app.py

import gradio as gr
from faster_whisper import WhisperModel
from docx import Document
import tempfile
import os

# Load model once at startup (cached after first run)
print("Loading Whisper model...")
model = WhisperModel("medium", compute_type="int8")
print("Model ready!")


def build_paragraphs(segments, pause_threshold=1.5):
    """
    Group Whisper segments into paragraphs.
    A new paragraph starts whenever the gap between the end of one segment
    and the start of the next exceeds `pause_threshold` seconds.
    """
    paragraphs = []
    current_sentences = []
    prev_end = None

    for seg in segments:
        if prev_end is not None and (seg.start - prev_end) >= pause_threshold:
            # Long pause → flush current paragraph
            if current_sentences:
                paragraphs.append(" ".join(current_sentences).strip())
                current_sentences = []
        current_sentences.append(seg.text.strip())
        prev_end = seg.end

    # Flush remaining
    if current_sentences:
        paragraphs.append(" ".join(current_sentences).strip())

    return paragraphs


def transcribe_audio(audio_file, output_format):
    """Transcribe an uploaded audio file and return a downloadable file + preview text."""
    if audio_file is None:
        return None, "⚠️ Please upload an audio file."

    # Transcribe — consume iterator once
    segments, _ = model.transcribe(audio_file)
    paragraphs = build_paragraphs(list(segments))

    base_name = os.path.splitext(os.path.basename(audio_file))[0]

    if output_format == "Word (.docx)":
        # ── Build Word document ───────────────────────────────────────────────
        doc = Document()
        doc.add_heading("Lecture Transcript", level=1)
        for para in paragraphs:
            doc.add_paragraph(para)

        tmp = tempfile.NamedTemporaryFile(
            delete=False,
            suffix=".docx",
            prefix=f"{base_name}_transcript_",
        )
        tmp.close()
        doc.save(tmp.name)
        preview = "\n\n".join(paragraphs)
        return tmp.name, preview

    else:
        # ── Plain text ────────────────────────────────────────────────────────
        full_text = "\n\n".join(paragraphs)
        tmp = tempfile.NamedTemporaryFile(
            delete=False,
            suffix=".txt",
            prefix=f"{base_name}_transcript_",
            mode="w",
            encoding="utf-8",
        )
        tmp.write(full_text)
        tmp.close()
        return tmp.name, full_text


# ── Gradio UI ─────────────────────────────────────────────────────────────────
with gr.Blocks(title="🎙️ Lecture Transcriber") as demo:

    gr.HTML("""
        <div style="text-align:center; margin-bottom: 4px;"><h1>🎙️ Lecture Transcriber</h1></div>
        <div style="text-align:center; color:#888; margin-bottom:24px; font-size:0.95em;">
            Upload an <b>.mp3</b> or <b>.m4a</b> audio file and get a formatted transcript instantly.
        </div>
    """)

    with gr.Row():
        with gr.Column(scale=1):
            audio_input = gr.Audio(
                label="Upload Audio File",
                type="filepath",
                sources=["upload"],
            )
            format_radio = gr.Radio(
                choices=["Plain Text (.txt)", "Word (.docx)"],
                value="Plain Text (.txt)",
                label="Output Format",
            )
            transcribe_btn = gr.Button("✨ Transcribe", variant="primary", size="lg")

        with gr.Column(scale=1):
            text_output = gr.Textbox(
                label="Transcript Preview",
                placeholder="Your transcript will appear here, split into paragraphs by natural pauses...",
                lines=16,
            )
            file_output = gr.File(label="⬇️ Download Transcript")

    transcribe_btn.click(
        fn=transcribe_audio,
        inputs=[audio_input, format_radio],
        outputs=[file_output, text_output],
        show_progress="full",
    )

    gr.HTML("""
        <div style="text-align:center; margin-top:16px; color:#aaa; font-size:0.85em;">
            Powered by <a href="https://github.com/SYSTRAN/faster-whisper" target="_blank">faster-whisper</a>
            · Paragraphs split by natural pauses · Runs 100% locally, no data stored.
        </div>
    """)

demo.launch(
    theme=gr.themes.Soft(primary_hue="violet"),
    css="""
    footer { display: none !important; }
    """,
)