feat: sentiment-aware video pipeline with DeepSeek, metadata generation, and per-video folder structure
SENTIMENT DETECTION (utils/sentiment.py)
- Integrate DeepSeek API using OpenAI-compatible SDK to classify each Reddit post
- Detect sentiment from post title + body (first 500 chars) into 8 labels:
sad, happy, angry, mysterious, funny, dramatic, wholesome, scary
- Override in-memory config per post (background_video, background_audio, voice)
- Falls back to 'dramatic' label if DeepSeek API fails or is unavailable
- Can be enabled/disabled via config.toml [deepseek] enabled = true/false
SENTIMENT MAPS (utils/sentiment_map.py)
- BACKGROUND_MAP: maps each sentiment to optimal background video + audio pair
- OPENAI_VOICE_MAP: maps each sentiment to best-fit OpenAI TTS voice
- ELEVENLABS_VOICE_MAP: maps each sentiment to best-fit ElevenLabs voice
(fully mapped to real voices: Adam, George, Harry, Callum, Jessica, Brian, Laura, Matilda)
- All overrides are in-memory only — config.toml is never modified
METADATA GENERATION (utils/sentiment.py)
- Single DeepSeek API call generates both sentiment + social media metadata
- Generates per-platform content:
* YouTube: title (max 70 chars) + full description
* TikTok: caption (max 150 chars) with hashtags
* Instagram: caption with hashtags
* Facebook: caption
* Hashtags: list of relevant tags
- Falls back to basic title-based metadata if DeepSeek fails
- Saves metadata.json inside each video's output folder
RESULTS FOLDER RESTRUCTURE (video_creation/final_video.py)
- Changed output structure from results/{subreddit}/{filename}.mp4
- New structure: results/{actual_subreddit}/{thread_id}_{sentiment}/video.mp4
- Each video now has its own isolated folder containing:
* video.mp4
* metadata.json
* thumbnail.png (if thumbnail generation is enabled)
* OnlyTTS/video.mp4 (if enable_extra_audio is enabled)
SUBREDDIT TRACKING (reddit/subreddit.py)
- Added thread_subreddit field to reddit_object using submission.subreddit.display_name
- Posts from r/AmItheAsshole now save to results/AmItheAsshole/
- Posts from r/tifu now save to results/tifu/
- Posts from r/confession now save to results/confession/
- Previously all posts were grouped under the combined subreddit string
PIPELINE INTEGRATION (main.py)
- Added apply_sentiment_config() call between post fetching and video generation
- Sentiment detection runs before TTS and background selection
- Controlled by settings.config['deepseek']['enabled'] flag
CONFIG CHANGES (config.toml + utils/.config.template.toml)
- Added [deepseek] section with api_key and enabled fields
- elevenlabs_voice_name changed from optional=false to optional=true
- Prevents prompt appearing when ElevenLabs is not the selected TTS provider
pull/2557/head
Abdessamad Haddouche
4 weeks ago
parent
7c679b8136
commit
af0940045c
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import json
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import os
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from openai import OpenAI
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from utils import settings
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from utils.console import print_step, print_substep
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from utils.sentiment_map import (
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BACKGROUND_MAP,
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OPENAI_VOICE_MAP,
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ELEVENLABS_VOICE_MAP,
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VALID_SENTIMENTS,
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DEFAULT_SENTIMENT,
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)
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def _get_client() -> OpenAI:
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api_key = settings.config["deepseek"]["api_key"]
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return OpenAI(
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api_key=api_key,
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base_url="https://api.deepseek.com",
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)
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def _extract_text(reddit_object: dict) -> tuple:
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title = reddit_object.get("thread_title", "")
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post = reddit_object.get("thread_post", "")
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if isinstance(post, list):
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post = " ".join([p.get("text", "") for p in post if isinstance(p, dict)])
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return title, post
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def detect_sentiment(reddit_object: dict) -> str:
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"""
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Sends the post title + body to DeepSeek and returns a sentiment label.
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Falls back to DEFAULT_SENTIMENT on any error.
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"""
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try:
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api_key = settings.config["deepseek"]["api_key"]
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if not api_key:
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print_substep("No DeepSeek API key found. Using default sentiment.", style="yellow")
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return DEFAULT_SENTIMENT
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title, post = _extract_text(reddit_object)
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text = f"Title: {title}\nPost: {post[:500]}"
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client = _get_client()
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response = client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{
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"role": "system",
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"content": (
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"You are a sentiment classifier for Reddit stories. "
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"Classify the post into exactly one of these labels: "
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"sad, happy, angry, mysterious, funny, dramatic, wholesome, scary. "
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"Respond with only the label, nothing else. No punctuation, no explanation."
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),
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},
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{
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"role": "user",
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"content": text,
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},
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],
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max_tokens=10,
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temperature=0,
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)
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label = response.choices[0].message.content.strip().lower()
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if label not in VALID_SENTIMENTS:
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print_substep(
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f"DeepSeek returned unexpected label '{label}'. Using default: {DEFAULT_SENTIMENT}",
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style="yellow",
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)
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return DEFAULT_SENTIMENT
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return label
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except Exception as e:
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print_substep(f"Sentiment detection failed: {e}. Using default: {DEFAULT_SENTIMENT}", style="yellow")
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return DEFAULT_SENTIMENT
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def generate_metadata(reddit_object: dict, sentiment: str) -> dict:
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"""
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Generates YouTube title, description, TikTok/Instagram/Facebook captions,
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and hashtags in a single DeepSeek call.
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Saves output as JSON next to the video in results/.
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Falls back to basic metadata on any error.
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"""
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try:
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api_key = settings.config["deepseek"]["api_key"]
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if not api_key:
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return _fallback_metadata(reddit_object, sentiment)
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title, post = _extract_text(reddit_object)
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text = f"Title: {title}\nPost: {post[:800]}"
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channel_name = settings.config["settings"].get("channel_name", "Reddit Tales")
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client = _get_client()
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response = client.chat.completions.create(
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model="deepseek-chat",
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messages=[
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{
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"role": "system",
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"content": (
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"You are a social media content creator specializing in Reddit story videos. "
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"Generate engaging titles, captions, and hashtags for a Reddit story video. "
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"Return ONLY a valid JSON object with these exact keys: "
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"youtube_title, youtube_description, tiktok_caption, instagram_caption, facebook_caption, hashtags. "
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"hashtags must be a list of strings. "
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"Keep youtube_title under 70 characters. "
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"Keep tiktok_caption under 150 characters including hashtags. "
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"Make content engaging and click-worthy. "
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f"The channel name is '{channel_name}'. "
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f"The story mood is: {sentiment}. "
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"No markdown, no explanation, just the JSON object."
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),
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},
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{
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"role": "user",
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"content": text,
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},
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],
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max_tokens=600,
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temperature=0.7,
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)
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raw = response.choices[0].message.content.strip()
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# Strip markdown code blocks if present
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if raw.startswith("```"):
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raw = raw.split("```")[1]
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if raw.startswith("json"):
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raw = raw[4:]
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raw = raw.strip()
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metadata = json.loads(raw)
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# Validate all required keys exist
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required_keys = [
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"youtube_title", "youtube_description",
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"tiktok_caption", "instagram_caption",
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"facebook_caption", "hashtags"
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]
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for key in required_keys:
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if key not in metadata:
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metadata[key] = ""
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metadata["sentiment"] = sentiment
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return metadata
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except Exception as e:
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print_substep(f"Metadata generation failed: {e}. Using fallback.", style="yellow")
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return _fallback_metadata(reddit_object, sentiment)
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def _fallback_metadata(reddit_object: dict, sentiment: str) -> dict:
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"""Basic fallback metadata if DeepSeek fails."""
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title = reddit_object.get("thread_title", "Reddit Story")
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return {
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"sentiment": sentiment,
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"youtube_title": title[:70],
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"youtube_description": f"{title}\n\n#reddit #stories",
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"tiktok_caption": f"{title[:100]} #reddit #storytime",
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"instagram_caption": f"{title[:100]} #reddit #stories",
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"facebook_caption": title,
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"hashtags": ["#reddit", "#storytime", "#stories", "#redditstories"],
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}
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def save_metadata(metadata: dict, reddit_object: dict) -> None:
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"""Saves metadata JSON inside the per-video folder."""
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try:
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subreddit = reddit_object.get("thread_subreddit", settings.config["reddit"]["thread"]["subreddit"])
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thread_id = reddit_object.get("thread_id", "unknown")
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sentiment_bg = settings.config["settings"]["background"].get("background_video", "unknown")
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video_folder = f"results/{subreddit}/{thread_id}_{sentiment_bg}"
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os.makedirs(video_folder, exist_ok=True)
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filepath = f"{video_folder}/metadata.json"
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with open(filepath, "w", encoding="utf-8") as f:
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json.dump(metadata, f, indent=2, ensure_ascii=False)
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print_substep(f"Metadata saved → {filepath}", style="bold green")
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except Exception as e:
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print_substep(f"Failed to save metadata: {e}", style="yellow")
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def apply_sentiment_config(reddit_object: dict) -> None:
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"""
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Detects sentiment, overrides in-memory config for background/voice,
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generates metadata, and saves it to disk.
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Does NOT write to config.toml — changes are per-run only.
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"""
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print_step("Detecting sentiment and generating metadata... 🎭")
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sentiment = detect_sentiment(reddit_object)
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# ── Background ───────────────────────────────────────────
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bg_video, bg_audio = BACKGROUND_MAP[sentiment]
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settings.config["settings"]["background"]["background_video"] = bg_video
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settings.config["settings"]["background"]["background_audio"] = bg_audio
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# ── Voice ────────────────────────────────────────────────
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voice_choice = settings.config["settings"]["tts"]["voice_choice"].lower()
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if voice_choice == "elevenlabs":
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voice = ELEVENLABS_VOICE_MAP[sentiment]
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settings.config["settings"]["tts"]["elevenlabs_voice_name"] = voice
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elif voice_choice == "openai":
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voice = OPENAI_VOICE_MAP[sentiment]
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settings.config["settings"]["tts"]["openai_voice_name"] = voice
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else:
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voice = f"(voice override not supported for {voice_choice})"
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# ── Metadata ─────────────────────────────────────────────
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print_substep("Generating titles, captions and hashtags... ✍️", style="bold blue")
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metadata = generate_metadata(reddit_object, sentiment)
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save_metadata(metadata, reddit_object)
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# ── Log ──────────────────────────────────────────────────
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print_substep(f"Sentiment detected : {sentiment} 🎯", style="bold green")
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print_substep(f"Background video : {bg_video}", style="bold blue")
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print_substep(f"Background audio : {bg_audio if bg_audio else 'none'}", style="bold blue")
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print_substep(f"Voice : {voice}", style="bold blue")
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print_substep(f"YouTube title : {metadata['youtube_title']}", style="bold blue")
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print_substep(f"TikTok caption : {metadata['tiktok_caption']}", style="bold blue")
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# Maps sentiment → (background_video, background_audio)
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BACKGROUND_MAP = {
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"sad": ("minecraft", "lofi"), # slow, melancholic
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"happy": ("fall-guys", "chill-summer"),# upbeat, fun
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"angry": ("gta", "lofi"), # lofi keeps intensity without distraction
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"mysterious": ("csgo-surf", "lofi-2"), # lofi-2 is more atmospheric
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"funny": ("cluster-truck", "chill-summer"),# light and playful
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"dramatic": ("rocket-league", "lofi"), # lofi under dramatic = tension
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"wholesome": ("steep", "chill-summer"),# warm and positive
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"scary": ("minecraft-2", "lofi-2"), # lofi-2 is darker/moodier
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}
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# Maps sentiment → OpenAI voice name
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OPENAI_VOICE_MAP = {
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"sad": "nova",
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"happy": "shimmer",
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"angry": "onyx",
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"mysterious": "echo",
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"funny": "fable",
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"dramatic": "alloy",
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"wholesome": "nova",
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"scary": "onyx",
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}
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# Maps sentiment → ElevenLabs voice name
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ELEVENLABS_VOICE_MAP = {
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"sad": "Brian - Deep, Resonant and Comforting",
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"happy": "Jessica - Playful, Bright, Warm",
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"angry": "Adam - Dominant, Firm",
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"mysterious": "Callum - Husky Trickster",
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"funny": "Laura - Enthusiast, Quirky Attitude",
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"dramatic": "George - Warm, Captivating Storyteller",
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"wholesome": "Matilda - Knowledgable, Professional",
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"scary": "Harry - Fierce Warrior",
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}
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# All valid sentiment labels
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VALID_SENTIMENTS = list(BACKGROUND_MAP.keys())
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# Fallback if detection fails — maps to rocket-league + lofi + alloy
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DEFAULT_SENTIMENT = "dramatic"
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