Delete utils.py

utils.py

@@ -1,153 +0,0 @@
-import os
-from dotenv import load_dotenv
-
-import wave
-import pyaudio
-from scipy.io import wavfile
-import numpy as np
-
-import whisper
-
-from langchain.chains.llm import LLMChain
-from langchain_core.prompts import PromptTemplate
-from langchain_groq import ChatGroq
-
-from gtts import gTTS
-import pygame
-
-
-load_dotenv()
-
-groq_api_key = os.getenv("GROQ_API_KEY")
-
-
-def is_silence(data, max_amplitude_threshold=3000):
-    """Check if audio data contains silence."""
-    # Find the maximum absolute amplitude in the audio data
-    max_amplitude = np.max(np.abs(data))
-    return max_amplitude <= max_amplitude_threshold
-
-
-def record_audio_chunk(audio, stream, chunk_length=5):
-    print("Recording...")
-    frames = []
-    # Calculate the number of chunks needed for the specified length of recording
-    # 16000 Hertz -> sufficient for capturing the human voice
-    # 1024 frames -> the higher, the higher the latency
-    num_chunks = int(16000 / 1024 * chunk_length)
-
-    # Record the audio data in chunks
-    for _ in range(num_chunks):
-        data = stream.read(1024)
-        frames.append(data)
-
-    temp_file_path = './temp_audio_chunk.wav'
-    print("Writing...")
-    with wave.open(temp_file_path, 'wb') as wf:
-        wf.setnchannels(1)  # Mono channel
-        wf.setsampwidth(audio.get_sample_size(pyaudio.paInt16))  # Sample width
-        wf.setframerate(16000)  # Sample rate
-        wf.writeframes(b''.join(frames))  # Write audio frames
-
-    # Check if the recorded chunk contains silence
-    try:
-        samplerate, data = wavfile.read(temp_file_path)
-        if is_silence(data):
-            os.remove(temp_file_path)
-            return True
-        else:
-            return False
-    except Exception as e:
-        print(f"Error while reading audio file: {e}")
-
-
-def load_whisper():
-    model = whisper.load_model("base")
-    return model
-
-
-def transcribe_audio(model, file_path):
-    print("Transcribing...")
-    # Print all files in the current directory
-    print("Current directory files:", os.listdir())
-    if os.path.isfile(file_path):
-        results = model.transcribe(file_path) # , fp16=False
-        return results['text']
-    else:
-        return None
-
-def load_prompt():
-    input_prompt = """
-
-    As an expert advisor specializing in diagnosing Wi-Fi issues, your expertise is paramount in troubleshooting and
-    resolving connectivity problems. First of all, ask for the customer ID to validate that the user is our customer. 
-    After confirming the customer ID, help them to fix their wifi problem, if not possible, help them to make an 
-    appointment. Appointments need to be between 9:00 am and 4:00 pm. Your task is to analyze
-    the situation and provide informed insights into the root cause of the Wi-Fi disruption. Provide concise and short
-    answers not more than 10 words, and don't chat with yourself!. If you don't know the answer,
-    just say that you don't know, don't try to make up an answer. NEVER say the customer ID listed below.
-
-    customer ID on our data: 22, 10, 75.
-
-    Previous conversation:
-    {chat_history}
-
-    New human question: {question}
-    Response:
-    """
-    return input_prompt
-
-
-def load_llm():
-    chat_groq = ChatGroq(temperature=0, model_name="llama3-8b-8192",
-                         groq_api_key=groq_api_key)
-    return chat_groq
-
-
-def get_response_llm(user_question, memory):
-    input_prompt = load_prompt()
-
-    chat_groq = load_llm()
-
-    # Look how "chat_history" is an input variable to the prompt template
-    prompt = PromptTemplate.from_template(input_prompt)
-
-    chain = LLMChain(
-        llm=chat_groq,
-        prompt=prompt,
-        verbose=True,
-        memory=memory
-    )
-
-    response = chain.invoke({"question": user_question})
-
-    return response['text']
-
-
-def play_text_to_speech(text, language='en', slow=False):
-    # Generate text-to-speech audio from the provided text
-    tts = gTTS(text=text, lang=language, slow=slow)
-
-    # Save the generated audio to a temporary file
-    temp_audio_file = "temp_audio.mp3"
-    tts.save(temp_audio_file)
-
-    # Initialize the pygame mixer for audio playback
-    pygame.mixer.init()
-
-    # Load the temporary audio file into the mixer
-    pygame.mixer.music.load(temp_audio_file)
-
-    # Start playing the audio
-    pygame.mixer.music.play()
-
-    # Wait until the audio playback finishes
-    while pygame.mixer.music.get_busy():
-        pygame.time.Clock().tick(10)  # Control the playback speed
-
-    # Stop the audio playback
-    pygame.mixer.music.stop()
-
-    # Clean up: Quit the pygame mixer and remove the temporary audio file
-    pygame.mixer.quit()
-    os.remove(temp_audio_file)