import os
import gradio as gr
from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex, LLMPredictor, ServiceContext, PromptHelper
from langchain.chat_models import ChatOpenAI

def init_index(directory_path):
    # Model parameters
    max_input_size = 4096
    num_outputs = 512
    max_chunk_overlap = 20
    chunk_size_limit = 600

    # Initialize LLM predictor with LangChain ChatOpenAI model
    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))

    # Read documents from specified directory
    documents = SimpleDirectoryReader(directory_path).load_data()

    # Initialize index with documents data
    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)

    # Save the created index
    index.save_to_disk('index.json')

    return index

def chatbot(input_text):
    # Load index
    index = GPTSimpleVectorIndex.load_from_disk('index.json')

    # Get response for the question
    response = index.query(input_text, response_mode="compact")

    return response.response

# Function to input OpenAI API key
def get_api_key():
    os.environ["OPENAI_API_KEY"] = input("Please enter your OpenAI API key: ")

# Create UI interface to interact with GPT-3 model
iface = gr.Interface(fn=chatbot,
                     inputs=gr.components.Textbox(lines=7, placeholder="Enter your question here"),
                     outputs="text",
                     title="Frost AI ChatBot: Your Knowledge Companion Powered by ChatGPT",
                     description="Ask any question about research papers",
                     allow_screenshot=True)

# Add API key input to interface
iface.add_input("textbox", label="OpenAI API Key", type="text", default=get_api_key())

# Initialize index
init_index("docs")

# Launch the interface
iface.launch(share=True)