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 us 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?"]]
).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", title=title,
#     description=description,
#     article=article,).launch()