training: add local run script and generated local summaries

Include the new local training entrypoint and its latest output summaries so HF Space has the same local-run artifacts available for debugging and comparison.

Made-with: Cursor

plots/train_local_log.jsonl

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+{"day": 1, "task": "monthly_engage", "seed": 42, "prompt": "Day: Mon | days_elapsed=0\nEnergy: 1.00 | Followers: 10000\nEngagement: 0.000 | Queue: 0\nCOACH HINT: post 2-3 times today at hours 19:00, 20:00, 14:00. Set scheduled_actions[i].hour to one of these.\nPlan today's actions (JSON only):", "response": "{\n  \"tool_calls\": [],\n  \"scheduled_actions\": [\n    { \"hour\": 19, \"action_type\": \"post\", \"content_type\": \"reel\", \"topic\": \"Tech News Highlights\", \"tags\": [\"tech\", \"news\"], \"intent\": \"send_bait\" },\n    { \"hour\": 20, \"action_type\": \"post\", \"content_type\": \"story\", \"topic\": \"Daily Tech Tips\", \"tags\": [\"tech\", \"tips\"], \"intent\": \"watch_bait\" },\n    { \"hour\": 14, \"action_type\": \"post\", \"content_type\": \"text_post\", \"topic\": \"Tech Breakthroughs This Week\", \"tags\": [\"tech\", \"breakthroughs\"], \"intent\": \"like_bait\" }\n  ],\n  \"notes\": \"Scheduled posts at optimal engagement times to boost visibility and interaction.\"\n}"}

plots/train_local_summary.json

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+{
+  "config": {
+    "task_horizon": 1,
+    "num_episodes": 1,
+    "num_rounds": 1,
+    "model": "qwen2.5:3b-instruct-q4_K_M",
+    "task": "monthly_engage",
+    "temperature": 0.7
+  },
+  "results": [
+    {
+      "round": 1,
+      "ep": 1,
+      "task": "monthly_engage",
+      "seed": 42,
+      "grader_score": 1.0,
+      "total_reward": 0.244,
+      "rewards": [
+        0.244
+      ],
+      "final_energy": 1.0,
+      "follower_delta": 401
+    }
+  ],
+  "elapsed_seconds": 7.4
+}

training/train_local.py

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+"""Minimal local rollout: 1 episode × 1 day × 1 agent (Ollama).
+
+No HuggingFace download required — uses your local Ollama model.
+
+Usage:
+    cd viral-posts-env
+    .venv/bin/python training/train_local.py
+
+Override via env:
+    TASK_HORIZON=1                         # days per episode
+    NUM_EPISODES=1                         # episodes per round
+    NUM_ROUNDS=1                           # outer loop
+    OLLAMA_MODEL=qwen2.5:3b-instruct-q4_K_M
+    TASK=monthly_engage                    # or monthly_strategic / monthly_competitive
+"""
+from __future__ import annotations
+
+import json
+import os
+import sys
+import textwrap
+import time
+from pathlib import Path
+
+os.environ.setdefault("TASK_HORIZON", "1")
+
+REPO_ROOT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(REPO_ROOT))
+
+import httpx  # noqa: E402
+
+from models import ScheduledAction, ToolCall, ViraltestAction  # noqa: E402
+from server.viraltest_environment import (  # noqa: E402
+    TASK_HORIZON,
+    ViraltestEnvironment,
+    get_peak_hours,
+)
+
+NUM_EPISODES = int(os.environ.get("NUM_EPISODES", "1"))
+NUM_ROUNDS = int(os.environ.get("NUM_ROUNDS", "1"))
+OLLAMA_MODEL = os.environ.get("OLLAMA_MODEL", "qwen2.5:3b-instruct-q4_K_M")
+OLLAMA_URL = os.environ.get("OLLAMA_URL", "http://localhost:11434")
+TASK = os.environ.get("TASK", "monthly_engage")
+SEED = int(os.environ.get("SEED", "42"))
+TEMPERATURE = float(os.environ.get("TEMPERATURE", "0.7"))
+
+OUT_DIR = REPO_ROOT / "plots"
+OUT_DIR.mkdir(parents=True, exist_ok=True)
+LOG_PATH = OUT_DIR / "train_local_log.jsonl"
+
+print(f"[config] task_horizon={TASK_HORIZON} episodes={NUM_EPISODES} rounds={NUM_ROUNDS} "
+      f"task={TASK} model={OLLAMA_MODEL}")
+
+
+SYSTEM_PROMPT = textwrap.dedent("""\
+You are an Instagram content strategy agent. Each step is one day.
+
+RESPONSE FORMAT — return ONLY valid JSON, no markdown:
+{
+  "tool_calls": [{"name": "<tool>", "arguments": {...}}],
+  "scheduled_actions": [
+    {"hour": 0-23, "action_type": "post|create_content",
+     "content_type": "reel|story|carousel|text_post",
+     "topic": "<string>", "tags": ["..."],
+     "intent": "send_bait|save_bait|watch_bait|like_bait"}
+  ],
+  "notes": "strategy notes"
+}
+
+VALID TOOL ARGS:
+- niche: tech | lifestyle | fitness | business | food | travel | fashion | beauty | photography | education
+- segment_id: young_professionals | students | parents | global_night_owls | passive_scrollers
+- competitor_id: niche_expert | viral_chaser | lifestyle_blogger | b2b_thought_leader | food_creator | fitness_coach | travel_creator
+
+POSTING RULES:
+- Active day: 2-3 `post` actions at peak hours.
+- Vary `intent` and `content_type`.""")
+
+
+_DAY_NAMES = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
+
+
+def format_obs(obs, hint_hours: str | None = None) -> str:
+    day_name = _DAY_NAMES[obs.day_of_week] if 0 <= obs.day_of_week < 7 else "?"
+    sig = getattr(obs, "engagement_signals", None)
+    sig_str = ""
+    if sig:
+        sig_str = (f"Signals: watch={sig.watch_time:.3f} "
+                   f"sends={sig.sends_per_reach:.3f} saves={sig.saves:.3f}\n")
+    hint = ""
+    if hint_hours:
+        hint = (f"COACH HINT: post 2-3 times today at hours {hint_hours}. "
+                "Set scheduled_actions[i].hour to one of these.\n")
+    return (f"Day: {day_name} | days_elapsed={obs.days_elapsed}\n"
+            f"Energy: {obs.creator_energy:.2f} | Followers: {obs.follower_count}\n"
+            f"Engagement: {obs.engagement_rate:.3f} | Queue: {obs.content_queue_size}\n"
+            f"{sig_str}{hint}Plan today's actions (JSON only):")
+
+
+def parse_model_output(text: str) -> ViraltestAction:
+    text = text.strip()
+    if "```" in text:
+        text = "\n".join(l for l in text.split("\n") if not l.strip().startswith("```")).strip()
+    s, e = text.find("{"), text.rfind("}") + 1
+    if s >= 0 and e > s:
+        text = text[s:e]
+    try:
+        data = json.loads(text)
+    except Exception:
+        return ViraltestAction(scheduled_actions=[])
+    tool_calls = []
+    for tc in data.get("tool_calls", []):
+        if not isinstance(tc, dict) or "name" not in tc:
+            continue
+        args = tc.get("arguments", {})
+        if isinstance(args, list) and args and isinstance(args[0], dict):
+            args = args[0]
+        if isinstance(args, dict):
+            try:
+                tool_calls.append(ToolCall(name=tc["name"], arguments=args))
+            except Exception:
+                pass
+    scheduled = []
+    for a in data.get("scheduled_actions", []):
+        try:
+            scheduled.append(ScheduledAction(**a))
+        except Exception:
+            pass
+    return ViraltestAction(tool_calls=tool_calls, scheduled_actions=scheduled,
+                           notes=data.get("notes"))
+
+
+def ollama_generate(prompt: str, temperature: float = 0.7, num_predict: int = 384) -> str:
+    try:
+        resp = httpx.post(
+            f"{OLLAMA_URL}/api/generate",
+            json={
+                "model": OLLAMA_MODEL,
+                "prompt": prompt,
+                "system": SYSTEM_PROMPT,
+                "stream": False,
+                "options": {"temperature": temperature, "num_predict": num_predict},
+            },
+            timeout=120.0,
+        )
+        resp.raise_for_status()
+        return resp.json().get("response", "")
+    except Exception as e:
+        print(f"  [ollama-error] {type(e).__name__}: {e}")
+        return '{"scheduled_actions": []}'
+
+
+def run_one_episode(task: str, seed: int, log_fp) -> dict:
+    env = ViraltestEnvironment()
+    obs = env.reset(task=task, seed=seed)
+    rewards: list[float] = []
+    pairs: list[dict] = []
+    for day in range(1, TASK_HORIZON + 1):
+        if obs.done:
+            break
+        peak = get_peak_hours(obs.day_of_week, top_k=3)
+        hint = ", ".join(f"{h:02d}:00" for h in peak) if peak else None
+        prompt = format_obs(obs, hint_hours=hint)
+        t = time.time()
+        response = ollama_generate(prompt, temperature=TEMPERATURE)
+        gen_s = time.time() - t
+        action = parse_model_output(response)
+        log_fp.write(json.dumps({
+            "day": day, "task": task, "seed": seed,
+            "prompt": prompt, "response": response,
+        }) + "\n")
+        log_fp.flush()
+        obs = env.step(action)
+        r = obs.reward or 0.0
+        rewards.append(r)
+        n_posts = sum(1 for sa in action.scheduled_actions if sa.action_type == "post")
+        n_tools = len(action.tool_calls)
+        print(f"    day {day}: gen={gen_s:.1f}s posts={n_posts} tools={n_tools} "
+              f"reward={r:.4f} energy={obs.creator_energy:.2f}")
+        pairs.append({"prompt": prompt, "response": response, "reward": r})
+    grader = (obs.metadata or {}).get("grader_score", 0.0)
+    return {
+        "task": task, "seed": seed,
+        "grader_score": grader,
+        "total_reward": sum(rewards),
+        "rewards": rewards,
+        "final_energy": obs.creator_energy,
+        "follower_delta": obs.follower_count - 10000,
+        "pairs": pairs,
+    }
+
+
+def main() -> None:
+    t_start = time.time()
+    try:
+        info = httpx.get(f"{OLLAMA_URL}/api/tags", timeout=5).json()
+        names = [m["name"] for m in info.get("models", [])]
+        print(f"[ollama] reachable. models: {names}")
+        if OLLAMA_MODEL not in names:
+            print(f"  WARNING: {OLLAMA_MODEL} not in {names}. "
+                  f"Run: ollama pull {OLLAMA_MODEL}")
+    except Exception as e:
+        print(f"[ollama] NOT reachable at {OLLAMA_URL}: {e}\n  Start it with: ollama serve")
+        sys.exit(1)
+
+    LOG_PATH.write_text("")
+    log_fp = LOG_PATH.open("a")
+
+    all_results: list[dict] = []
+    for round_idx in range(NUM_ROUNDS):
+        print(f"\n[round] {round_idx + 1}/{NUM_ROUNDS}")
+        for ep in range(NUM_EPISODES):
+            seed = SEED + ep + round_idx * 100
+            print(f"  [episode] {ep + 1}/{NUM_EPISODES} task={TASK} seed={seed}")
+            t_ep = time.time()
+            result = run_one_episode(TASK, seed, log_fp)
+            all_results.append({"round": round_idx + 1, "ep": ep + 1, **result})
+            print(f"  -> grader={result['grader_score']:.4f} "
+                  f"reward={result['total_reward']:.3f} "
+                  f"energy={result['final_energy']:.2f} "
+                  f"({time.time() - t_ep:.1f}s)")
+
+    log_fp.close()
+
+    summary = {
+        "config": {
+            "task_horizon": TASK_HORIZON,
+            "num_episodes": NUM_EPISODES,
+            "num_rounds": NUM_ROUNDS,
+            "model": OLLAMA_MODEL,
+            "task": TASK,
+            "temperature": TEMPERATURE,
+        },
+        "results": [
+            {k: v for k, v in r.items() if k != "pairs"} for r in all_results
+        ],
+        "elapsed_seconds": round(time.time() - t_start, 1),
+    }
+    summary_path = OUT_DIR / "train_local_summary.json"
+    summary_path.write_text(json.dumps(summary, indent=2))
+    print(f"\n[summary] -> {summary_path}")
+    print(f"[log]     -> {LOG_PATH}")
+    print(f"[done] {time.time() - t_start:.1f}s total")
+    print("\nResults:")
+    for r in all_results:
+        print(f"  round={r['round']} ep={r['ep']} task={r['task']} "
+              f"grader={r['grader_score']:.4f} reward={r['total_reward']:.3f}")
+
+
+if __name__ == "__main__":
+    main()