from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Qdrant
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import ConversationalRetrievalChain
from langchain.llms import OpenAI
from langchain.memory import ConversationBufferMemory
from qdrant_client import QdrantClient
from qdrant_client.http import models as qdrant_models
import os
from langchain.document_loaders import PyPDFLoader
import gradio as gr

openai_api_key = os.getenv('OPENAI_API_KEY')
openai_api_key = openai_api_key

pdf_folder_path = "files"

documents = []
for filename in os.listdir(pdf_folder_path):
    if filename.endswith(".pdf"):
        file_path = os.path.join(pdf_folder_path, filename)
        loader = PyPDFLoader(file_path)
        documents.extend(loader.load())

text_splitter = CharacterTextSplitter(chunk_size=512, chunk_overlap=25)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

qdrant_client = QdrantClient(":memory:")

qdrant_client.recreate_collection(
    collection_name="langchain_collection",
    vectors_config=qdrant_models.VectorParams(size=1536, distance=qdrant_models.Distance.COSINE)
)

from langchain_qdrant import QdrantVectorStore,FastEmbedSparse,RetrievalMode

sparse_embeddings = FastEmbedSparse(model_name="Qdrant/bm25")


vector_store =  QdrantVectorStore.from_documents(
    docs,
    embedding=embeddings,
    sparse_embedding=sparse_embeddings,
    location=":memory:",
    collection_name="langchain_collection",
    retrieval_mode=RetrievalMode.HYBRID,
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

retriever = vector_store.as_retriever()
# llm = OpenAI(temperature=0.4)
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.3)
conversational_chain = ConversationalRetrievalChain.from_llm(
    llm=llm,
    retriever=retriever,
    memory=memory
)

query = "What is COMVIVA CDR"
response = conversational_chain.invoke({"question": query})
print(response['answer'])




def chat_with_ai(user_input, chat_history):
    response = conversational_chain.invoke({"question":user_input})

    chat_history.append((user_input, str(response)))

    return chat_history, ""


def gradio_chatbot():
    with gr.Blocks() as demo:
        gr.Markdown("# Chat Interface for Langchain")

        chatbot = gr.Chatbot(label="Langchain Chatbot")
        user_input = gr.Textbox(
            placeholder="Ask a question...", label="Enter your question"
        )

        submit_button = gr.Button("Send")

        chat_history = gr.State([])

        submit_button.click(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])

        user_input.submit(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])

    return demo

gradio_chatbot().launch(debug=True)