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import gradio as gr
from pptx import Presentation
import re
from transformers import pipeline
# Create a summarization pipeline
summarizer = pipeline("summarization", model="Falconsai/text_summarization")
def extract_text_from_pptx(file_path):
presentation = Presentation(file_path)
text = []
for slide_number, slide in enumerate(presentation.slides, start=1):
for shape in slide.shapes:
if hasattr(shape, "text"):
text.append(shape.text)
return "\n".join(text)
def predict_pptx_content(file_path):
try:
extracted_text = extract_text_from_pptx(file_path)
cleaned_text = re.sub(r'\s+', ' ', extracted_text)
# Summarize the cleaned text
summary = summarizer(cleaned_text, max_length=80, min_length=30, do_sample=False)[0]['summary_text']
prediction = {
"Summary": summary
}
return prediction
except Exception as e:
# Log the error details
print(f"Error in predict_pptx_content: {e}")
return {"error": str(e)}
# Define the Gradio interface
iface = gr.Interface(
fn=predict_pptx_content,
inputs=gr.File(type="filepath", label="Upload PowerPoint (.pptx) file"),
outputs="text", # Only output the summary
live=False, # Change to True for one-time analysis
title="<h1 style='color: lightgreen; text-align: center;'>HackTalk Analyzer</h1>",
)
# Deploy the Gradio interface
iface.launch(share=True) |