Update app.py

app.py

@@ -4,7 +4,7 @@ import requests
 from geopy.geocoders import Nominatim  # Valid import
 from stable_baselines3 import PPO
 from stable_baselines3.common.vec_env import DummyVecEnv
-import gymnasium as gym
+import gym as gym
 from gymnasium import spaces
 import numpy as np
 from PIL import Image
@@ -148,14 +148,22 @@ def optimize_signal_rl(congestion_level):
         model.learn(total_timesteps=1000)
 
         # Reset the environment
-        obs = env.reset()  # Returns a single value (batch of observations)
+        obs = env.reset()
+
+        # Ensure obs is properly handled (flatten if necessary)
+        if obs.ndim == 0:
+            obs = np.expand_dims(obs, axis=0)  # Convert 0D array to 1D
 
         # RL Optimization loop
         for _ in range(10):
             action, _ = model.predict(obs, deterministic=True)
             obs, rewards, dones, infos = env.step(action)
 
-            # Unpack single observation and reward (batch size of 1)
+            # Ensure obs is properly handled
+            if obs.ndim == 0:
+                obs = np.expand_dims(obs, axis=0)
+
+            # Extract single observation and reward
             obs = obs[0]
             reward = rewards[0]
             done = dones[0]
@@ -175,6 +183,7 @@ def optimize_signal_rl(congestion_level):
         return "Error in RL Optimization"
 
 
+
 def process_traffic_image(image):
     """
     Orchestrates the traffic analysis workflow.