File size: 1,993 Bytes
98c0f54 18332e8 2292b28 bbeaa3a 09ca2da 18332e8 ecd8fcb 09ca2da 2bb61b8 c4d5545 2bb61b8 12b0ed7 95d05cb 8cb1867 b246175 18332e8 98c0f54 18332e8 b246175 95d05cb 09ca2da 8b24c55 5833f42 95d05cb 367a8a1 95d05cb 12b0ed7 fcf7672 93595ae f4067be bcb2ab6 12b0ed7 2292b28 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
import gradio as gr
from transformers import pipeline
from pptx import Presentation # Import the Presentation class
import re
# Create a text classification pipeline
classifier = pipeline("text-classification", model="Ahmed235/roberta_classification", tokenizer="Ahmed235/roberta_classification")
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)
# Perform inference using the pipeline
result = classifier(extracted_text)
predicted_label = result[0]['label']
predicted_probability = result[0]['score']
summary = summarizer(extracted_text, max_length=80, min_length=30, do_sample=False)[0]['summary_text']
prediction = {
"Summary": summary,
"Evaluation": f"Evaluate the topic according to {predicted_label} is: {predicted_probability}",
"Predicted_Label": predicted_label, # Adjusted key to match Gradio output key
}
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"], # Adjusted output keys
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)
|