app_2.py

'''

CONFIG AND IMPORTS

'''
from config import default_config

from types import SimpleNamespace
import gradio as gr
import os, random
from pathlib import Path
import tiktoken
from getpass import getpass

from openai import OpenAI

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY", ""))
from langchain.text_splitter import MarkdownHeaderTextSplitter
import numpy as np

from langchain.embeddings import OpenAIEmbeddings
# from langchain.vectorstores import Chroma
from typing import Iterable
from gradio.themes.base import Base
from gradio.themes.utils import colors, fonts, sizes
import time




if os.getenv("OPENAI_API_KEY") is None:
  if any(['VSCODE' in x for x in os.environ.keys()]):
    print('Please enter password in the VS Code prompt at the top of your VS Code window!')
  os.environ["OPENAI_API_KEY"] = getpass("Paste your OpenAI key from: https://platform.openai.com/account/api-keys\n")

assert os.getenv("OPENAI_API_KEY", "").startswith("sk-"), "This doesn't look like a valid OpenAI API key"
print("OpenAI API key configured")

embeddings_model = OpenAIEmbeddings()

md = ""
directory_path = "safety_docs"

for filename in os.listdir(directory_path):
    if filename.endswith(".md"):
        with open(os.path.join(directory_path, filename), 'r') as file:
            content = file.read()
            md = md + content

markdown_document = md

headers_to_split_on = [
    ("#", "Header 1"),
    ("##", "Header 2"),
    ("###", "Header 3"),
]

markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
md_header_splits = markdown_splitter.split_text(markdown_document)

def find_nearest_neighbor(argument="", max_args_in_output=2):
    '''
        INPUT:
            argument (string)

        RETURN the nearest neighbor(s) in vectorDB to argument as string
    '''

    embeddings = embeddings_model
    embedding_matrix = np.array([embeddings.embed_query(text.page_content) for text in md_header_splits])
    argument_embedding = embeddings.embed_query(argument)

    dot_products = np.dot(embedding_matrix, argument_embedding)
    norms = np.linalg.norm(embedding_matrix, axis=1) * np.linalg.norm(argument_embedding)
    cosine_similarities = dot_products / norms

    nearest_indices = np.argsort(cosine_similarities)[-max_args_in_output:][::-1]

    arr = [md_header_splits[index].metadata for index in nearest_indices]
    output = ""
    for thing in arr:
        output = output + thing['Header 1'] + "\n"

    return output

def get_gpt_response(user_prompt, system_prompt=default_config.system_prompt, model=default_config.model_name, n=1, max_tokens=200):
    '''
    INPUT: 
    Argument
    user_prompt
    system_prompt
    model
    '''

    messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt},
            ]
    response = client.chat.completions.create(model=model,
    messages=messages,
    n=n,
    max_tokens=max_tokens)
    

    for choice in response.choices:
        generation = choice.message.content
        return generation

    
# return the gpt generated response
def greet1(argument):
    user_prompt = default_config.user_prompt_1 + argument + default_config.user_prompt_2
    response = get_gpt_response(user_prompt=user_prompt)
    return response

# return the nearest neighbor arguments
def greet2(argument):
    nearest_neighbor = find_nearest_neighbor(argument)
    return "Your argument may fall under the common arguments against AI safety. \nIs it one of these? \n" + nearest_neighbor + "\nSee the taxonomy of arguments below"




# theme = gr.themes.Monochrome()
theme = gr.themes.Monochrome(
    # neutral_hue=gr.themes.colors.red,

    #                           n,     boxes,   text,       nothing                      bottom text            most text
    neutral_hue=gr.themes.Color("red", "#636363", "#636363", "lightgrey", "lightgrey", "lightgrey", "lightgrey", "grey",        "red", "black",       "red"),
    primary_hue=gr.themes.Color("#8c0010", "#8c0010", "#8c0010", "#8c0010", "#8c0010", "#8c0010", "#8c0010", "#8c0010", "#8c0010", "#8c0010", "#8c0010"),
    secondary_hue=gr.themes.Color("white", "white", "white", "white", "white", "white", "white", "white", "white", "white", "white"),
)


theme = theme.set(
    body_background_fill="black",
    block_title_background_fill="black",
    block_background_fill="black",
    body_text_color="white",
    link_text_color='*primary_50',
    link_text_color_dark='*primary_50',
    link_text_color_active='*primary_50',
    link_text_color_active_dark='*primary_50',
    link_text_color_hover='*primary_50',
    link_text_color_hover_dark='*primary_50',
    link_text_color_visited='*primary_50',
    link_text_color_visited_dark='*primary_50'
)

css_string = """
@import url('https://fonts.googleapis.com/css2?family=Gabarito&family=Gothic+A1:wght@100;200;300;400;500;600;700;800;900&display=swap');
force_black_bg {
    background-color: blue !important;
    color: white !important;
    font-family: 'Gabarito', cursive !important;
}
force_black_bg *{
    background-color: blue !important;
    color: white !important;
    font-family: 'Gabarito', cursive !important;
}

footer{
    display:none !important
    }

"""
css_string2 = ""

# with gr.Blocks(theme=theme, css=css_string2) as demo:
#     with gr.Row(elem_id="force_black_bg"):
#         with gr.Column(elem_id="force_black_bg"):
#             seed = gr.Text( label="AI Safety Skepticism: What's Your Take?", placeholder="Enter an argument or something you'd like to say!")
#             btn = gr.Button("Generate >")

#             english = gr.Text(elem_id="themed_question_box", label="Common Argument Classifier")


#         with gr.Column():
#             german = gr.Text(label="Safetybot Response")
#     btn.click(greet2, inputs=[seed],outputs=english)
#     btn.click(greet1, inputs=[seed],outputs=german)


#     gr.Examples(["AGI is far away, I'm not worried", "AI is confined to a computer and cannot interact with the physical world", "AI isn't concious", "If we don't develop AGI, China will!", "If we don't develop AGI, the Americans will!"], inputs=[seed])

with gr.Blocks(css=css_string) as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.ClearButton([msg, chatbot])

    def respond(message, chat_history):
        bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
        chat_history.append((message, bot_message))
        time.sleep(2)
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])

demo.queue()
demo.launch()