import gradio as gr
from g4f import Provider, models
from langchain.llms.base import LLM
import asyncio
import nest_asyncio
from llama_index import ServiceContext, LLMPredictor, PromptHelper
from llama_index.text_splitter import TokenTextSplitter
from llama_index.node_parser import SimpleNodeParser
from langchain.embeddings import HuggingFaceEmbeddings
from llama_index import SimpleDirectoryReader
from gradio import Interface
nest_asyncio.apply()

embed_model = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-mpnet-base-v2"
)
node_parser = SimpleNodeParser.from_defaults(text_splitter=TokenTextSplitter(chunk_size=1024, chunk_overlap=20))
prompt_helper = PromptHelper(
  context_window=4096,
  num_output=256,
  chunk_overlap_ratio=0.1,
  chunk_size_limit=None
)

from langchain_g4f import G4FLLM

async def main(question):
    llm: LLM = G4FLLM(
        model=models.gpt_35_turbo,
        provider=Provider.DeepAi,
    )
    from llama_index.llms import LangChainLLM

    llm = LangChainLLM(llm=llm)
    
    service_context = ServiceContext.from_defaults(llm=llm,
  embed_model=embed_model,
  node_parser=node_parser,
  prompt_helper=prompt_helper)

    documents = SimpleDirectoryReader("data/").load_data()
    index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)
    query_engine = index.as_query_engine(service_context=service_context)
    response = query_engine.query(question)
    return response

iface = Interface(fn=main, inputs="text", outputs="text")
iface.launch()