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import logging
import sys
import gradio as gr
import asyncio
import nest_asyncio
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
from llama_index.llms import HuggingFaceLLM
from langchain.embeddings import HuggingFaceEmbeddings
from g4f import Provider, models
from langchain.llms.base import LLM
from llama_index.llms import LangChainLLM
from langchain_g4f import G4FLLM
nest_asyncio.apply()
documents = SimpleDirectoryReader('data').load_data()
embed_model = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-mpnet-base-v2"
)
async def main(query):
llm: LLM = G4FLLM(
model=models.gpt_35_turbo,
provider=Provider.DeepAi,
)
llm = LangChainLLM(llm=llm)
service_context = ServiceContext.from_defaults(chunk_size=512, llm=llm, embed_model=embed_model)
index = VectorStoreIndex.from_documents(documents, service_context=service_context)
query_engine = index.as_query_engine()
response = query_engine.query(query)
return response
iface = gr.Interface(fn=main, inputs="text", outputs="text")
iface.launch()
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