app.py

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

# Constants
TITLE = "AgriTure"
DESCRIPTION = """
----
This Project demonstrates a model fine-tuned by Achyuth. This Model is named as "AgriTure". This Model helps the farmers and scientists to develop the art of agriculture and farming.
Hope this will be a Successful Project!!!
~Achyuth
----
"""

# Initialize client
with st.sidebar:
    system_promptSide = st.text_input("Optional system prompt:")
    temperatureSide = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.9, step=0.05)
    max_new_tokensSide = st.slider("Max new tokens", min_value=0.0, max_value=4096.0, value=4096.0, 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("Repetition penalty", min_value=0.0, max_value=2.0, value=1.2, step=0.05)

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


def transcribe(wav_path):
    return whisper_client.predict(
        wav_path,
        "transcribe",
        api_name="/predict"
    )

# Prediction function
def predict(message, system_prompt='Your name is OpenGPT. You are developed by Achyuth. You need to mostly focus on giving information about future agriculture and advanced farming. Empower yourself farming future with cutting-edge technology and sustainable practices. You need to cultivate a greener and more productive. Your developer is studying in The Hyderabad Public School Kadapa.', temperature=0.7, max_new_tokens=4096, Topp=0.5, Repetitionpenalty=1.2):
    with st.status("Starting client"):
        client = Client("https://huggingface-projects-llama-2-7b-chat.hf.space/")
        st.write("Requesting Audio Transcriber")
    with st.status("Requesting AgriTure v1"):
        st.write("Requesting API")
        response = client.predict(
            message,
            system_prompt,
            max_new_tokens,
            temperature,
            Topp,
            500,
            Repetitionpenalty,
            api_name="/chat"
        )
        st.write("Done")
        return response

# Function to convert text to speech
def text_to_speech(text, language='en', filename='output.mp3'):
    tts = gTTS(text=text, lang=language, slow=False)
    tts.save(filename)
    os.system(f'start {filename}')

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

# Initialize session state
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 AgriTure anything...")

# Assume st_audiorec is a simplified audio recorder function
wav_audio_data = st.audio_recorder()

if wav_audio_data is not None:
    with st.status("Transcribing audio..."):
        with open("audio.wav", "wb") as f:
            f.write(wav_audio_data)
        prompt = transcribe("audio.wav")
        st.write("Transcribed Given Audio ✔")

    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)
    st.session_state.messages.append({"role": "assistant", "content": response})

    # Convert response to audio
    text_to_speech(response)

# React to user input
if prompt := textinput:
    st.chat_message("human", avatar="💬: ").markdown(prompt)
    st.session_state.messages.append({"role": "human", "content": prompt})

    response = predict(message=prompt)

    with st.chat_message("assistant", avatar='🦙'):
        st.markdown(response)
    st.session_state.messages.append({"role": "assistant", "content": response})

    # Convert response to audio
    text_to_speech(response)