Removed share=True from launch

app.py

@@ -1,54 +1,54 @@
-import glob
-import gradio as gr
-import gym
-import sys
-from torch.utils.tensorboard import SummaryWriter
-import yaml
-import torch
-from cartpole import (
-    make_env, reset_env, Agent, rollout_phase, get_action_shape
-)
-
-MAIN = __name__ == "__main__"
-examples = [0, 1, 31415, 'Hello, World!', 'This is a seed...']
-
-def generate_video(
-    string: str, wandb_path='wandb/run-20230303_211416-ox4d1p0u/files'
-):
-    with open(f'{wandb_path}/config.yaml') as f_cfg:
-        config = yaml.safe_load(f_cfg)
-    seed = hash(string)  % ((sys.maxsize + 1) * 2)
-    num_envs = config['num_envs']['value']
-    num_steps = config['num_steps']['value']
-    assert seed >= 0
-    assert isinstance(seed, int)
-    run_name = f'seed{seed}'
-    log_dir = f'generate/{run_name}'
-    writer = SummaryWriter(log_dir)
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    envs = gym.vector.SyncVectorEnv([
-        make_env("CartPole-v1", seed, i, True, run_name) 
-        for i in range(num_envs)
-    ])
-    action_shape = get_action_shape(envs)
-    next_obs, next_done = reset_env(envs, device)
-    global_step = 0
-    agent = Agent(envs).to(device)
-    agent.load_state_dict(torch.load(f'{wandb_path}/model_state_dict.pt'))
-    rollout_phase(
-        next_obs, next_done, agent, envs, writer, device, 
-        global_step, action_shape, num_envs, num_steps,
-    )
-    video_path = glob.glob(f'videos/{run_name}/*.mp4')[0]
-    return video_path
-
-if MAIN:
-    demo = gr.Interface(
-        fn=generate_video,
-        inputs=[
-            gr.components.Textbox(lines=1, label="Seed"),
-        ],
-        outputs=gr.components.Video(label="Generated Video"),
-        examples=examples,
-    )
-    demo.launch(share=True)
+import glob
+import gradio as gr
+import gym
+import sys
+from torch.utils.tensorboard import SummaryWriter
+import yaml
+import torch
+from cartpole import (
+    make_env, reset_env, Agent, rollout_phase, get_action_shape
+)
+
+MAIN = __name__ == "__main__"
+examples = [0, 1, 31415, 'Hello, World!', 'This is a seed...']
+
+def generate_video(
+    string: str, wandb_path='wandb/run-20230303_211416-ox4d1p0u/files'
+):
+    with open(f'{wandb_path}/config.yaml') as f_cfg:
+        config = yaml.safe_load(f_cfg)
+    seed = hash(string)  % ((sys.maxsize + 1) * 2)
+    num_envs = config['num_envs']['value']
+    num_steps = config['num_steps']['value']
+    assert seed >= 0
+    assert isinstance(seed, int)
+    run_name = f'seed{seed}'
+    log_dir = f'generate/{run_name}'
+    writer = SummaryWriter(log_dir)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    envs = gym.vector.SyncVectorEnv([
+        make_env("CartPole-v1", seed, i, True, run_name) 
+        for i in range(num_envs)
+    ])
+    action_shape = get_action_shape(envs)
+    next_obs, next_done = reset_env(envs, device)
+    global_step = 0
+    agent = Agent(envs).to(device)
+    agent.load_state_dict(torch.load(f'{wandb_path}/model_state_dict.pt'))
+    rollout_phase(
+        next_obs, next_done, agent, envs, writer, device, 
+        global_step, action_shape, num_envs, num_steps,
+    )
+    video_path = glob.glob(f'videos/{run_name}/*.mp4')[0]
+    return video_path
+
+if MAIN:
+    demo = gr.Interface(
+        fn=generate_video,
+        inputs=[
+            gr.components.Textbox(lines=1, label="Seed"),
+        ],
+        outputs=gr.components.Video(label="Generated Video"),
+        examples=examples,
+    )
+    demo.launch()