLE_RATE = 16000 # Sample rate for recording
CHANNELS = 1 # Number of audio channels
CHUNK_SIZE = 100 # Number of characters per chunk

# Initialize ChatGroq client
client = ChatGroq(
apiKey=”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”
)

# Queue to hold recorded audio data
audio_queue = queue.Queue()

# Function to record audio
def record_audio():
def callback(indata, frames, time, status):
audio_queue.put(indata.copy())

with sd.InputStream(samplerate=SAMPLE_RATE, channels=CHANNELS, callback=callback):
print(“Recording… Press Left Ctrl to stop.”)
keyboard.wait(‘left ctrl’)
print(“Stopped recording.”)

# Function to convert recorded audio to a format suitable for Whisper
def convert_audio_to_wav():
audio_path = ‘temp_audio.wav’
wav_data = np.concatenate(list(audio_queue.queue))
ffmpeg.input(‘pipe:0′, format=’f32le’, ac=CHANNELS, ar=SAMPLE_RATE).output(audio_path).run(input=wav_data.tobytes(), overwrite_output=True)
return audio_path

# Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
model = whisper.load_model(“tiny.en”)
result = model.transcribe(audio_path)
return result[‘text’]

# Function to get completion from ChatGroq
def get_completion(history):
prompt = ChatPromptTemplate.from_messages(history)
chain = prompt.pipe(client)
response = chain.invoke({})
return response[‘content’]

# Function to speak the response text
def speak(text):
tts_engine.say(text)
tts_engine.runAndWait()

# Function to print the response text
def print_response_text(response_text):
print(“Response Text:”, response_text)

# Main function
def main():
history = [
[“system”, ai_role_message],
]

while True:
# Record audio
record_audio()

# Convert recorded audio to WAV format
audio_path = convert_audio_to_wav()

# Transcribe audio using Whisper
transcription = transcribe_audio(audio_path)
print(f”Transcription: {transcription}”) # Log transcription

# Get completion from ChatGroq
history.append([“human”, transcription])
response_content = get_completion(history)
new_message = [“assistant”, response_content]
history.append(new_message)

# Print the response text
print_response_text(response_content)

# Speak the response text
speak(response_content)

if __name__ == “__main__”:
main()
“`

### Key Adjustments:
1. **Corrected Client Initialization**: Ensured `ChatGroq` is initialized with the correct `apiKey`.
2. **Updated `get_completion` Function**: Corrected the creation of the prompt and chaining with `ChatGroq` to invoke the response.
3. **Consistent Naming and Handling**: Ensured all interactions with `ChatGroq` and prompt templates follow the provided example.

### Next Steps:
**a.** Run the script to verify it functions correctly with the `ChatGroq` client.
**b.** Add error handling to ensure robustness and manage potential API errors or issues during the recording and transcription process.