waiting for the client review

.gitignore

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+.env
+env/
+__pycache__/

app.py

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+import os
+import gradio as gr
+from dotenv import load_dotenv
+import openai
+from utils import serialize
+from utils import compress
+
+from description import DESCRIPTION
+
+load_dotenv()
+
+# configuring openai package
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+openai.api_key = OPENAI_API_KEY
+
+
+def chat(message, history):
+    """
+    Sends a request to the OpenAi api based on the user input and the history
+    """
+    messages = serialize(history)
+    messages.append({"role": "user", "content": message})
+
+    completion = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=messages,
+    )
+
+    return completion["choices"][0]["message"]["content"].strip()
+
+
+def transcribe(audio_file):
+    audio_file = open(audio_file, "rb")
+    transcription = openai.Audio.transcribe("whisper-1", audio_file, language="en")
+    transcription = transcription["text"]
+    return transcription
+
+
+def predict(input, history=[]):
+    compress(input)
+    transcription = transcribe(input)
+
+    answer = chat(transcription, history)
+    history.append((transcription, answer))
+    response = history
+    return response, history
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+    chatbot = gr.Chatbot()
+    state = gr.State([])
+
+    with gr.Row():
+        audio_file = gr.Audio(label="Audio", source="microphone", type="filepath")
+
+    audio_file.change(predict, [audio_file, state], [chatbot, state])
+
+demo.launch()

description.py

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+DESCRIPTION = """
+
+# AssistGpt
+
+Introducing our cutting-edge web app that utilizes the advanced capabilities of GPT-based 
+chatbots to create an intelligent assistant like no other.
+
+"""

utils.py

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+import librosa
+from pathlib import Path, PurePath
+import soundfile as sf
+
+
+def serialize(messages):
+    """
+    Converts a list of tuples where each element of the list represents a message to dictionary of
+    messages
+    """
+
+    serialized_messages = []
+    for message in messages:
+        serialized_message = [
+            {"role": "system", "content": message[0]},
+            {"role": "user", "content": message[1]},
+        ]
+
+        serialized_messages.extend(serialized_message)
+
+    return serialized_messages
+
+
+def compress(audio_file):
+    y, s = librosa.load(audio_file, sr=8000)  # Downsample 44.1kHz to 8kHz
+    sf.write(audio_file, y, s, "PCM_24")
+    return audio_file