from gpt_index import GPTSimpleVectorIndex
from langchain import OpenAI
import gradio as gr
import sys
import os
import datetime

os.environ["OPENAI_API_KEY"] = os.environ['SECRET_CODE']


def get_index(index_file_path):
    if os.path.exists(index_file_path):
        return GPTSimpleVectorIndex.load_from_disk(index_file_path)
    else:
        print(f"Error: '{index_file_path}' does not exist.")
        sys.exit()


def chatbot(input_text, mentioned_person='Mediator John Haynes'):
    index = get_index('index.json')
    prompt = f"You are {mentioned_person}: {input_text}\n\n At the end of your answer  ask a provocative question."
    response = index.query(prompt, response_mode="compact")



   # code to save chat log to file
    current_time = datetime.datetime.now()
    current_time_str = current_time.strftime("%Y-%m-%d_%H-%M-%S")
    chat_log_filename = "chat_history.txt"
    chat_log_dir = os.path.dirname(os.path.abspath(__file__))
    chat_log_filepath = os.path.join(chat_log_dir, chat_log_filename)
    with open(chat_log_filepath, "a") as f:
        f.write(f"Chat at {current_time_str}\n")
        f.write(f"User: {input_text}\n")
        f.write(f"Chatbot: {response.response}\n\n")
        print(f"Chat log written to {chat_log_filepath}")
    
    # return the response
    return response.response



iface = gr.Interface(fn=chatbot, 
                     inputs=gr.inputs.Textbox("Enter your question"), 
                     outputs="text",
                     title="AI Chatbot trained on J. Haynes mediation material, v0.1",
                     description="test")
iface.launch()