app.py

import os
import subprocess
import sys

def install_with_subprocess(args):
    subprocess.run(args, check=True)

def remove_tensorflow():
    subprocess.run([sys.executable, '-m', 'pip', 'uninstall', '-y', 'tensorflow'], check=True)
    
print("CURRENT WORKING DIRECTORY:",os.getcwd())
print("CURRENT WORKING DIRECTORY LIST:",os.listdir(os.getcwd()))
setup_flag = 'setup_complete.flag'

if not os.path.exists(setup_flag):
    remove_tensorflow()
    os.system('wget https://indic-asr-public.objectstore.e2enetworks.net/ai4b_nemo.zip')
    os.system('unzip -q ai4b_nemo.zip')
    
    os.chdir('NeMo')
    install_with_subprocess([sys.executable, '-m', 'pip', 'install', '-e', '.'])
    os.system('bash reinstall.sh')
    os.chdir('..')

    os.system('git clone -q https://github.com/VarunGumma/IndicTransTokenizer')
    os.chdir('IndicTransTokenizer')
    install_with_subprocess([sys.executable,'-m','pip','install','-q','--editable','./'])
    os.chdir('..')

    subprocess.run(['apt-get', 'update'], check=True)
    subprocess.run(['apt-get', 'install', '-y', 'libsndfile1-dev', 'ffmpeg'], check=True)
    
    os.system('git clone https://github.com/gokulkarthik/TTS')
    os.chdir('TTS')
    install_with_subprocess(['pip3', 'install', '-e', '.[all]'])
    install_with_subprocess(['pip3', 'install', '-r', 'requirements.txt'])
    os.chdir('..')
    
    with open(setup_flag, 'w') as f:
        f.write('Setup complete')

    os.execv(sys.executable, ['python'] + sys.argv)
    

import gradio as gr
from torch import cuda, inference_mode
import nemo.collections.asr as nemo_asr
from IndicTransTokenizer import IndicProcessor
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


DEVICE = "cuda" if cuda.is_available() else "cpu"

print(f"Using device: {DEVICE}")


import os
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.document_loaders import PyPDFLoader

"""### Load and convert PDF data into vectorDB"""

pm_kisan_doc = "PM-KISANOperationalGuidelines(English).pdf"

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=600,
    chunk_overlap=100
)

loader = PyPDFLoader(pm_kisan_doc)
pages = loader.load_and_split(text_splitter=text_splitter)

pages_chunks = [page.page_content for page in pages]
print(f"Generated {len(pages_chunks)} chunks of {pm_kisan_doc}")

# pages_chunks[8]

embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")

faiss = FAISS.from_texts(pages_chunks, embeddings)

"""### Querying the vectorDB"""

# Test query
# result = faiss.similarity_search("what are the benefits of PM kisan yojna", k=3)

# This returns the most relevant doc similar to the query

# print(result[0].page_content)

# Result_with_score = faiss.similarity_search_with_score("what are the benefits of PM kisan yojna", k=3)
# Result_with_score[0]



gemini_api_key = os.getenv('GEMINI_API_KEY')

import google.generativeai as genai

def get_gemini_output(prompt, temperature=0.6):

    genai.configure(api_key= gemini_api_key)
    model =  genai.GenerativeModel(model_name='gemini-pro')
    answer = model.generate_content(prompt)

    return answer.text

"""## Build an end-to-end RAG powered Voice Assistant
"""

ip = IndicProcessor(inference=True)


# Commented out IPython magic to ensure Python compatibility.
# # %%capture
 
en2indic_tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True)
en2indic_model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-en-indic-dist-200M", trust_remote_code=True)


# Commented out IPython magic to ensure Python compatibility.
# # %%capture
 
indic2en_tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indictrans2-indic-en-dist-200M", trust_remote_code=True)
indic2en_model = AutoModelForSeq2SeqLM.from_pretrained("ai4bharat/indictrans2-indic-en-dist-200M", trust_remote_code=True)


model_tokenizer_config = {
    "en2indic": {
        "tokenizer": en2indic_tokenizer,
        "model": en2indic_model,
    },
    "indic2en": {
        "tokenizer": indic2en_tokenizer,
        "model": indic2en_model,
    }
}

def indic_translate(src_lang: str, tgt_lang: str, sents_to_translate: list):

  lang_map = {
    "punjabi": "pan_Guru",
    "bengali": "ben_Beng",
    "malayalam": "mal_Mlym",
    "marathi": "mar_Deva",
    "tamil": "tam_Taml",
    "gujarati": "guj_Gujr",
    "telugu": "tel_Telu",
    "hindi": "hin_Deva",
    "kannada": "kan_Knda",
    "odia": "ory_Orya",
    "english": "eng_Latn"
    }

  src_lang = lang_map[src_lang]
  tgt_lang = lang_map[tgt_lang]

  if src_lang == "eng_Latn":
    tokenizer = model_tokenizer_config["en2indic"]["tokenizer"]
    model = model_tokenizer_config["en2indic"]["model"]

    print(f"Using en2indic, src_lang: {src_lang}, tgt_lang: {tgt_lang}")

  else:
    tokenizer = model_tokenizer_config["indic2en"]["tokenizer"]
    model = model_tokenizer_config["indic2en"]["model"]

    print(f"Using indic2en, src_lang: {src_lang}, tgt_lang: {tgt_lang}")


  batch = ip.preprocess_batch(sents_to_translate, src_lang=src_lang, tgt_lang=tgt_lang, show_progress_bar=False)
  batch = tokenizer(batch, padding="longest", truncation=True, max_length=256, return_tensors="pt")

  with inference_mode():
      print("Generating...")
      outputs = model.generate(**batch, num_beams=5, num_return_sequences=1, max_length=256)

  with tokenizer.as_target_tokenizer():
      outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True)

  if tgt_lang != "en_Latn":
    print(f"Postprocessing for {tgt_lang}")
    outputs = ip.postprocess_batch(outputs, lang=tgt_lang)


  return outputs

def download_ai4b_tts_model(lang: str):

  lang_map = {
      "odia": "or",
      "hindi": "hi",
      "tamil": "ta",
      "telugu": "te",
      "punjabi": "pa",
      "kannada": "kn",
      "bengali": "bn",
      "marathi": "mr",
      "gujarati": "gu",
      "malayalam": "ml",
  }

  selected_lang = lang_map[lang]

  download_path = f"/{selected_lang}.zip"

  if os.path.exists(download_path):
    print(f"IndicTTS Model for {lang} already exists.")

def run_tts(text, tts_lang):

  lang_map = {
      "odia": "or",
      "hindi": "hi",
      "tamil": "ta",
      "telugu": "te",
      "punjabi": "pa",
      "kannada": "kn",
      "bengali": "bn",
      "marathi": "mr",
      "gujarati": "gu",
      "malayalam": "ml",
  }

  download_ai4b_tts_model(lang=tts_lang)

  tts_lang = lang_map[tts_lang]
  print(f"Lang code: {tts_lang}")

  print("TTS WORKING DIRECTORY:",os.getcwd())
  tts_command = f'python3 -m TTS.bin.synthesize --text "{text}" \
    --model_path /models/v1/{tts_lang}/fastpitch/best_model.pth \
    --config_path /models/v1/{tts_lang}/fastpitch/config.json \
    --vocoder_path /models/v1/{tts_lang}/hifigan/best_model.pth \
    --vocoder_config_path /models/v1/{tts_lang}/hifigan/config.json \
    --speakers_file_path /models/v1/{tts_lang}/fastpitch/speakers.pth \
    --out_path /tts_output.wav \
    --speaker_idx male'

  if DEVICE == "cuda":
    tts_command += " --use_cuda True"
    print(f"Running IndicTTS on GPU")

  else:
    print(f"Running IndicTTS on CPU")

  os.system(tts_command)

os.makedirs('asr_models', exist_ok=True)
def download_ai4b_asr_model(lang: str):

  available_langs = {
      "odia": "or",
      "hindi": "hi",
      "tamil": "ta",
      "telugu": "te",
      "punjabi": "pa",
      "kannada": "kn",
      "bengali": "bn",
      "marathi": "mr",
      "gujarati": "gu",
      "malayalam": "ml",
  }

  os.makedirs('asr_models', exist_ok=True)
  # download_path = f"/asr_models/ai4b_indicConformer_{available_langs[lang]}.nemo"
  download_dir = "asr_models"
  download_path = os.path.join(download_dir,f"ai4b_indicConformer_{available_langs[lang]}.nemo")
  print(f"Downloaded ASR model path: {download_path}")

  if os.path.exists(download_path):
      print(f"Model for {lang} already exists.")

  elif lang not in available_langs:
      raise ValueError(f"Invalid language code: {lang}")
  else:
      url = f"https://objectstore.e2enetworks.net/indic-asr-public/indicConformer/ai4b_indicConformer_{available_langs[lang]}.nemo"
      try:
          result= subprocess.run(['wget', url, '-O', download_path], check=True, capture_output=True, text=True)
          print("ASR MODEL DOWNLOADED SUCCESSFULLY",result.stdout)
      except subprocess.CallProcessError as e:
          print(f"Error occured: {e.stderr}")
          raise

  return download_path

os.makedirs('asr_models', exist_ok=True)

def ensure_numpy_version():
    try:
        import numpy as np
        required_version = '1.23'
        if np.__version__ != required_version:
            subprocess.run(['pip', 'install', f'numpy=={required_version}'], check=True)
    except ImportError:
        subprocess.run(['pip', 'install', 'numpy==1.21.0'], check=True)

ensure_numpy_version()

subprocess.run(['pip', 'install', 'numba==0.60.0'], check=True)


# import librosa

# def preprocess_audio(audio_path):
#     audio,sr = librosa.load(audio_path,sr=None,mono=True)
#     return audio, sr

def transcribe(audio: str, lang: str):
    # audio, sr = preprocess_audio(audio)

    lang_map = {
      "odia": "or",
      "hindi": "hi",
      "tamil": "ta",
      "telugu": "te",
      "punjabi": "pa",
      "kannada": "kn",
      "bengali": "bn",
      "marathi": "mr",
      "gujarati": "gu",
      "malayalam": "ml",
    }

    download_path = download_ai4b_asr_model(lang=lang)

    asr_model = nemo_asr.models.ASRModel.restore_from(
          download_path, map_location=DEVICE
    )

    # transcription = asr_model.transcribe(audio, batch_size=1, language_id=lang_map[lang])[0][0]
    # transcription = asr_model.transcribe(paths2audio_files=[audio], batch_size=1)[0]
    transcription = asr_model.transcribe([audio], batch_size=1, language_id=lang_map[lang])[0][0]
    print(f"Transcription: {transcription}")

    return transcription

def query_vector_db(query):
  # Combine the top-3 similar documents from the vectorDB
  result = " ".join([result.page_content for result in faiss.similarity_search(query, k=3)])

  return result

from langchain_core.prompts import PromptTemplate

def process_user_query(user_query, retrieved_doc):

  prompt_template = PromptTemplate.from_template(
    "You are a chatbot , which provides information to user based on their queries, \
    the user asks: {user_query}, The information from the related query is: {retrieved_doc}. \
    Now give the output based on the query and relevant information that i provided, written in a structured, well-formatted and concise way. \
    The length of the output should be no more than 70 words, must be in 5 lines."
  )

  prompt = prompt_template.format(user_query=user_query, retrieved_doc=retrieved_doc)
  print("Input prompt:", prompt)
  
  processed_doc = get_gemini_output(prompt)
  print("Output prompt:",processed_doc)

  return processed_doc




def process_gradio_input(audio, user_lang):
  
    # Use IndicASR to transcribe the input audio
    print(f"Transcribing...")
    query_transcription = transcribe(audio, lang=user_lang)

    # Convert the Indic text from transcription to English, so that GPT-3.5 can process it
    print(f"Translating indic to en..")
    indic_to_en = indic_translate(src_lang=user_lang, tgt_lang="english", sents_to_translate=[query_transcription])[0]


    # context = context_manager.get_context()
    # contexulized_query = f"Previous context: {context} \n\nCurrent query: {indic_to_en}"

    # Query the Vector DB to get the relevant document from the query
    print(f"Querying vector db")
    retrieved_doc = query_vector_db(indic_to_en)

    # Extract relevant information from the retrieved document
    print(f"Processing user query")
    processed_doc = process_user_query(user_query=indic_to_en, retrieved_doc=retrieved_doc)

    # context_manager.add_interaction(indic_to_en, processed_doc)

    # Break the document into chunks for faster batch processing
    print(f"Breaking document into chunks..")
    processed_doc_chunks = processed_doc.strip().split(". ")
    processed_doc_chunks = [f"{chunk}." for chunk in processed_doc_chunks if chunk != ""]

    # Translate the the extracted information back to Indic language
    print(f"Translating en to indic..")
    en_to_indic_chunks = indic_translate(src_lang="english", tgt_lang=user_lang, sents_to_translate=processed_doc_chunks)
    en_to_indic_doc = " ".join(en_to_indic_chunks)
    print(f"en_to_indic_doc: {en_to_indic_doc}")

    # Run IndicTTS to generate audio
    print(f"Running TTS to generate audio..")
    run_tts(text=en_to_indic_doc, tts_lang=user_lang)
    print("Finished running TTS")

    print("PRESENT WORKING DIRECTORY OF AUDIO SAVED:", os.getcwd())
    audio_outfile_path = "tts_output.wav"


    return en_to_indic_doc, audio_outfile_path


def launch_gradio_app(show_log=False):

  languages = ["hindi", "odia", "tamil", "telugu", "punjabi", "kannada", "bengali", "marathi", "gujarati", "malayalam"]

  iface = gr.Interface(
      fn=process_gradio_input,
      inputs=[
          gr.Audio(sources=['upload', 'microphone'], type="filepath", show_download_button=True),  # Input audio
          gr.Dropdown(languages, label="Language", value="hindi"),  # Language selection
      ],
      outputs=["text", "audio"],
      allow_flagging="never",
      title="Farmer's Voice Assistant 🧑‍🌾 Powered by AI4Bharat Tech",
      description="Know about latest farming schemes, this system is powered by tools from AI4Bharat, like IndicASR, IndicTTS and IndicTrans",
  )

  iface.launch(debug=show_log)

launch_gradio_app(show_log=True)