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import os
import gradio as gr
import openai
from gtts import gTTS
openai.api_key = os.environ["OPEN_AI_KEY"]
def transcribe(audio):
audio_file = open(audio, "rb")
# Call the transcribe method with the file-like object
transcript = openai.Audio.transcribe("whisper-1", audio_file)
return transcript["text"]
with gr.Blocks() as demo:
messages = gr.State(value=[{"role": "system", "content": "You are a therapist. Respond in less than 5 sentences."}])
def botResponse(user_input, messages):
messages.append({"role": "user", "content": user_input})
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo-0301",
messages=messages
)
system_message = response["choices"][0]["message"]["content"]
messages.append({"role": "assistant", "content": system_message})
chat_transcript = ""
for message in messages:
if (message["role"] != "system"):
chat_transcript += message["role"] + ": " + message["content"] + "\n\n"
return chat_transcript
def giveVoice(messages):
bot_message=messages[-1]
myobj = gTTS(text=bot_message["content"])
myobj.save("temp.mp3")
dir = os.getcwd()
new_path = os.path.join(dir, "temp.mp3")
return new_path
with gr.Row():
with gr.Column(scale=1):
user_audio = gr.Audio(source="microphone", type="filepath", label="Input Phrase")
submit_btn = gr.Button(value="Transcribe")
gpt_voice = gr.Audio(label="Voice Response")
with gr.Column(scale=2):
user_transcript = gr.Text(label="User Transcript")
gpt_transcript = gr.Text(label="Chat Transcript")
submit_btn.click(transcribe, user_audio, user_transcript)
user_transcript.change(botResponse, [user_transcript, messages], gpt_transcript)
gpt_transcript.change(giveVoice, messages, gpt_voice)
demo.launch(share=False) |