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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from pptx import Presentation
from pptx.util import Inches
import subprocess
import os
# Content Generation Function
def generate_content(prompt):
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
inputs = tokenizer.encode(prompt + tokenizer.eos_token, return_tensors='pt')
outputs = model.generate(inputs, max_length=100, do_sample=True)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return text
# Slide Design Function
def create_presentation(content_dict, output_file):
prs = Presentation()
# Create slides based on content_dict
# ...
prs.save(output_file)
# Output Conversion Function
def convert_to_pdf(pptx_file, pdf_file):
subprocess.run(['soffice', '--headless', '--convert-to', 'pdf', pptx_file, '--outdir', os.path.dirname(pdf_file)])
# Main Function
def main(title, subtitle, num_slides, slide_prompts):
slides = []
for prompt in slide_prompts:
content = generate_content(prompt)
slides.append({'title': content, 'content': content})
content_dict = {
'title': title,
'subtitle': subtitle,
'slides': slides
}
pptx_file = "output.pptx"
create_presentation(content_dict, pptx_file)
pdf_file = "output.pdf"
convert_to_pdf(pptx_file, pdf_file)
return pptx_file, pdf_file
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# Presentation Generator")
title = gr.Textbox(label="Presentation Title")
subtitle = gr.Textbox(label="Subtitle")
num_slides = gr.Number(label="Number of Slides", value=1)
slide_prompts = gr.Textbox(label="Slide Prompts (one per line)", lines=5)
generate_button = gr.Button("Generate Presentation")
output_pptx = gr.File(label="Download PPTX")
output_pdf = gr.File(label="Download PDF")
generate_button.click(
main,
inputs=[title, subtitle, num_slides, slide_prompts],
outputs=[output_pptx, output_pdf]
)
demo.launch() |