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import numpy as np
import time
import os
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from env.drone_3d import Drone3DEnv
from models.liquid_ppo import make_liquid_ppo
from stable_baselines3 import PPO
def run_demo():
print("Initializing Project Neuro-Flyt 3D Demo (Matplotlib Mode)...")
env = Drone3DEnv(render_mode="human", wind_scale=5.0, wind_speed=2.0)
model_path = "liquid_ppo_drone_final.zip"
if os.path.exists(model_path):
print(f"Loading trained model from {model_path}...")
model = PPO.load(model_path, env=env)
else:
print("No trained model found. Using untrained Liquid Brain.")
model = make_liquid_ppo(env, verbose=1)
print("\n=== DEMO STARTING ===")
obs, info = env.reset()
# Setup Plot
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
from matplotlib.animation import FuncAnimation
def update(frame):
nonlocal obs, info
action, _ = model.predict(obs, deterministic=True)
obs, reward, term, trunc, info = env.step(action)
ax.clear()
ax.set_xlim(-20, 20)
ax.set_ylim(-20, 20)
ax.set_zlim(0, 20)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title(f'Neuro-Flyt 3D | Step: {frame}')
pos = obs[0:3]
wind = info.get("wind", np.zeros(3))
# Draw Drone
ax.scatter(pos[0], pos[1], pos[2], c='blue', s=100, label='Drone')
# Draw Wind Vector
ax.quiver(pos[0], pos[1], pos[2], wind[0], wind[1], wind[2], length=1.0, color='red', label='Wind Force')
# Draw Target
ax.scatter(0, 0, 10, c='green', marker='x', s=100, label='Target')
ax.legend()
if term or trunc:
obs, info = env.reset()
print("Generating Animation (demo.gif)...")
anim = FuncAnimation(fig, update, frames=200, interval=50)
anim.save('demo.gif', writer='pillow', fps=20)
print("Animation saved to demo.gif")
env.close()
if __name__ == "__main__":
run_demo()
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