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'] = os.environ["my_key"]

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

# 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 = """
<center>"
The program is trained to answer questions based on the documentation of 'Lessons Learned' from previous projects!
 <img src= "https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>>
 </center>
 """
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?"], ["How to determine the exact location of existing underground lines?"], ['What one should do to avoid struck-by hazard incidents?']]
).launch()

import gradio as gr