# Install necessary libraries
libraries = [
    'transformers==4.12.2',
    'gradio',
    'python-pptx==0.6.19',
]

for library in libraries:
    try:
        subprocess.check_call(['pip', 'install', library])
    except subprocess.CalledProcessError as e:
        print(f"Error installing {library}: {e}")

from pptx import Presentation
import re
from transformers import pipeline
import subprocess
import gradio as gr

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)

    classifier = pipeline("text-classification", model="Ahmed235/roberta_classification")
    summarizer = pipeline("summarization", model="Falconsai/text_summarization")

    result = classifier(cleaned_text)[0]
    predicted_label = result['label']
    predicted_probability = result['score']

    prediction = {
        "Predicted Label": predicted_label,
        "Evaluation": f"Evaluate the topic according to {predicted_label} is: {predicted_probability}",
        "Summary": summarizer(cleaned_text, max_length=80, min_length=30, do_sample=False)
    }

    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 False for one-time analysis
    title="<h1 style='color: lightgreen; text-align: center;'>PPTX Analyzer</h1>",
)

# Deploy the Gradio interface
iface.launch(share=True)