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