from gpt_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain.chat_models import ChatOpenAI
import gradio as gr
import sys
import os
from gradio_client import Client



os.environ["OPENAI_API_KEY"] = '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'

# def construct_index(directory_path):
#     max_input_size = 4096
#     num_outputs = 512
#     max_chunk_overlap = 20
#     chunk_size_limit = 600

#     prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

#     llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0.7, model_name="gpt-3.5-turbo", max_tokens=num_outputs))

#     documents = SimpleDirectoryReader(directory_path).load_data()

#     index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper)

#     index.save_to_disk('index.json')

#     return index

def chatbot(input_text):
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    response = index.query(input_text, response_mode="compact")
    return response.response

iface = gr.Interface(fn=chatbot,
                     inputs=gr.components.Textbox(lines=7, label="Enter your text"),
                     outputs="text",
                     title="Custom-trained AI Chatbot")

# index = construct_index("docs")
iface.launch()
client = Client("https://karan156-custom-data-chatbot.hf.space/")
result = client.predict(
				input_text = "Howdy!",	# str  in 'Enter your text' Textbox component
				api_name="/predict"
)
print(result)