main.py (12026B)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 | import base64 import os import uvicorn import numpy as np import torch from io import BytesIO from collections import deque from fastapi import FastAPI from pydantic import BaseModel from typing import List from PIL import Image from dotenv import load_dotenv from pymongo import MongoClient, ReturnDocument from pymongo.errors import ConfigurationError from datetime import datetime # Load env from root current_dir = os.path.dirname(os.path.abspath(__file__)) env_path = os.path.join(current_dir, "..", ".env") load_dotenv(env_path) MONGO_URI = os.environ.get("MONGODB_URI") mongo_client = MongoClient(MONGO_URI) try: db = mongo_client.get_default_database() except ConfigurationError: db = mongo_client["test"] crops_collection = db["cropstates"] sim_state_collection = db["simulator_state"] print(f"[DEBUG] MongoDB connected to DB: '{db.name}'") print(f"[DEBUG] MONGO_URI = {MONGO_URI[:40] if MONGO_URI else 'NOT SET'}...") MODEL_PATH = "models/PPO/lettuce_brain_v1.zip" HISTORY_LEN = 20 CROP_LIFECYCLES = { "lettuce": { "stages": [ {"name": "seedling", "end_hour": 168}, {"name": "vegetative", "end_hour": 504}, {"name": "harvest", "end_hour": 999999}, ] }, "tomato": { "stages": [ {"name": "seedling", "end_hour": 336}, {"name": "vegetative", "end_hour": 1008}, {"name": "flowering", "end_hour": 1680}, {"name": "fruiting", "end_hour": 999999}, ] }, "basil": { "stages": [ {"name": "seedling", "end_hour": 168}, {"name": "vegetative", "end_hour": 672}, {"name": "harvest", "end_hour": 999999}, ] }, "strawberry": { "stages": [ {"name": "seedling", "end_hour": 336}, {"name": "vegetative", "end_hour": 1008}, {"name": "flowering", "end_hour": 1512}, {"name": "fruiting", "end_hour": 999999}, ] }, } class FarmAction(BaseModel): acid_dosage_ml: float = 0.0 base_dosage_ml: float = 0.0 nutrient_dosage_ml: float = 0.0 fan_speed_pct: float = 0.0 water_refill_l: float = 0.0 debug_force_ph: float | None = None class BatchActionRequest(BaseModel): crop_id: str action: FarmAction class ResidualPhysicsNet(torch.nn.Module): def __init__(self, state_dim, action_dim): super().__init__() self.net = torch.nn.Sequential( torch.nn.Linear(state_dim + action_dim, 64), torch.nn.Tanh(), torch.nn.Linear(64, 64), torch.nn.ReLU(), torch.nn.Linear(64, state_dim), ) def forward(self, state, action): x = torch.cat([state, action], dim=-1) return self.net(x) class DigitalTwin: def __init__(self, crop_id: str, initial_state: list): self.crop_id = crop_id self.state = np.array(initial_state, dtype=np.float32) self.tank_volume = 100.0 self.plant_health = 100.0 self.residual_model = ResidualPhysicsNet(7, 4) # Try to load trained weights trained_weights_path = os.path.join(current_dir, "residual_physics.pt") if os.path.exists(trained_weights_path): try: checkpoint = torch.load(trained_weights_path, map_location="cpu") self.residual_model.load_state_dict(checkpoint["model_state_dict"]) print(f"[DEBUG] Loaded trained ResidualPhysicsNet weights from {trained_weights_path}") except Exception as e: print(f"[WARNING] Failed to load trained weights: {e}. Using random initialization.") else: print(f"[DEBUG] Trained weights not found at {trained_weights_path}. Using random initialization.") self.history = { "ph": deque([float(self.state[0])] * 5, maxlen=HISTORY_LEN), "ec": deque([float(self.state[1])] * 5, maxlen=HISTORY_LEN), "water_temp": deque([float(self.state[2])] * 5, maxlen=HISTORY_LEN), "air_temp": deque([float(self.state[3])] * 5, maxlen=HISTORY_LEN), "humidity": deque([float(self.state[4])] * 5, maxlen=HISTORY_LEN), "co2": deque([400.0] * 5, maxlen=HISTORY_LEN), "light_intensity": deque([0.0] * 5, maxlen=HISTORY_LEN), "vpd": deque([float(self.state[5])] * 5, maxlen=HISTORY_LEN), } def _calculate_vpd(self, temp, hum): es = 0.61078 * np.exp((17.27 * temp) / (temp + 237.3)) ea = es * (hum / 100.0) return max(0.0, es - ea) def step(self, action: FarmAction = None): if action is None: action = FarmAction() u = np.array( [ action.acid_dosage_ml / 10.0, action.base_dosage_ml / 10.0, action.nutrient_dosage_ml / 20.0, action.fan_speed_pct / 100.0, ], dtype=np.float32, ) ph, ec, wt, at, hum, vpd, bio = self.state d_ph = (u[1] * 0.5) - (u[0] * 0.5) + (0.001 * bio) if action.debug_force_ph: self.state[0] = action.debug_force_ph uptake = 0.02 * bio * vpd d_ec = (u[2] * 0.2) - (uptake / self.tank_volume) d_at = 0.1 - (u[3] * 1.5) d_hum = 1.0 - (u[3] * 5.0) stress = abs(vpd - 1.0) growth = 0.1 * bio * (1.0 - min(stress, 1.0)) physics_delta = np.array( [d_ph, d_ec, 0, d_at, d_hum, 0, growth], dtype=np.float32 ) with torch.no_grad(): nn_delta = self.residual_model( torch.tensor(self.state), torch.tensor(u) ).numpy() self.state += physics_delta + (nn_delta * 0.05) self.state[3] = np.clip(self.state[3], 0, 50) self.state[4] = np.clip(self.state[4], 0, 100) self.state[5] = self._calculate_vpd(self.state[3], self.state[4]) self.state[6] = max(0.1, self.state[6]) ph_score = max(0, 1.0 - abs(self.state[0] - 6.0)) vpd_score = max(0, 1.0 - abs(self.state[5] - 1.0)) self.plant_health = max( 0.0, min(100.0, (float(ph_score) + float(vpd_score)) * 50.0) ) self._update_history() return self._generate_image() def _update_history(self): s = self.state self.history["ph"].append(float(s[0])) self.history["ec"].append(float(s[1])) self.history["water_temp"].append(float(s[2])) self.history["air_temp"].append(float(s[3])) self.history["humidity"].append(float(s[4])) self.history["vpd"].append(float(s[5])) def _generate_image(self): bucket = max(0, min(90, int(self.plant_health // 10) * 10)) filename = f"{bucket}.png" if os.path.exists(filename): return Image.open(filename) return Image.new("RGB", (512, 512), (50, 50, 50)) app = FastAPI() simulators = {} def sync_simulators_from_db(): db_crops = crops_collection.find({}) for crop in db_crops: cid = crop.get("crop_id") if not cid: continue if cid not in simulators: sensors = crop.get("sensors", {}) ph_val = sensors.get("pH", [6.0]) ec_val = sensors.get("EC", [1.5]) temp_val = sensors.get("temp", [24.0]) hum_val = sensors.get("humidity", [60.0]) state = [ ph_val[-1] if isinstance(ph_val, list) else ph_val, ec_val[-1] if isinstance(ec_val, list) else ec_val, 20.0, temp_val[-1] if isinstance(temp_val, list) else temp_val, hum_val[-1] if isinstance(hum_val, list) else hum_val, 1.0, 10.0, ] simulators[cid] = DigitalTwin(cid, state) @app.get("/simulation/state") async def get_all_states(): clock = sim_state_collection.find_one_and_update( {"_id": "global_clock"}, {"$inc": {"tick_hours": 1}}, upsert=True, return_document=ReturnDocument.AFTER, ) current_tick = clock["tick_hours"] crops_collection.update_many({}, {"$inc": {"simulated_age_hours": 1}}) sync_simulators_from_db() db_crops = list(crops_collection.find({})) response = [] for crop in db_crops: cid = crop.get("crop_id") if not cid: continue if cid not in simulators: print(f"⚠️ Crop {cid} not in simulators after sync — skipping") continue crop_type = crop.get("crop", "lettuce").lower() age_hours = crop.get("sequence_number", 0) * crop.get("cycle_duration_hours", 1) print(f"Simulating Crop ID: {cid} | Type: {crop_type} | Age (hrs): {age_hours}") cycle_duration = crop.get("cycle_duration_hours", 1) new_stage = crop.get("stage", "seedling") if crop_type in CROP_LIFECYCLES: for stage_info in CROP_LIFECYCLES[crop_type]["stages"]: if age_hours <= stage_info["end_hour"]: new_stage = stage_info["name"] break if new_stage != crop.get("stage"): crops_collection.update_one( {"crop_id": cid}, {"$set": {"stage": new_stage}} ) print( f" current_tick: {current_tick} | crop_id: {cid} | age_hours: {age_hours} | stage: {new_stage} | cycle_duration: {cycle_duration}" ) if current_tick % cycle_duration != 0 and current_tick != 1: continue sim = simulators[cid] pil_img = sim._generate_image() buf = BytesIO() pil_img.save(buf, format="PNG") img_b64 = base64.b64encode(buf.getvalue()).decode("utf-8") response.append( { "crop_id": cid, "sensor_window": {k: list(v) for k, v in sim.history.items()}, "metadata": { "crop": crop_type, "stage": new_stage, "health": round(float(sim.plant_health), 1), "biomass_est": round(float(sim.state[6]), 2), "age_hours": age_hours, "global_tick": current_tick, }, "image": img_b64, } ) return response @app.post("/simulation/action") async def take_batch_actions(payload: List[BatchActionRequest]): sync_simulators_from_db() results = [] for req in payload: cid = req.crop_id if cid not in simulators: results.append( {"crop_id": cid, "status": "error", "message": "Crop not found"} ) continue sim = simulators[cid] sim.step(req.action) push_payload = { "sensors.pH": {"$each": [float(sim.state[0])], "$slice": -5}, "sensors.EC": {"$each": [float(sim.state[1])], "$slice": -5}, "sensors.temp": {"$each": [float(sim.state[3])], "$slice": -5}, "sensors.humidity": {"$each": [float(sim.state[4])], "$slice": -5}, } set_payload = { "action_taken": req.action.dict(), "last_updated": datetime.utcnow(), } crops_collection.update_one( {"crop_id": cid}, { "$push": push_payload, "$set": set_payload, "$inc": {"sequence_number": 1}, }, ) results.append( { "crop_id": cid, "status": "success", "new_state": { "pH": float(sim.state[0]), "EC": float(sim.state[1]), "temp": float(sim.state[3]), "humidity": float(sim.state[4]), }, } ) return {"updated_crops": results} if __name__ == "__main__": port = int(os.environ.get("SIMULATOR_PORT", 8001)) uvicorn.run(app, host="0.0.0.0", port=port) |