import gradio as gr
from bs4 import BeautifulSoup as bs
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from sentence_transformers import SentenceTransformer
from transformers import pipeline
__import__('pysqlite3')
import sys
sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')

# Function to load, split, and retrieve documents
def load_and_retrieve_docs(url):
    loader = WebBaseLoader(
        web_paths=(url,),
        bs_kwargs=dict() 
    )
    docs = loader.load()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
    splits = text_splitter.split_documents(docs)
    embedding_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
    
    # Define a custom embedding function compatible with Chroma's interface
    class CustomEmbeddings:
        def __init__(self, model):
            self.model = model

        def embed_documents(self, texts):
            return self.model.encode(texts, convert_to_tensor=True).tolist()

    embeddings = CustomEmbeddings(embedding_model)
    
    vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings)
    return vectorstore.as_retriever()

# Function to format documents
def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

# Function that defines the RAG chain
def rag_chain(url, question):
    retriever = load_and_retrieve_docs(url)
    retrieved_docs = retriever.invoke(question)
    formatted_context = format_docs(retrieved_docs)
    formatted_prompt = f"Question: {question}\n\nContext: {formatted_context}"
    
    # Using Hugging Face's pipeline for text generation (QA model)
    qa_pipeline = pipeline("text-generation", model="gpt-2")
    response = qa_pipeline(formatted_prompt, max_length=200)
    
    return response[0]['generated_text']

# Gradio interface
iface = gr.Interface(
    fn=rag_chain,
    inputs=["text", "text"],
    outputs="text",
    title="RAG Chain Question Answering",
    description="Enter a URL and a query to get answers from the RAG chain."
)

# Launch the app
iface.launch()