app.py

import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_groq import ChatGroq
from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
import tempfile
from gtts import gTTS
import os

def text_to_speech(text):
    tts = gTTS(text=text, lang='en')
    audio_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
    temp_filename = audio_file.name
    tts.save(temp_filename)
    st.audio(temp_filename, format='audio/mp3')
    os.remove(temp_filename)

def get_pdf_text(pdf_docs):
    text=""
    for pdf in pdf_docs:
        pdf_reader= PdfReader(pdf)
        for page in pdf_reader.pages:
            text+= page.extract_text()
    return  text

def get_text_chunks(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    chunks = text_splitter.split_text(text)
    return chunks
    
def get_vector_store(text_chunks, api_key):
    embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=api_key, model_name="sentence-transformers/all-MiniLM-l6-v2")
    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
    vector_store.save_local("faiss_index")

def get_conversational_chain():

    prompt_template = """
    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
    Context:\n {context}?\n
    Question: \n{question}\n
    Answer:
    """

    model = ChatGroq(temperature=0, groq_api_key=os.environ["groq_api_key"], model_name="llama3-8b-8192")

    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)

    return chain

def user_input(user_question, api_key):
    embeddings = HuggingFaceInferenceAPIEmbeddings(api_key=api_key, model_name="sentence-transformers/all-MiniLM-l6-v2")
    
    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
    docs = new_db.similarity_search(user_question)

    chain = get_conversational_chain()

    response = chain(
        {"input_documents":docs, "question": user_question}
        , return_only_outputs=True)

    print(response)  # Debugging line
    
    st.write("Replies:")
    if isinstance(response["output_text"], str):
        response_list = [response["output_text"]]
    else:
        response_list = response["output_text"]
    
    for text in response_list:
        st.write(text)
        # Convert text to speech for each response
        text_to_speech(text)

def main():
    
    st.set_page_config(layout="centered")
    st.header("Chat with DOCS")
    st.markdown("<h1 style='font-size:20px;'>ChatBot by Muhammad Huzaifa</h1>", unsafe_allow_html=True)
    api_key = st.secrets["inference_api_key"]
    
    
    with st.sidebar:
        st.header("Chat with PDF")
        # st.title("Menu:")
        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit Button", accept_multiple_files=True, type=["pdf"])
        if st.button("Submit"):
            with st.spinner("Processing..."):
                raw_text = get_pdf_text(pdf_docs)
                text_chunks = get_text_chunks(raw_text)
                get_vector_store(text_chunks, api_key)
                st.success("Done")

    # Check if any document is uploaded
    if pdf_docs:
        user_question = st.text_input("Ask a question from the Docs")
        if user_question:
            user_input(user_question, api_key)
    else:
        st.write("Please upload a document first to ask questions.")
           
if __name__ == "__main__":
    main()