app.py

"""Gradio Space: YouTube topic -> captioned .docx tutorial (API-based acquisition).

Pipeline:
  search top videos -> rank by YouTube Data API comment sentiment -> transcript via
  youtube-transcript-api -> DeepSeek-V3 tutorial -> real screenshots via yt-dlp stream URL
  + ffmpeg -ss (weighted timestamps) -> VLM captions -> .docx.

No video download and no cookies/proxy/PO-token UI. A thin fallback remains via the
optional Space secrets YT_COOKIES / YT_PROXY (used only to resolve the stream URL and the
transcript when the Space's datacenter IP is blocked).
"""
from __future__ import annotations

import base64
import binascii
import os
import re
import shutil
import tempfile

import gradio as gr

from pipeline import (
    captions as captions_mod,
    docx_builder,
    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",
]


# --------------------------------------------------------------- thin access fallback
def _looks_like_netscape(text: str) -> bool:
    head = text.lstrip()
    return head.startswith("#") or "\tTRUE\t" in text or "\tFALSE\t" in text


def _maybe_b64_decode(text: str) -> str | None:
    compact = "".join(text.split())
    if len(compact) < 16 or re.search(r"[^A-Za-z0-9+/=]", compact):
        return None
    try:
        decoded = base64.b64decode(compact, validate=True).decode("utf-8", "replace")
    except (binascii.Error, ValueError):
        return None
    return decoded if _looks_like_netscape(decoded) else None


def _cookiefile(workdir: str) -> str | None:
    """Materialize the optional YT_COOKIES secret to a Netscape file; return path or None."""
    data = os.environ.get("YT_COOKIES")
    if not data or not data.strip():
        return None
    if not _looks_like_netscape(data):
        decoded = _maybe_b64_decode(data)
        if decoded:
            data = decoded
    if not data.lstrip().startswith(("# Netscape", "# HTTP")):
        data = "# Netscape HTTP Cookie File\n" + data.lstrip("\n")
    if not data.endswith("\n"):
        data += "\n"
    path = os.path.join(workdir, "cookies.txt")
    with open(path, "w", encoding="utf-8", newline="\n") as fh:
        fh.write(data)
    return path


def _resolve_proxy() -> str | None:
    proxy = os.environ.get("YT_PROXY", "").strip()
    return proxy or None


def _resolve_api_key(ui_key: str | None) -> str | None:
    key = (ui_key or "").strip() or os.environ.get("YOUTUBE_API_KEY", "").strip()
    return key or None


# ----------------------------------------------------------------------------- helpers
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.get('positive_share', 0) * 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 _collect_keywords(primary_kw, secondary_kw) -> dict:
    primary = (primary_kw or "").strip()
    secondary, seen = [], {primary.lower()}
    for part in (secondary_kw or "").split(","):
        kw = part.strip()
        if kw and kw.lower() not in seen:
            seen.add(kw.lower())
            secondary.append(kw)
    return {"primary": primary, "secondary": secondary}


def run_pipeline(topic, hf_token, yt_api_key, llm_model, vlm_model,
                 w_llm, w_whisper, lead, max_shots, primary_kw, secondary_kw,
                 progress=gr.Progress()):
    """Generator yielding (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")
    try:
        api_key = _resolve_api_key(yt_api_key)
        cookiefile = _cookiefile(workdir)
        proxy = _resolve_proxy()

        # 1. Search ------------------------------------------------------------------
        progress(0.03, 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 (YouTube Data API comments) ---------------------------
        if api_key:
            yield status("💬 Fetching comments (YouTube Data API) and scoring sentiment…"), gr.update(), gr.update(), gr.update()
            best, scored = sentiment_mod.rank_by_sentiment(videos, api_key, progress)
            picked_msg = f"({best['positive_share'] * 100:.0f}% positive comments)"
        else:
            best = videos[0]
            scored = [{**v, "positive_share": 0.0, "n_comments": 0,
                       "note": "sentiment skipped (no API key)", "search_rank": i}
                      for i, v in enumerate(videos)]
            picked_msg = "(no YouTube Data API key → used top search result)"
        ranking = gr.update(value=_ranking_rows(scored))
        yield (status(f"🏆 Picked **{best.get('title', best['video_id'])}** {picked_msg}."),
               ranking, gr.update(), gr.update())

        # 3. Transcript (youtube-transcript-api) -------------------------------------
        progress(0.3, desc="Transcript")
        yield status("📝 Fetching the timestamped transcript…"), ranking, gr.update(), gr.update()
        segs = transcribe_mod.get_segments(best["video_id"], proxy=proxy)
        transcript = transcribe_mod.transcript_text(segs)
        yield (status(f"Transcript ready ({len(segs)} segments)."),
               ranking, gr.update(value=transcript), gr.update())

        # 4. Resolve a stream URL for screenshots (best-effort) ----------------------
        progress(0.45, desc="Resolving stream")
        stream_url = None
        try:
            stream_url, _ = frames_mod.get_stream_url(best["video_id"], cookiefile, proxy)
        except Exception as exc:
            yield (status(f"⚠️ Couldn't resolve a video stream for screenshots "
                          f"({type(exc).__name__}) — producing a **text-only** tutorial. "
                          "Set YT_PROXY/YT_COOKIES secrets to enable screenshots."),
                   ranking, gr.update(value=transcript), gr.update())

        # 5. Tutorial text -----------------------------------------------------------
        progress(0.6, desc="Writing tutorial")
        keywords = _collect_keywords(primary_kw, secondary_kw)
        kw_note = f" • primary: '{keywords['primary']}'" if keywords["primary"] else ""
        if keywords["secondary"]:
            kw_note += f" • secondary: {', '.join(keywords['secondary'])}"
        yield status(f"🤖 Generating tutorial with `{llm_model}`{kw_note}…"), ranking, gr.update(value=transcript), gr.update()
        tut = tutorial_mod.generate_tutorial(transcript, hf_token.strip(), llm_model, keywords)
        if keywords["primary"]:
            n = tutorial_mod.count_keyword(tut, keywords["primary"])
            yield (status(f"🔑 Primary keyword '{keywords['primary']}' appears {n}× in the post."),
                   ranking, gr.update(value=transcript), gr.update())

        # 6. Screenshots (weighted timestamps -> ffmpeg grab) + captions -------------
        selected, caps = {}, {}
        if stream_url:
            progress(0.75, desc="Screenshots")
            times = frames_mod.compute_shot_times(
                tut["steps"], segs, w_llm=float(w_llm), w_whisper=float(w_whisper),
                lead=float(lead), max_shots=int(max_shots))
            yield status(f"🎞️ Capturing {len(times)} screenshots at weighted timestamps…"), ranking, gr.update(value=transcript), gr.update()
            selected = frames_mod.capture_shots(times, stream_url, frames_dir, proxy, progress)
            if selected:
                progress(0.88, desc="Captioning")
                yield status(f"✍️ Captioning {len(selected)} 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)

        # 7. DOCX --------------------------------------------------------------------
        progress(0.96, 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")
        shots_msg = f"{len(selected)} screenshots" if selected else "text-only"
        yield (status(f"✅ Done ({shots_msg}). Download your tutorial below."),
               ranking, gr.update(value=transcript), gr.update(value=out_path))

    except gr.Error:
        raise
    except (transcribe_mod.TranscriptError, sentiment_mod.SentimentError,
            RuntimeError, ValueError) as exc:
        raise gr.Error(str(exc))


def build_ui():
    with gr.Blocks(title="YouTube → Tutorial Post") as demo:
        gr.Markdown(
            "# 📝 YouTube → Tutorial Post Generator\n"
            "Enter a topic, your **Hugging Face token** (LLM + vision model, billed to you) "
            "and a **YouTube Data API key** (for comments). The Space picks the best video by "
            "comment sentiment, pulls its transcript, writes an AEO-friendly tutorial, grabs "
            "real screenshots at the right moments, and builds a **.docx**."
        )
        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)")
                yt_api_key = gr.Textbox(label="YouTube Data API key", type="password",
                                        placeholder="for comments (or set the YOUTUBE_API_KEY secret)")
            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("SEO / AEO keywords (optional)", open=False):
            gr.Markdown(
                "The **primary keyword** is used naturally ~3× in the body and placed in "
                "the title, URL slug, meta description, the first 100 words, and one or two "
                "H2 headings. Each **secondary keyword** is used once. The post also follows "
                "answer-engine best practices (direct answer up top, FAQ, last-updated date, "
                "source citation)."
            )
            primary_kw = gr.Textbox(label="Primary keyword", placeholder="e.g. excel pivot tables")
            secondary_kw = gr.Textbox(label="Secondary keywords (comma-separated)",
                                      placeholder="e.g. pivot chart, data summary")

        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: transcript timing")
                lead = gr.Slider(0.0, 5.0, value=1.0, step=0.5, label="Lead offset (s)")
            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)
        docx_file = gr.File(label="Download tutorial (.docx)")

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


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