app.py

from gradio_client import Client
import gradio as gr
import os
import random

def check_password(username, password):
    if password == os.environ["ACCESS"]:
        return True
    else:
        return False


def func(file, number_of_pages, secret):
    if secret != os.environ["ACCESS"]:
        return "Wrong password, please try again"
    text = client.predict(file, number_of_pages)
    # generate a random sequence of numbers
    # s = "".join([str(random.randint(0, 9)) for i in range(10)])
    # with open(f"{s}.tex", "w", encoding="utf-8") as f:
    #     f.write(text)
    return text

def upload_file(file):
    return file.name

description = r"""
<h3> SCIDECK is a tool that allows you to convert your PDF files into a presentation deck.</h3>
<br>

❗️❗️❗️[<b>Important</b>] Instructions:<br>
1️⃣ <b>Upload the PDF document</b>: Select the PDF file you want to convert into slides.<br>
2️⃣ <b>Specify the number of pages</b>: Indicate the range of pages you'd like to include in the slide generation. <b>Set it to 0</b> if you want to include all pages. <br>
3️⃣ <b>Enter the password provided in the invite email.</b><br>
4️⃣ <b>Click the Generate button</b>: Initiate the slide generation process by clicking the designated "Generate" button.<br>
5️⃣ <b>Be patient 🙂</b>: Generating the slides could take between 1 minute and 5 minutes.<br>

 
📝 If you get an error message, you can send me email with the PDF file attached to this email address: <b>nkoisheke [at] ethz [dot] ch</b>, and I will generate the slides for you. If there are any other issues or questions, please do not hesitate to contact me 🤗 <br>

🖼️ Some examples of slides generated using SCIDECK are shown below: <br>
1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift [[Paper]](https://arxiv.org/pdf/1502.03167.pdf) [[Slides]](https://drive.google.com/file/d/1Zt5FFH0nKxut-LyEr9pNAIdtgR_lBtIj/view?usp=sharing) <br>
2. Attention Is All You Need [[Paper]](https://arxiv.org/pdf/1706.03762.pdf) [[Slides]](https://drive.google.com/file/d/1xKgohh_QKV9pD_XjDuXR566h0VJ1S7WI/view?usp=sharing) <br>
3. Denoising Diffusion Probabilistic Models [[Paper]](https://arxiv.org/pdf/2006.11239.pdf) [[Slides]](https://drive.google.com/file/d/1D2ZfoJpHR3kP0JdsYyjxUq-vjVMV-KTO/view?usp=sharing) <br>


ver 0.1
"""

read_key = os.environ.get("HF_TOKEN", None)

if __name__ == "__main__":
    client = Client.duplicate("Nauryzbay/deckify_private", hf_token=read_key)

    temp = "<h1> SCIDECK: Generate slides (LaTeX Beamer) from PDF</h1>"
    with gr.Blocks() as demo:
        gr.Markdown(temp)
        gr.Image("demo.png", width=600, show_download_button=False, show_label=False)
        gr.Markdown(description)
        
        file_output = gr.File()
        upload_button = gr.UploadButton("Click to Upload a PDF File", file_types=["file"], file_count="single", size="sm")
        upload_button.upload(upload_file, upload_button, file_output)

        number_of_pages = gr.Number(label="Number of pages")
        secret = gr.Textbox(label="Password", type="password")
        output = gr.Textbox(label="Output")
        greet_btn = gr.Button("Generate slides")
        greet_btn.click(fn=func, inputs=[upload_button, number_of_pages, secret], outputs=output, api_name="greet")
    
    demo.queue(max_size=5)
    demo.launch()