from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
import os
from llama_index.node_parser import SimpleNodeParser
from llama_index import StorageContext, load_index_from_storage
import gradio as gr
import openai

os.environ['OPENAI_API_KEY'] = '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'

# rebuild storage context
storage_context = StorageContext.from_defaults(persist_dir="index_dir")

# # load index
# index = load_index_from_storage(storage_context)

# # strat a search engine
# query_engine = index.as_query_engine()



# APP

# def get_model_reply_no_prev_context(question):
#     response = query_engine.query(question)
#     final_response = response.response[1:]
#     return final_response


# def get_model_reply_no_prev_context(question):
#     final_response = question
#     return final_response

# title = "Knowledge Center at Penta Building Group"
# description = """
# The program is trained to answer questions based on the documentation of 'Lessons Learned' from previous projects!

# """

# article = "Your feedback matters!If you like it, contact me at mgupta70@asu.edu"

# gr.Interface(
#     fn=get_model_reply_no_prev_context,
#     inputs="textbox",
#     outputs="text",
#     title=title,
#     description=description,
#     article=article,
#     examples=[["Which code is to be used while planning a pedestrian walkway?"], ["What is AHJ?"]], live=True
# ).launch()

import gradio as gr
def sketch_recognition(img):
    pass# Implement your sketch recognition model here...

gr.Interface(fn=sketch_recognition, inputs="sketchpad", outputs="label").launch()