import os
import pickle
import time
import gradio as gr
from langchain import OpenAI
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredURLLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from dotenv import load_dotenv

load_dotenv()  # take environment variables from .env (especially openai api key)

# Define the main function to process URLs and handle queries
def process_and_query(url1, url2, url3, query):
    urls = [url1, url2, url3]
    file_path = "faiss_store_openai.pkl"
    llm = OpenAI(temperature=0.9, max_tokens=500)

    # Load data
    loader = UnstructuredURLLoader(urls=urls)
    data = loader.load()

    # Split data
    text_splitter = RecursiveCharacterTextSplitter(
        separators=['\n\n', '\n', '.', ','],
        chunk_size=1000
    )
    docs = text_splitter.split_documents(data)

    # Create embeddings and save it to FAISS index
    embeddings = OpenAIEmbeddings()
    vectorstore_openai = FAISS.from_documents(docs, embeddings)

    # Save the FAISS index to a pickle file
    with open(file_path, "wb") as f:
        pickle.dump(vectorstore_openai, f)

    # Process the query
    if os.path.exists(file_path):
        with open(file_path, "rb") as f:
            vectorstore = pickle.load(f)
            chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vectorstore.as_retriever())
            result = chain({"question": query}, return_only_outputs=True)
            answer = result["answer"]

            # Extract and format sources
            sources = result.get("sources", "")
            sources_list = sources.split("\n") if sources else []
            return answer, sources_list

# Define the Gradio interface
url1_input = gr.Textbox(label="URL 1")
url2_input = gr.Textbox(label="URL 2")
url3_input = gr.Textbox(label="URL 3")
query_input = gr.Textbox(label="Question")

output_text = gr.Textbox(label="Answer")
output_sources = gr.Textbox(label="Sources")

interface = gr.Interface(
    fn=process_and_query,
    inputs=[url1_input, url2_input, url3_input, query_input],
    outputs=[output_text, output_sources],
    title="RockyBot: News Research Tool 📈",
    description="Enter up to three news article URLs and ask a question. The bot will process the articles and provide an answer along with the sources."
)

if __name__ == "__main__":
    interface.launch()