import streamlit as st
import os
import langchain
import langchain_community
import langchain_openai
import qdrant_client
from langchain_community.document_loaders import PyMuPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import Qdrant
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from operator import itemgetter
from langchain_community.embeddings import HuggingFaceEmbeddings

# Print version information
print(f"langchain version: {langchain.__version__}")
print(f"langchain_community version: {langchain_community.__version__}")
print(f"langchain_openai version: {langchain_openai.__version__}")
print(f"qdrant_client version: {qdrant_client.__version__}")

# Set up API keys
os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]

# PDF links
pdf_links = [
    "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf",
    "https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf"
]

@st.cache_resource
def load_and_process_pdfs(pdf_links):
    documents = []
    for link in pdf_links:
        loader = PyMuPDFLoader(file_path=link)
        documents.extend(loader.load())

    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=500,
        chunk_overlap=40,
        length_function=len,
        is_separator_regex=False
    )

    return text_splitter.split_documents(documents)

@st.cache_resource
def setup_vectorstore():
    LOCATION = ":memory:"
    COLLECTION_NAME = "AI_Ethics_Framework"
    
    qdrant_client = QdrantClient(location=LOCATION)

    # Create the embeddings
    embeddings = HuggingFaceEmbeddings(model_name="Technocoloredgeek/midterm-finetuned-embedding")
    
    # Get the vector size from the embeddings
    VECTOR_SIZE = len(embeddings.embed_query("test"))

    # Create the collection
    qdrant_client.create_collection(
        collection_name=COLLECTION_NAME,
        vectors_config=VectorParams(size=VECTOR_SIZE, distance=Distance.COSINE),
    )

    # Create the vector store
    qdrant_vector_store = Qdrant(
        client=qdrant_client,
        collection_name=COLLECTION_NAME,
        embedding_function=embeddings
    )

    # Load and add documents
    documents = load_and_process_pdfs(pdf_links)
    qdrant_vector_store.add_documents(documents)

    return qdrant_vector_store

@st.cache_resource
def create_rag_pipeline(_vector_store):
    retriever = _vector_store.as_retriever()
    
    template = """
    You are an expert AI assistant with deep knowledge of business, technology, and entrepreneurship. Your task is to provide accurate, insightful answers based solely on the given context. Follow these guidelines:
    1. Analyze the question carefully to understand the core information being sought.
    2. Thoroughly examine the provided context, identifying key relevant information.
    3. Formulate a clear, concise answer that directly addresses the question.
    4. Use specific details and examples from the context to support your answer.
    5. If the context doesn't contain sufficient information to fully answer the question, state this clearly and say,'I don't know'.
    6. Do not introduce any information not present in the context.
    7. If asked for an opinion or recommendation, base it strictly on insights from the context.
    8. Use a confident, authoritative tone while maintaining accuracy.
    9. If you cannot provide a clear answer to the question, reply with "I don't know".
    Question:
    {question}
    Context:
    {context}
    Answer:
    """

    prompt = ChatPromptTemplate.from_template(template)
    primary_qa_llm = ChatOpenAI(model_name="gpt-4", temperature=0)

    retrieval_augmented_qa_chain = (
        {"context": itemgetter("question") | retriever, "question": itemgetter("question")}
        | RunnablePassthrough.assign(context=itemgetter("context"))
        | {"response": prompt | primary_qa_llm, "context": itemgetter("context")}
    )

    return retrieval_augmented_qa_chain

# Streamlit UI
st.title("Ask About AI Ethics!")

vector_store = setup_vectorstore()
rag_pipeline = create_rag_pipeline(vector_store)

user_query = st.text_input("Enter your question about AI Ethics:")

if user_query:
    with st.spinner("Generating response..."):
        result = rag_pipeline.invoke({"question": user_query})
    
    st.write("Response:")
    st.write(result["response"].content)

    st.write("Context Used:")
    st.write(result["context"])