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from pptx import Presentation
import re
import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import torch.nn.functional as F
from transformers import pipeline
# Load the pre-trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Ahmed235/roberta_classification")
model = AutoModelForSequenceClassification.from_pretrained("Ahmed235/roberta_classification")
# 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):
extracted_text = extract_text_from_pptx(file_path)
cleaned_text = re.sub(r'\s+', ' ', extracted_text)
# Tokenize and encode the cleaned text
input_encoding = tokenizer(cleaned_text, truncation=True, padding=True, return_tensors="pt")
# Perform inference
with torch.no_grad():
outputs = model(**input_encoding)
logits = outputs.logits
probabilities = F.softmax(logits, dim=1)
predicted_label_id = torch.argmax(logits, dim=1).item()
predicted_label = model.config.id2label[predicted_label_id]
predicted_probability = probabilities[0][predicted_label_id].item()
# Summarize the cleaned text
summary = summarizer(cleaned_text, max_length=80, min_length=30, do_sample=False)[0]['summary_text']
prediction = {
"Predicted Label": predicted_label,
"Evaluation": f"Evaluate the topic according to {predicted_label} is: {predicted_probability}",
"Summary": summary
}
return prediction
# Define the Gradio interface
iface = gr.Interface(
fn=predict_pptx_content,
inputs=gr.File(type="file", label="Upload PowerPoint (.pptx) file"),
outputs=["text", "text", "text"], # Predicted Label, Evaluation, Summary
live=False, # Change to True for one-time analysis
title="<h1 style='color: lightgreen; text-align: center;'>PPTX Analyzer</h1>",
)
# Deploy the Gradio interface
iface.launch(share=True)
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