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import whisper |
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import torch |
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import streamlit as st |
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from st_audiorec import st_audiorec |
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from langchain.prompts.prompt import PromptTemplate |
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from langchain_openai import OpenAI |
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from langchain.chains import ConversationChain |
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from langchain.memory import ConversationBufferMemory |
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import os |
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from dotenv import load_dotenv |
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from pathlib import Path |
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import time |
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from pygame import mixer |
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from openai import OpenAI as op |
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import getans |
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load_dotenv(Path(".env")) |
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def audio_to_text(): |
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model = whisper.load_model("base") |
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wav_audio_data = st_audiorec() |
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if wav_audio_data is not None: |
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st.audio(wav_audio_data, format='audio/wav') |
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with open("/tmp/audio.wav", "wb") as f: |
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f.write(wav_audio_data) |
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data = whisper.load_audio("/tmp/audio.wav") |
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result = model.transcribe(data) |
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query = result["text"] |
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return query |
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def querygen(query): |
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reply=getans.answer(input=query) |
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return reply |
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def text_to_speech(reply): |
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speech=op() |
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speech_file_path = "/tmp/speech.mp3" |
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response = speech.audio.speech.create( |
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model="tts-1", |
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voice="alloy", |
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input=reply |
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) |
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response.stream_to_file(speech_file_path) |
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return speech_file_path |
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def play_audio(file_path): |
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mixer.init() |
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mixer.music.load(file_path) |
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mixer.music.play() |
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while mixer.music.get_busy(): |
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time.sleep(1) |
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mixer.quit() |
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if __name__ == "__main__": |
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st.title("GPT Based Psychotherapist") |
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query=audio_to_text() |
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st.write(query) |
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if st.button("Generate Response"): |
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reply=querygen(query) |
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st.write(reply) |
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file_path=text_to_speech(reply) |
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play_audio(file_path) |
