code: import os
  import dotenv
  import gradio as gr
  import lancedb
  import logging
  from langchain.embeddings.cohere import CohereEmbeddings
  from langchain.llms import Cohere
  from langchain.prompts import PromptTemplate
  from langchain.chains import RetrievalQA
  from langchain.vectorstores import LanceDB
  from langchain.document_loaders import TextLoader
  from langchain.text_splitter import RecursiveCharacterTextSplitter
  from langchain_community.document_loaders import PyPDFLoader
  import argostranslate.package
  import argostranslate.translate
  
  
  # Configuration Management
  dotenv.load_dotenv(".env")
  DB_PATH = "/tmp/lancedb"
  
  COHERE_MODEL_NAME = "multilingual-22-12"
  LANGUAGE_ISO_CODES = {
      "English": "en",
      "Hindi": "hi",
      "Turkish": "tr",
      "French": "fr",
  }
  
  # Logging Configuration
  logging.basicConfig(level=logging.INFO)
  logger = logging.getLogger(__name__)
  
  
  def initialize_documents_and_embeddings(input_file_path):
      """
      Initialize documents and their embeddings from a given file.
  
      Parameters:
      - input_file_path (str): The path to the input file. Supported formats are .txt and .pdf.
  
      Returns:
      - tuple: A tuple containing a list of texts split from the document and the embeddings object.
      """
      file_extension = os.path.splitext(input_file_path)[1]
      if file_extension == ".txt":
          logger.info("txt file processing")
          # Handle text file
          loader = TextLoader(input_file_path)
          documents = loader.load()
          text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
          texts = text_splitter.split_documents(documents)
      elif file_extension == ".pdf":
          logger.info("pdf file processing")
          # Handle PDF file
          loader = PyPDFLoader(input_file_path)
          texts = loader.load_and_split()
          text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
          texts = text_splitter.split_documents(texts)
      else:
          raise ValueError(
              "Unsupported file type. Supported files are .txt and .pdf only."
          )
  
      embeddings = CohereEmbeddings(model=COHERE_MODEL_NAME)
      return texts, embeddings
  
  
  # Database Initialization
  def initialize_database(texts, embeddings):
      """
      Initialize and populate a LanceDB database with documents and their embeddings.
  
      Parameters:
      - texts (list): A list of texts to be stored in the database.
      - embeddings (CohereEmbeddings): An embeddings object used to generate vector embeddings for the texts.
  
      Returns:
      - LanceDB: An instance of LanceDB with the documents and their embeddings stored.
      """
      db = lancedb.connect(DB_PATH)
      table = db.create_table(
          "multiling-rag",
          data=[
              {
                  "vector": embeddings.embed_query("Hello World"),
                  "text": "Hello World",
                  "id": "1",
              }
          ],
          mode="overwrite",
      )
      return LanceDB.from_documents(texts, embeddings, connection=table)
  
  
  # Translation Function
  def translate_text(text, from_code, to_code):
      """
      Translate a given text from one language to another.
  
      Parameters:
      - text (str): The text to translate.
      - from_code (str): The ISO language code of the source language.
      - to_code (str): The ISO language code of the target language.
  
      Returns:
      - str: The translated text.
      """
      try:
          argostranslate.package.update_package_index()
          available_packages = argostranslate.package.get_available_packages()
          package_to_install = next(
              filter(
                  lambda x: x.from_code == from_code and x.to_code == to_code,
                  available_packages,
              )
          )
          argostranslate.package.install_from_path(package_to_install.download())
          return argostranslate.translate.translate(text, from_code, to_code)
      except Exception as e:
          logger.error(f"Error in translate_text: {str(e)}")
          return "Translation error"
  
  
  prompt_template = """Text: {context}
  
  Question: {question}
  
  Answer the question based on the text provided. If the text doesn't contain the answer, reply that the answer is not available."""
  PROMPT = PromptTemplate(
      template=prompt_template, input_variables=["context", "question"]
  )
  
  
  # Question Answering Function
  def answer_question(question, input_language, output_language, db):
      """
      Answer a given question by retrieving relevant information from a database,
      translating the question and answer if necessary.
      Parameters:
      - question (str): The question to answer.
      - input_language (str): The language of the input question.
      - output_language (str): The desired language of the answer.
      - db (LanceDB): The LanceDB instance to use for information retrieval.
  
      Returns:
      - str: The answer to the question, in the desired output language
      """
      try:
          input_lang_code = LANGUAGE_ISO_CODES[input_language]
          output_lang_code = LANGUAGE_ISO_CODES[output_language]
  
          question_in_english = (
              translate_text(question, from_code=input_lang_code, to_code="en")
              if input_language != "English"
              else question
          )
          prompt = PromptTemplate(
              template=prompt_template, input_variables=["context", "question"]
          )
          qa = RetrievalQA.from_chain_type(
              llm=Cohere(model="command", temperature=0),
              chain_type="stuff",
              retriever=db.as_retriever(),
              chain_type_kwargs={"prompt": prompt},
              return_source_documents=True,
          )
  
          answer = qa({"query": question_in_english})
          result_in_english = answer["result"].replace("\n", "").replace("Answer:", "")
  
          return (
              translate_text(result_in_english, from_code="en", to_code=output_lang_code)
              if output_language != "English"
              else result_in_english
          )
      except Exception as e:
          logger.error(f"Error in answer_question: {str(e)}")
          return "An error occurred while processing your question. Please try again."
  
  
  def setup_gradio_interface(db):
      """
      Setup a Gradio interface for interacting with the multilingual chatbot.
  
      Parameters:
      - db (LanceDB): The database instance to use for information retrieval.
  
      Returns:
      - gr.Interface: A Gradio interface object for the chatbot.
      """
  
      return gr.Interface(
          fn=lambda question, input_language, output_language: answer_question(
              question, input_language, output_language, db
          ),
          inputs=[
              gr.Textbox(lines=2, placeholder="Type your question here..."),
              gr.Dropdown(list(LANGUAGE_ISO_CODES.keys()), label="Input Language"),
              gr.Dropdown(list(LANGUAGE_ISO_CODES.keys()), label="Output Language"),
          ],
          outputs="text",
          title="Multilingual Chatbot",
          description="Ask any question in your chosen language and get an answer in the language of your choice.",
      )
  
  
  # Main Function
  def main():
      INPUT_FILE_PATH = "healthy-diet-fact-sheet-394.pdf"
      texts, embeddings = initialize_documents_and_embeddings(INPUT_FILE_PATH)
      db = initialize_database(texts, embeddings)
      iface = setup_gradio_interface(db)
      iface.launch(share=True, debug=True)
  
  
  if __name__ == "__main__":
      main()
  
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