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)
    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="wide")
    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"]

    # Sidebar column for file upload
    with st.sidebar:
        # st.header("Chat with PDF")
    
    # Main column for displaying extracted text and user interaction
    col1, col2 = st.columns([1, 2])

    if pdf_docs and col1.button("Submit"):
        with col1:
            pdf_docs = st.sidebar.file_uploader("Upload your PDF Files", accept_multiple_files=True, type=["pdf"])
            st.spinner("Processing..."):
            raw_text = get_pdf_text(pdf_docs)
            text_chunks = get_text_chunks(raw_text)
            get_vector_store(text_chunks, api_key)
            col1.success("Processing Complete")
            if col1.success("Processing Complete"):
                user_question = st.text_input("Ask a question from the Docs")
                if user_question:
                    user_input(user_question, api_key)
    if raw_text:
        with col2:
            st.subheader("Extracted Text from PDF:")
            st.text(raw_text)



if __name__ == "__main__":
    main()