app.py

import streamlit as st
from gradio_client import Client
from st_audiorec import st_audiorec
from gtts import gTTS
import os

# Constants
TITLE = "𝗙𝗹𝗮𝘄𝗹𝗲𝘀𝘀𝗔𝗜"
DESCRIPTION = """"""

# Sidebar
with st.sidebar:
    temperatureSide = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
    max_new_tokensSide = st.slider("Max Tokens", min_value=0.0, max_value=4096.0, value=512.0, step=64.0)
    ToppSide = st.slider("Top-P (Nucleus Sampling", min_value=0.0, max_value=1.0, value=0.6, step=0.05)
    RepetitionpenaltySide = st.slider("Repetetion Penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)

# Initialize client
whisper_client = Client("https://sanchit-gandhi-whisper-large-v2.hf.space/")

# Function to convert text to speech using gTTS
def text_to_speech(text, lang='en'):
    tts = gTTS(text=text, lang=lang, slow=False)
    tts.save("response.mp3")
    return "response.mp3"

# Function to transcribe audio
def transcribe(wav_path):
    return whisper_client.predict(
        wav_path,
        "transcribe",
        api_name="/predict"
    )

# Prediction function
def predict(message, system_prompt='Your name is FlawlessAI. You are developed by Achyuth. Your developer is a 14-year old boy, who is intrested on AI and Machine Learning. Dont talk about your developers age, until the user asks for your developers age. You need to give the user a long and big text as output with useful information. When the user asks you for information, never exclaim the words, and directly come to point and answer to the user. You need to provide short text-answers, meaningful answers and directly to the point. Never miscalculate the calculations. You need to check and verify the answer and then provide to user, the correct answer. You need to provide accurate and correct information. If the user conveys with you in any language, you also should convey in the same language', temperature=0.7, max_new_tokens=2048, Topp=0.5, Repetitionpenalty=1.2):
    with st.status("sᴛᴀʀᴛɪɴɢ ᴄʟɪᴇɴᴛ..."):
        client = Client("https://huggingface-projects-llama-2-13b-chat.hf.space/")
        st.write("Requesting audio transcriber...")
    with st.status("Requesting Flawless-LLM..."):
        st.write("Requesting API...")
        response = client.predict(
            message,
            system_prompt,
            max_new_tokens,
            temperature,
            Topp,
            500,
            Repetitionpenalty,
            api_name="/chat"
        )
        st.write("Success ✅")
        return response

# Streamlit UI
st.title(TITLE)
st.write(DESCRIPTION)

if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"], avatar=("🧑‍💻" if message["role"] == 'human' else '🦙')):
        st.markdown(message["content"])

textinput = st.chat_input("Ask FlawlessAI anything...")
wav_audio_data = st_audiorec()

if wav_audio_data is not None:
    with st.status("Transcribing audio..."):
        # save audio
        with open("audio.wav", "wb") as f:
            f.write(wav_audio_data)
        prompt = transcribe("audio.wav")
        st.write("Transcribed audio successfully ✅")

    st.chat_message("human", avatar="😎").markdown(prompt)
    st.session_state.messages.append({"role": "human", "content": prompt})

    # transcribe audio
    response = predict(message=prompt)

    with st.chat_message("assistant", avatar='🔥'):
        st.markdown(response)

    # Convert AI response to speech
    speech_file = text_to_speech(response)

    # Play the generated speech
    st.audio(speech_file, format="audio/mp3")

    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})

# React to user input
if prompt := textinput:
    # Display user message in chat message container
    st.chat_message("human", avatar="🔥").markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "human", "content": prompt})

    response = predict(message=prompt)

    # Convert AI response to speech
    speech_file = text_to_speech(response)

    # Display assistant response in chat message container
    with st.chat_message("assistant", avatar='🔥'):
        st.markdown(response)

    # Play the generated speech
    st.audio(speech_file, format="audio/mp3")

    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})