app.py

import gradio as gr

from processing.slide_parser import parse_slides
from agent.planner import explain_slide
from utils.translator import translate
from speech.tts_engine import create_voice


def generate_lecture(ppt_file, language):

    slides = parse_slides(ppt_file)

    scripts = []
    audio_files = []

    total = len(slides)

    for i, slide in enumerate(slides):

        explanation = explain_slide(slide["text"])

        translated = translate(explanation, language)

        audio = create_voice(translated, language)

        scripts.append(
            f"Slide {slide['index']}:\n{translated}\n"
        )

        audio_files.append(audio)

        progress = int((i+1)/total*100)

    return audio_files, "\n".join(scripts), progress


with gr.Blocks() as demo:

    gr.Markdown("""
# 🎓 AI Agentic Slide Lecturer

Upload a PowerPoint presentation.

The AI will:
- Explain each slide
- Generate narration
- Support Tamil / English
""")

    ppt_input = gr.File(label="Upload PPTX")

    language = gr.Radio(
        ["English","Tamil"],
        value="English",
        label="Lecture Language"
    )

    run_btn = gr.Button("Generate Lecture")

    progress = gr.Slider(
        0,
        100,
        value=0,
        label="Progress"
    )

    audio_output = gr.Audio(
        label="Slide Narration"
    )

    script_output = gr.Textbox(
        label="Lecture Script",
        lines=15
    )

    run_btn.click(
        generate_lecture,
        inputs=[ppt_input, language],
        outputs=[audio_output, script_output, progress]
    )

demo.launch()