import gradio as gr
import os
import openai

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings

import logging

# Configure logging
logging.basicConfig(
    level=logging.INFO,  # Set the logging level
    format='%(asctime)s - %(levelname)s - %(message)s',  # Define the log format
    handlers=[
        logging.StreamHandler()  # Output logs to the console
    ]
)


openai.api_key = os.environ['OpenAI_ApiKey']
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
logging.info("Start load document.")

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

def greet(question):
    return question
    # return query_engine.query(question)


demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()