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import os, config, requests
import gradio as gr
import pandas as pd
import numpy as np
from openai.embeddings_utils import get_embedding, cosine_similarity
import openai
openai.api_key = config.OPENAI_API_KEY
messages = [{"role": "system", "content": 'You are a telecom advisor. Respond to all input in 50 words in dictionary format .'}]
# prepare Q&A embeddings dataframe
question_df = pd.read_csv('data/questions_with_embeddings.csv')
question_df['embedding'] = question_df['embedding'].apply(eval).apply(np.array)
def transcribe(audio):
global messages, question_df
# API now requires an extension so we will rename the file
audio_filename_with_extension = audio + '.wav'
os.rename(audio, audio_filename_with_extension)
audio_file = open(audio_filename_with_extension, "rb")
transcript = openai.Audio.transcribe("whisper-1", audio_file)
question_vector = get_embedding(transcript['text'], engine='text-embedding-ada-002')
question_df["similarities"] = question_df['embedding'].apply(lambda x: cosine_similarity(x, question_vector))
question_df = question_df.sort_values("similarities", ascending=False)
best_answer = question_df.iloc[0]['answer']
user_text = f"Using the following text, answer the question '{transcript['text']}'. {config.ADVISOR_CUSTOM_PROMPT}: {best_answer}"
messages.append({"role": "user", "content": user_text})
response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)
system_message = response["choices"][0]["message"]
print(system_message)
messages.append(system_message)
# text to speech request with eleven labs
url = f"https://api.elevenlabs.io/v1/text-to-speech/{config.ADVISOR_VOICE_ID}/stream"
data = {
"text": system_message["content"].replace('"', ''),
"voice_settings": {
"stability": 0.1,
"similarity_boost": 0.8
}
}
r = requests.post(url, headers={'xi-api-key': config.ELEVEN_LABS_API_KEY}, json=data)
output_filename = "reply.mp3"
with open(output_filename, "wb") as output:
output.write(r.content)
chat_transcript = ""
for message in messages:
if message['role'] != 'system':
chat_transcript += message['role'] + ": " + message['content'] + "\n\n"
# return chat_transcript
return chat_transcript, output_filename
# set a custom theme
theme = gr.themes.Default().set(
body_background_fill="#000000",
)
with gr.Blocks(theme=theme) as ui:
# advisor image input and microphone input
advisor = gr.Image(value=config.ADVISOR_IMAGE).style(width=config.ADVISOR_IMAGE_WIDTH, height=config.ADVISOR_IMAGE_HEIGHT)
audio_input = gr.Audio(source="microphone", type="filepath")
# text transcript output and audio
text_output = gr.Textbox(label="Conversation Transcript")
audio_output = gr.Audio()
btn = gr.Button("Run")
btn.click(fn=transcribe, inputs=audio_input, outputs=[text_output, audio_output])
ui.launch(debug=True, share=True) |