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
import docx
from pptx import Presentation

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 read_text_from_pdf(pdf_file):
    pdf_reader = PdfReader(pdf_file)
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()
    return text

def read_text_from_docx(docx_file):
    doc = docx.Document(docx_file)
    text = "\n".join([paragraph.text for paragraph in doc.paragraphs])
    return text

def read_text_from_pptx(pptx_file):
    presentation = Presentation(pptx_file)
    text = ""
    for slide in presentation.slides:
        for shape in slide.shapes:
            if hasattr(shape, "text"):
                text += shape.text + "\n"
    return text

def get_text_from_file(file):
    content = ""
    if file.type == "application/pdf":
        content = read_text_from_pdf(file)
    elif file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
        content = read_text_from_docx(file)
    elif file.type == "application/vnd.openxmlformats-officedocument.presentationml.presentation":
        content = read_text_from_pptx(file)
    elif file.type == "text/plain":
        content = file.getvalue().decode("utf-8")
    return content

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="centered")
    st.header("Chat with DOCS")
    st.markdown("<h1 style='font-size:24px;'>ChatBot by Muhammad Huzaifa</h1>", unsafe_allow_html=True)
    api_key = st.secrets["inference_api_key"]

    with st.sidebar:
        st.title("Menu:")
        uploaded_files = st.file_uploader("Upload your files (PDF, DOCX, PPTX, TXT)", accept_multiple_files=True)
        if st.button("Submit & Process"):
            with st.spinner("Processing..."):
                raw_text = ""
                for file in uploaded_files:
                    file_text = get_text_from_file(file)
                    raw_text += file_text
                text_chunks = get_text_chunks(raw_text)
                get_vector_store(text_chunks, api_key)
                st.success("Done")

    # Check if any document is uploaded
    if uploaded_files:
        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 (PDF, DOCX, PPTX, TXT) first to ask questions.")

if __name__ == "__main__":
    main()