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 = "AgriTure"
DESCRIPTION = """
----
This Project demonstrates a model fine-tuned by Achyuth. This Model is named as "AgriaTure". 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,	# 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 AgriaTure. You are developed by Achyuth. Empower yourself farming future with cutting-edge technology and sustainable practices. You need to cultivate a greener and more productive. You need to give short answer like 2 to 5 sentences. If the user asks for any information about subject, then answer with long paragraphs.', 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 AgriTure 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})