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 customer Support Representative Respond to all input in 50 words with bulleted point. Speak in the first person. Do not use the $ sign, write out dollar amounts with the full word dollars. Do not use quotation marks. Do not say you are an AI language model.'}] # 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)