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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 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) |