app.py

import streamlit as st
from gradio_client import Client
from st_audiorec import st_audiorec


# 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. This model helps you know about the latest advanced Farming Tips and Tricks. And helps you clarify your doubts regarding Agriculture.
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,	# str (filepath or URL to file) in 'inputs' Audio component
				"transcribe",	# str in 'Task' Radio component
				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 say about the latest advancements in agriculture, precision farming, and eco-friendly cultivation methods. 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,	# str in 'Message' Textbox component
                system_prompt,	# str in 'Optional system prompt' Textbox component
    			max_new_tokens,	# int | float (numeric value between 0 and 4096)
    			temperature,	# int | float (numeric value between 0.0 and 1.0)
    			Topp,	
                500,
    			Repetitionpenalty,	# int | float (numeric value between 1.0 and 2.0)
    			api_name="/chat"
        )
        st.write("Done")
        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 LLama-2-7b anything...")
wav_audio_data = st_audiorec()

if wav_audio_data != 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 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)
    # 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)#, temperature= temperatureSide,max_new_tokens=max_new_tokensSide, Topp=ToppSide,Repetitionpenalty=RepetitionpenaltySide)
    # Display assistant response in chat message container
    with st.chat_message("assistant", avatar='🦙'):
        st.markdown(response)
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response})