Reinforcement Learning
ml-agents
TensorBoard
ONNX
Pyramids
deep-reinforcement-learning
ML-Agents-Pyramids
Instructions to use apple9855/ppo-Pyramids with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ml-agents
How to use apple9855/ppo-Pyramids with ml-agents:
mlagents-load-from-hf --repo-id="apple9855/ppo-Pyramids" --local-dir="./downloads"
- Notebooks
- Google Colab
- Kaggle
Download app.py from apple9855/ppo-Pyramids: direct link, hf CLI and curl.
- Browser
- Download file 4.32 kB
-
https://huggingface.co/apple9855/ppo-Pyramids/resolve/refs%2Fpr%2F1/app.py
- Command line
-
hf download hf://apple9855/ppo-Pyramids@refs/pr/1/app.py
-
curl -L -o app.py https://huggingface.co/apple9855/ppo-Pyramids/resolve/refs%2Fpr%2F1/app.py
4.32 kB
| import gradio as gr | |
| import os, requests, json, subprocess | |
| import boto3 | |
| from groq import Groq | |
| import google.generativeai as genai | |
| # SECRETS HF - Settings > Variables and Secrets | |
| GROQ_KEY = os.getenv("GROQ_API_KEY") | |
| GEMINI_KEY = os.getenv("GEMINI_API_KEY") | |
| R2_ACCESS = os.getenv("R2_ACCESS") | |
| R2_SECRET = os.getenv("R2_SECRET") | |
| R2_BUCKET = os.getenv("R2_BUCKET") | |
| R2_BUCKET_URL = os.getenv("R2_BUCKET_URL") # https://xxx.r2.cloudflarestorage.com | |
| R2_PUBLIC = os.getenv("R2_PUBLIC_URL") # https://pub-xxx.r2.dev | |
| LWS_WEBHOOK = "https://opus.dostodgroup.com/webhook.php?token=change_moi_12345_opus" | |
| genai.configure(api_key=GEMINI_KEY) | |
| groq_client = Groq(api_key=GROQ_KEY) | |
| def upload_r2(local_path, remote_name): | |
| s3 = boto3.client('s3', | |
| endpoint_url=R2_BUCKET_URL, | |
| aws_access_key_id=R2_ACCESS, | |
| aws_secret_access_key=R2_SECRET | |
| ) | |
| s3.upload_file(local_path, R2_BUCKET, remote_name) | |
| return f"{R2_PUBLIC}/{remote_name}" | |
| def factory(job_id, video_url): | |
| try: | |
| tmp_in = f"/tmp/{job_id}.mp4" | |
| gr.Info(f"Download {video_url}...") | |
| with requests.get(video_url, stream=True, timeout=120) as r: | |
| r.raise_for_status() | |
| with open(tmp_in, 'wb') as f: | |
| for chunk in r.iter_content(chunk_size=1024*1024): | |
| if chunk: f.write(chunk) | |
| gr.Info("Transcription Whisper...") | |
| from faster_whisper import WhisperModel | |
| model = WhisperModel("small", device="cpu", compute_type="int8") | |
| segments, _ = model.transcribe(tmp_in, language="fr") | |
| full_text = " ".join([s.text for s in segments])[:8000] | |
| if not full_text.strip(): | |
| return "❌ Transcription vide" | |
| gr.Info("Analyse virale Groq...") | |
| prompt = f"""Tu es Opus.pro. Trouve 10 clips viraux 30-60s max score viral. | |
| Format JSON STRICT sans texte autour, juste le tableau: [{{"start":12.5,"end":45.2,"title":"Hook choc","score":92}}] | |
| Transcription: {full_text}""" | |
| comp = groq_client.chat.completions.create( | |
| model="llama-3.3-70b-versatile", | |
| messages=[{"role":"user","content":prompt}], | |
| temperature=0.7 | |
| ) | |
| txt = comp.choices[0].message.content.replace("```json","").replace("```","").strip() | |
| clips = json.loads(txt) | |
| final_for_lws = [] | |
| gr.Info(f"{len(clips)} clips detectes, rendu 1080p...") | |
| for i, c in enumerate(clips[:10]): | |
| out = f"/tmp/{job_id}_clip{i+1}_1080p.mp4" | |
| cmd = [ | |
| "ffmpeg","-ss",str(c['start']),"-to",str(c['end']),"-i",tmp_in, | |
| "-vf","crop=1080:1920:(in_w-1080)/2:0,scale=1080:1920:flags=lanczos", | |
| "-c:v","libx264","-crf","18","-preset","ultrafast","-c:a","aac","-b:a","128k","-y",out | |
| ] | |
| subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) | |
| if os.path.exists(out) and os.path.getsize(out) > 10000: | |
| remote = f"finales/{job_id}_clip{i+1}_1080p.mp4" | |
| r2_url = upload_r2(out, remote) | |
| final_for_lws.append({ | |
| "url": r2_url, | |
| "title": c.get('title',''), | |
| "score": c.get('score',0) | |
| }) | |
| # Envoie à LWS | |
| gr.Info(f"Envoi {len(final_for_lws)} clips vers LWS...") | |
| requests.post(LWS_WEBHOOK, json={"job_id": job_id, "clips": final_for_lws}, timeout=60) | |
| return f"✅ {len(final_for_lws)} clips 1080p envoyés sur opus.dostodgroup.com/videos/finales/\n\n" + "\n".join([f"{x['score']} - {x['title']} -> {x['url']}" for x in final_for_lws]) | |
| except Exception as e: | |
| import traceback | |
| return f"❌ Erreur: {str(e)}\n{traceback.format_exc()}" | |
| with gr.Blocks(title="OPUS 1080p Factory") as demo: | |
| gr.Markdown("# 🏭 OPUS 1080p - Usine Gratuite ZeroGPU\nR2 -> 10 clips viraux 1080p -> LWS opus.dostodgroup.com") | |
| job_id = gr.Textbox(label="job_id (ex: job_123)", value="job_test_1") | |
| video_url = gr.Textbox(label="URL video longue R2 (https://pub-.../video.mp4)", placeholder="https://pub-xxx.r2.dev/uploads/...") | |
| btn = gr.Button("🚀 RENDRE 10 CLIPS 1080p", variant="primary") | |
| output = gr.Textbox(label="Logs", lines=15) | |
| btn.click(factory, [job_id, video_url], output) | |
| demo.launch() |