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import json
import os
from typing import Any, Dict, Optional

import requests
import streamlit as st

# Page configuration - removed "wide" layout to make it narrower
st.set_page_config(page_title="PDF to Markdown Converter", page_icon="📄")

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import torch
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

model_id = "stabilityai/stable-diffusion-2"

# Use the Euler scheduler here instead
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

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如何使用Stable Diffusion Webui (GPU版本)

使用指令

git clone https://github.com/jim60105/docker-stable-diffusion-webui.git
cd docker-stable-diffusion-webui
docker compose up -d

注意事項

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"""
版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
原文链接：https://blog.csdn.net/James_Bond_slm/article/details/117432357

采用的收发都是3根天线，每根天线都有30个子载波，一秒钟3000个包，所以最后csv的数据维度是3000 * （30 * 3 * 3）即3000 * 270的大小。
"""

import numpy as np
import math

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import os
from pypdf import PdfReader
import re
from pathlib import Path

def get_pdf_title(pdf_path):
    """Get PDF title from metadata, fallback to content extraction if needed."""
    try:
        with open(pdf_path, 'rb') as file:
            pdf_reader = PdfReader(file)

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import requests
import os
from urllib.parse import urlparse

def convert_to_pdf_url(presentation_url):
    # Remove any trailing parameters or /edit
    base_url = presentation_url.split('/edit')[0].split('?')[0]
    # Add /export/pdf to the end
    return f"{base_url}/export/pdf"

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# git clone https://github.com/salesforce/BLIP
# cd BLIP
# touch BLIP_img2caption.py

import os
import json
from PIL import Image
import torch
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

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import pandas as pd
from twstock import Stock
import argparse


def parse():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--etf_code", type=str, default="00733",
    )

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from backtesting import Backtest, Strategy
from backtesting.lib import crossover

from FinMind.data import DataLoader
import pandas as pd

import talib
from talib import abstract

## 取得資料

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import whisper
import openai
import os

openai.api_key = "sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX" 

# input audio file name
audio_file = r"C:\Users\User\Desktop\input.mp3"

# load the model and transcribe the audio