import streamlit as st
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
from dotenv import load_dotenv
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
import os
from youtube_transcript_api import YouTubeTranscriptApi
import time
import shutil

# Load environment variables
load_dotenv()

# icons = {"assistant": "robot.png", "user": "man-kddi.png"}

# Configure the Llama index settings
Settings.llm = HuggingFaceInferenceAPI(
    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
    context_window=3000,
    token=os.getenv("HF_TOKEN"),
    max_new_tokens=512,
    generate_kwargs={"temperature": 0.1},
)
Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)

# Define the directory for persistent storage and data
PERSIST_DIR = "./db"
DATA_DIR = "data"

# Ensure data directory exists
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)

def data_ingestion():
    documents = SimpleDirectoryReader(DATA_DIR).load_data()
    print(documents)
    storage_context = StorageContext.from_defaults()
    index = VectorStoreIndex.from_documents(documents,show_progress=True)
    index.storage_context.persist(persist_dir=PERSIST_DIR)

def extract_transcript_details(youtube_video_url):
    try:
        video_id=youtube_video_url.split("=")[1]
        
        transcript_text=YouTubeTranscriptApi.get_transcript(video_id)

        transcript = ""
        for i in transcript_text:
            transcript += " " + i["text"]
       
        return transcript

    except Exception as e:
        st.error(e)

def remove_old_files():
    # Specify the directory path you want to clear
    directory_path = "data"

    # Remove all files and subdirectories in the specified directory
    shutil.rmtree(directory_path)

    # Recreate an empty directory if needed
    os.makedirs(directory_path)


def handle_query(query):
    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
    index = load_index_from_storage(storage_context)
    chat_text_qa_msgs = [
    (
        "user",
        """You are a Q&A assistant named CHATTO, created by Suriya. You have a specific response programmed for when users specifically ask about your creator, Suriya. The response is: "I was created by Suriya, an enthusiast in Artificial Intelligence. He is dedicated to solving complex problems and delivering innovative solutions. With a strong focus on machine learning, deep learning, Python, generative AI, NLP, and computer vision, Suriya is passionate about pushing the boundaries of AI to explore new possibilities." For all other inquiries, your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
        Context:
        {context_str}
        Question:
        {query_str}
        """
    )
    ]
    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
    
    query_engine = index.as_query_engine(text_qa_template=text_qa_template)
    answer = query_engine.query(query)

    final_ans = []
    if hasattr(answer, 'response'):
        final_ans.append(answer.response)
    elif isinstance(answer, dict) and 'response' in answer:
        final_ans.append(answer['response'])
    else:
        final_ans.append("Sorry, I couldn't find an answer.")

    ans = " ".join(final_ans)
    for i in ans:
        yield str(i)
        time.sleep(0.001)


# Streamlit app initialization
st.title("Chat with your PDF📄")
st.markdown("Built by [Suriya❤️](https://github.com/theSuriya)")
st.markdown("chat here👇")

if 'messages' not in st.session_state:
    st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}]
    
# Display or clear chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])
        
with st.sidebar:
    st.title("Menu:")
    uploaded_file = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button")
    video_url = st.text_input("Enter Youtube Video Link: ")
    if st.button("Submit & Process"):
        with st.spinner("Processing..."):
            if len(os.listdir("data")) !=0:
                remove_old_files()
                
            if uploaded_file:
                filepath = "data/saved_pdf.pdf"
                with open(filepath, "wb") as f:
                    f.write(uploaded_file.getbuffer())
        
            if video_url:
                extracted_text = extract_transcript_details(video_url)
                with open("data/saved_text.txt", "w") as file:
                    file.write(extracted_text)
                
            data_ingestion()  # Process PDF every time new file is uploaded
            st.success("Done")

user_prompt = st.chat_input("Ask me anything about the content of the PDF:")
if user_prompt and (uploaded_file or video_url):
    st.session_state.messages.append({'role': 'user', "content": user_prompt})
    with st.chat_message("user"):
        st.write(user_prompt)

# Generate a new response if last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        response = handle_query(user_prompt)
        full_response = st.write_stream(response)
    message = {"role": "assistant", "content": full_response}
    st.session_state.messages.append(message)