backupapp.py

import streamlit as st
import re
import json

def remove_timestamps(text):
    return re.sub(r'\d{1,2}:\d{2}\n', '', text)

def process_text(text):
    lines = text.split("\n")
    processed_lines = []

    for line in lines:
        if line:
            processed_lines.append(line)

    outline = ""
    for i, line in enumerate(processed_lines):
        if i % 2 == 0:
            outline += f"**{line}**\n"
        else:
            outline += f"- {line} 😄\n"

    return outline

def create_jsonl_list(text):
    lines = text.split("\n")
    jsonl_list = []

    for line in lines:
        if line:
            jsonl_list.append({"text": line})

    return jsonl_list

def unit_test(input_text):
    st.write("Test Text without Timestamps:")
    test_text_without_timestamps = remove_timestamps(input_text)
    st.write(test_text_without_timestamps)

    st.write("Test JSONL List:")
    test_jsonl_list = create_jsonl_list(test_text_without_timestamps)
    st.write(test_jsonl_list)

text_input = st.text_area("Enter text:", value="", height=300)
text_without_timestamps = remove_timestamps(text_input)

st.markdown("**Text without Timestamps:**")
st.write(text_without_timestamps)

processed_text = process_text(text_without_timestamps)
st.markdown("**Markdown Outline with Emojis:**")
st.markdown(processed_text)

unit_test_text = '''
1:42
program the does very very well on your data then you will achieve the best
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generalization possible with a little bit of modification you can turn it into a precise theorem
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and on a very intuitive level it's easy to see what it should be the case if you
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have some data and you're able to find a shorter program which generates this
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data then you've essentially extracted all the all conceivable regularity from
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this data into your program and then you can use these objects to make the best predictions possible like if if you have
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data which is so complex but there is no way to express it as a shorter program
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then it means that your data is totally random there is no way to extract any regularity from it whatsoever now there
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is little known mathematical theory behind this and the proofs of these statements actually not even that hard
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but the one minor slight disappointment is that it's actually not possible at
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least given today's tools and understanding to find the best short program that explains or generates or
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solves your problem given your data this problem is computationally intractable
'''

unit_test(unit_test_text)

unit_test_text_2 = '''
5
to talk a little bit about reinforcement learning so reinforcement learning is a framework it's a framework of evaluating
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agents in their ability to achieve goals and complicated stochastic environments
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you've got an agent which is plugged into an environment as shown in the figure right here and for any given
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agent you can simply run it many times and compute its average reward now the
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thing that's interesting about the reinforcement learning framework is that there exist interesting useful
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reinforcement learning algorithms the framework existed for a long time it
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became interesting once we realized that good algorithms exist now these are there are perfect algorithms but they
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are good enough todo interesting things and all you want the mathematical
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problem is one where you need to maximize the expected reward now one
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important way in which the reinforcement learning framework is not quite complete is that it assumes that the reward is
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given by the environment you see this picture the agent sends an action while
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the reward sends it an observation in a both the observation and the reward backwards that's what the environment
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communicates back the way in which this is not the case in the real world is that we figure out
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what the reward is from the observation we reward ourselves we are not told
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environment doesn't say hey here's some negative reward it's our interpretation over census that lets us determine what
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the reward is and there is only one real true reward in life and this is
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existence or nonexistence and everything else is a corollary of that so well what
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should our agent be you already know the answer should be a neural network because whenever you want to do
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something dense it's going to be a neural network and you want the agent to map observations to actions so you let
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it be parametrized with a neural net and you apply learning algorithm so I want to explain to you how reinforcement
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learning works this is model free reinforcement learning the reinforcement learning has actually been used in practice everywhere but it's
'''

unit_test(unit_test_text_2)