{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viraltest v2 — TRL GRPO Training\n",
    "\n",
    "Train Qwen2.5-1.5B-Instruct on the Viraltest environment using Group Relative Policy Optimization.\n",
    "\n",
    "**Requirements:** Free Colab T4 GPU, ~30 min for 100 episodes.\n",
    "\n",
    "**Reward:** per-step env reward (0-1) + 2× terminal grader_score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q trl transformers accelerate peft bitsandbytes openai httpx matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import List, Dict, Any\n",
    "\n",
    "# Set your env server URL (run the Docker container or HF Space first)\n",
    "ENV_BASE_URL = os.getenv(\"ENV_BASE_URL\", \"http://localhost:8000\")\n",
    "MODEL_NAME = \"Qwen/Qwen2.5-1.5B-Instruct\"\n",
    "\n",
    "print(f\"Environment: {ENV_BASE_URL}\")\n",
    "print(f\"Model: {MODEL_NAME}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Episode Collection\n",
    "\n",
    "Run the agent against the environment and collect (prompt, response, reward) tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import httpx\n",
    "\n",
    "def reset_env(task: str = \"monthly_engage\") -> Dict[str, Any]:\n",
    "    resp = httpx.post(f\"{ENV_BASE_URL}/reset\", json={\"task\": task}, timeout=30)\n",
    "    return resp.json()\n",
    "\n",
    "def step_env(action: Dict[str, Any]) -> Dict[str, Any]:\n",
    "    resp = httpx.post(f\"{ENV_BASE_URL}/step\", json=action, timeout=30)\n",
    "    return resp.json()\n",
    "\n",
    "def collect_episode(task: str, max_steps: int = 30) -> List[Dict[str, Any]]:\n",
    "    \"\"\"Collect one episode of (obs, action, reward) tuples.\"\"\"\n",
    "    obs = reset_env(task)\n",
    "    trajectory = []\n",
    "    for step in range(max_steps):\n",
    "        obs_data = obs.get(\"observation\", {})\n",
    "        if obs.get(\"done\", False):\n",
    "            break\n",
    "        # Simple heuristic agent for data collection\n",
    "        action = {\n",
    "            \"scheduled_actions\": [\n",
    "                {\"hour\": 12, \"action_type\": \"post\", \"content_type\": \"carousel\",\n",
    "                 \"topic\": \"AI tools\", \"tags\": [\"ai\", \"coding\"], \"intent\": \"save_bait\"},\n",
    "            ],\n",
    "            \"notes\": f\"Step {step}: collecting training data.\"\n",
    "        }\n",
    "        obs = step_env(action)\n",
    "        reward = obs.get(\"reward\", 0.0)\n",
    "        trajectory.append({\"obs\": obs_data, \"action\": action, \"reward\": reward})\n",
    "    return trajectory\n",
    "\n",
    "# Collect baseline episodes\n",
    "print(\"Collecting baseline episodes...\")\n",
    "baseline_rewards = []\n",
    "for task in [\"monthly_engage\", \"monthly_strategic\", \"monthly_competitive\"]:\n",
    "    traj = collect_episode(task)\n",
    "    total_reward = sum(t[\"reward\"] for t in traj)\n",
    "    baseline_rewards.append(total_reward)\n",
    "    print(f\"  {task}: {total_reward:.4f} ({len(traj)} steps)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GRPO Training Loop\n",
    "\n",
    "Uses TRL's GRPOTrainer with the environment reward as the RL signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: Full GRPO training requires:\n",
    "# 1. Running the env server (docker or uvicorn)\n",
    "# 2. A reward function that maps env observations to scalar rewards\n",
    "# 3. Enough GPU memory for the model + optimizer\n",
    "#\n",
    "# This skeleton shows the structure. Adapt based on your compute.\n",
    "\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "# from trl import GRPOConfig, GRPOTrainer  # uncomment when running\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)\n",
    "# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True, torch_dtype=\"auto\")\n",
    "\n",
    "print(f\"Tokenizer loaded: {MODEL_NAME}\")\n",
    "print(\"To run full training, uncomment model loading and GRPOTrainer setup.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Reward Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Placeholder — replace with actual training rewards\n",
    "import numpy as np\n",
    "\n",
    "episodes = list(range(1, 201))\n",
    "# Simulated reward curve (replace with real data)\n",
    "rewards = np.cumsum(np.random.randn(200) * 0.02 + 0.01)\n",
    "rewards = np.clip(rewards, 0, 1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.plot(episodes, rewards, linewidth=1.5, color='#2196F3')\n",
    "ax.set_xlabel('Episode')\n",
    "ax.set_ylabel('Cumulative Reward')\n",
    "ax.set_title('Viraltest v2 — GRPO Training Reward Curve')\n",
    "ax.grid(True, alpha=0.3)\n",
    "fig.savefig('../plots/reward_curve.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved plots/reward_curve.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before vs After comparison\n",
    "tasks = ['monthly_engage', 'monthly_strategic', 'monthly_competitive']\n",
    "before_scores = [0.12, 0.10, 0.08]  # Replace with actual baseline\n",
    "after_scores = [0.45, 0.35, 0.28]   # Replace with actual trained\n",
    "\n",
    "x = np.arange(len(tasks))\n",
    "width = 0.35\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "bars1 = ax.bar(x - width/2, before_scores, width, label='Baseline', color='#FF9800')\n",
    "bars2 = ax.bar(x + width/2, after_scores, width, label='Trained (GRPO)', color='#4CAF50')\n",
    "\n",
    "ax.set_ylabel('Grader Score')\n",
    "ax.set_title('Before vs After Training — Grader Scores')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(tasks, rotation=15)\n",
    "ax.legend()\n",
    "ax.set_ylim(0, 0.8)\n",
    "ax.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "fig.savefig('../plots/before_after.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "print('Saved plots/before_after.png')"
   ]
  }
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