app.py

"""Gradio Space: YouTube topic -> captioned .docx tutorial.

Orchestrates the pipeline stages and streams progress/status to the UI. Heavy ML
imports (torch/transformers/faster-whisper) are lazy inside the pipeline modules, so app
startup stays fast.
"""
from __future__ import annotations

import os
import re
import shutil
import tempfile

import gradio as gr

from pipeline import (
    captions as captions_mod,
    docx_builder,
    download as download_mod,
    frames as frames_mod,
    search as search_mod,
    sentiment as sentiment_mod,
    transcribe as transcribe_mod,
    tutorial as tutorial_mod,
)

LLM_CHOICES = [
    "deepseek-ai/DeepSeek-V3",
    "meta-llama/Llama-3.3-70B-Instruct",
    "openai/gpt-oss-120b",
]
VLM_CHOICES = [
    "Qwen/Qwen2.5-VL-72B-Instruct",
    "Qwen/Qwen2.5-VL-7B-Instruct",
    "meta-llama/Llama-3.2-90B-Vision-Instruct",
]


def _cookiefile(workdir: str) -> str | None:
    """Materialize the YT_COOKIES secret (Netscape cookie file contents) to disk."""
    data = os.environ.get("YT_COOKIES")
    if not data:
        return None
    path = os.path.join(workdir, "cookies.txt")
    with open(path, "w", encoding="utf-8") as fh:
        fh.write(data)
    return path


def _ranking_rows(scored: list[dict]) -> list[list]:
    rows = []
    for rank, v in enumerate(scored, start=1):
        rows.append([
            rank,
            v.get("title", v["video_id"]),
            f"{v['positive_share'] * 100:.0f}%",
            v.get("n_comments", 0),
            v.get("note", "") or "ok",
            v["url"],
        ])
    return rows


def _safe_name(text: str) -> str:
    return re.sub(r"[^A-Za-z0-9._-]+", "_", text).strip("_")[:60] or "tutorial"


def run_pipeline(topic, hf_token, llm_model, vlm_model, w_llm, w_whisper, lead,
                 max_minutes, max_shots, progress=gr.Progress()):
    """Generator that yields (status_md, ranking_df, transcript, docx_file)."""
    log: list[str] = []

    def status(msg: str):
        log.append(msg)
        return "\n\n".join(log)

    topic = (topic or "").strip()
    if not topic:
        raise gr.Error("Please enter a topic.")
    if not (hf_token or "").strip():
        raise gr.Error("Please paste your Hugging Face token (used for the LLM + vision model).")

    workdir = tempfile.mkdtemp(prefix="ytt_")
    frames_dir = os.path.join(workdir, "frames")
    video_path = None
    try:
        cookiefile = _cookiefile(workdir)

        # 1. Search ------------------------------------------------------------------
        progress(0.02, desc="Searching")
        yield status(f"🔍 Searching top videos for **{topic}**…"), gr.update(), gr.update(), gr.update()
        videos = search_mod.search_top5(topic)
        yield status(f"Found {len(videos)} candidate videos."), gr.update(), gr.update(), gr.update()

        # 2. Sentiment ranking -------------------------------------------------------
        yield status("💬 Fetching comments and scoring sentiment…"), gr.update(), gr.update(), gr.update()
        best, scored = sentiment_mod.rank_by_sentiment(videos, cookiefile, progress)
        ranking = gr.update(value=_ranking_rows(scored))
        yield (status(f"🏆 Picked **{best.get('title', best['video_id'])}** "
                      f"({best['positive_share'] * 100:.0f}% positive)."),
               ranking, gr.update(), gr.update())

        # 3. Download + audio --------------------------------------------------------
        progress(0.25, desc="Downloading")
        yield status("⬇️ Downloading the chosen video…"), ranking, gr.update(), gr.update()
        video_path, duration = download_mod.download_video(
            best["url"], workdir, cookiefile, int(max_minutes))
        wav = download_mod.extract_audio(video_path, workdir)

        # 4. Transcribe --------------------------------------------------------------
        progress(0.4, desc="Transcribing")
        yield status("📝 Transcribing with Whisper (this is the slow part on CPU)…"), ranking, gr.update(), gr.update()
        segs = transcribe_mod.transcribe(wav, progress)
        transcript = transcribe_mod.transcript_text(segs)
        yield (status(f"Transcript ready ({len(segs)} segments)."),
               ranking, gr.update(value=transcript), gr.update())

        # 5. Candidate frames, then DELETE the video --------------------------------
        progress(0.6, desc="Extracting frames")
        candidates = frames_mod.extract_candidates(video_path, frames_dir, duration)
        frames_mod.delete_video(video_path)
        video_path = None
        yield (status(f"🎞️ Extracted {len(candidates)} candidate frames and "
                      f"**deleted the downloaded video**."),
               ranking, gr.update(value=transcript), gr.update())

        # 6. Tutorial text -----------------------------------------------------------
        progress(0.72, desc="Writing tutorial")
        yield status(f"🤖 Generating tutorial with `{llm_model}`…"), ranking, gr.update(value=transcript), gr.update()
        tut = tutorial_mod.generate_tutorial(transcript, hf_token.strip(), llm_model)

        # 7. Weighted screenshot selection ------------------------------------------
        selected = frames_mod.select_screenshots(
            tut["steps"], segs, candidates,
            w_llm=float(w_llm), w_whisper=float(w_whisper), lead=float(lead),
            max_shots=int(max_shots),
        )
        yield (status(f"🖼️ Selected {len(selected)} screenshots via the weighted indicator."),
               ranking, gr.update(value=transcript), gr.update())

        # 8. Captions ----------------------------------------------------------------
        progress(0.85, desc="Captioning")
        yield status(f"✍️ Captioning screenshots with `{vlm_model}`…"), ranking, gr.update(value=transcript), gr.update()
        caps = captions_mod.caption_frames(selected, tut["steps"], hf_token.strip(), vlm_model, progress)

        # 9. DOCX --------------------------------------------------------------------
        progress(0.95, desc="Building document")
        out_path = os.path.join(workdir, f"{_safe_name(tut['title'])}.docx")
        docx_builder.build_docx(tut, selected, caps, out_path, source_url=best["url"])

        progress(1.0, desc="Done")
        yield (status("✅ Done! Download your tutorial below."),
               ranking, gr.update(value=transcript), gr.update(value=out_path))

    except gr.Error:
        raise
    except (download_mod.DownloadError, RuntimeError, ValueError) as exc:
        raise gr.Error(str(exc))
    finally:
        # Always remove the video if it somehow survived; keep frames/docx until the
        # response is sent (Gradio copies the returned file out).
        if video_path:
            frames_mod.delete_video(video_path)


def build_ui():
    with gr.Blocks(title="YouTube → Tutorial Post") as demo:
        gr.Markdown(
            "# 📝 YouTube → Tutorial Post Generator\n"
            "Enter a topic and your Hugging Face token. The Space picks the best video, "
            "transcribes it, and builds a **captioned `.docx` tutorial**. Your token is "
            "used only for the LLM + vision-model calls and **billed to your account**."
        )
        with gr.Row():
            with gr.Column(scale=2):
                topic = gr.Textbox(label="Topic", placeholder="e.g. Excel pivot tables for beginners")
                hf_token = gr.Textbox(label="Hugging Face token", type="password",
                                      placeholder="hf_…  (Inference Providers permission)")
            with gr.Column(scale=1):
                llm_model = gr.Dropdown(LLM_CHOICES, value=LLM_CHOICES[0],
                                        label="Tutorial LLM", allow_custom_value=True)
                vlm_model = gr.Dropdown(VLM_CHOICES, value=VLM_CHOICES[0],
                                        label="Vision model (captions)", allow_custom_value=True)

        with gr.Accordion("Advanced settings", open=False):
            with gr.Row():
                w_llm = gr.Slider(0.0, 1.0, value=0.4, step=0.05, label="Weight: LLM timestamp")
                w_whisper = gr.Slider(0.0, 1.0, value=0.6, step=0.05, label="Weight: Whisper timing")
                lead = gr.Slider(0.0, 5.0, value=1.0, step=0.5, label="Lead offset (s)")
            with gr.Row():
                max_minutes = gr.Slider(2, 60, value=20, step=1, label="Max video length (min)")
                max_shots = gr.Slider(1, 15, value=8, step=1, label="Max screenshots")

        run_btn = gr.Button("Generate tutorial", variant="primary")

        status_md = gr.Markdown(label="Status")
        ranking_df = gr.Dataframe(
            headers=["#", "Title", "Positive", "Comments", "Note", "URL"],
            label="Sentiment ranking", interactive=False, wrap=True,
        )
        transcript_box = gr.Textbox(label="Transcript preview", lines=10, max_lines=20, show_copy_button=True)
        docx_file = gr.File(label="Download tutorial (.docx)")

        run_btn.click(
            run_pipeline,
            inputs=[topic, hf_token, llm_model, vlm_model, w_llm, w_whisper, lead, max_minutes, max_shots],
            outputs=[status_md, ranking_df, transcript_box, docx_file],
        )
    return demo


if __name__ == "__main__":
    build_ui().queue().launch()