update gradio

app.py

@@ -33,15 +33,16 @@ class DemoRunner:
         hydra.initialize(version_base="1.2", config_path='cliport/cfg')
         self._cfg = hydra.compose(config_name="data")
 
-    def setup(self, api_key):
+    def setup(self, api_key, model_choice):
         cfg = self._cfg
+        cfg['gpt_model'] = model_choice
         openai.api_key = api_key
         cfg['model_output_dir'] = 'temp'
         cfg['prompt_folder'] = 'bottomup_task_generation_prompt'
         set_gpt_model(cfg['gpt_model'])
-        cfg['load_memory'] = True
+        cfg['load_memory'] = False
         cfg['use_template'] = True
-        cfg['task_description_candidate_num'] = 10
+        cfg['task_description_candidate_num'] = 2
         cfg['record']['save_video'] = True
 
         print("cfg = ", cfg)
@@ -54,8 +55,9 @@ class DemoRunner:
 
         return info
 
-    def setup_top_down(self, api_key, target_task_name):
+    def setup_top_down(self, api_key, target_task_name, model_choice):
         cfg = self._cfg
+        cfg['gpt_model'] = model_choice
         openai.api_key = api_key
         cfg['model_output_dir'] = 'temp'
         cfg['prompt_folder'] = 'topdown_task_generation_prompt'
@@ -105,19 +107,21 @@ class DemoRunner:
         print("run example cost = ", t2 - t1, " s")
 
 
-def setup(api_key, option_choice, target_task_name):
+def setup(api_key, option_choice, model_choice, target_task_name):
     print(option_choice)
     if not api_key:
         return 'Please enter your OpenAI API key!', None
 
+    if model_choice is None:
+        return 'Please choose the model!', None
     if option_choice is None:
         return 'Please choose the mode!', None
     demo_runner = DemoRunner()
 
     if option_choice == 'top-down':
-        info = demo_runner.setup_top_down(api_key, target_task_name) + option_choice
+        info = demo_runner.setup_top_down(api_key, target_task_name, model_choice) + option_choice
     elif option_choice == 'bottom-up':
-        info = demo_runner.setup(api_key) + option_choice
+        info = demo_runner.setup(api_key, model_choice) + option_choice
     else:
         raise NotImplementedError
     return info, demo_runner
@@ -153,6 +157,7 @@ if __name__ == '__main__':
                 with gr.Row():
                     inp_api_key = gr.Textbox(label='OpenAI API Key (this is not stored anywhere)', lines=1)
 
+                model_choice = gr.Radio(["gpt-3.5-turbo-16k", "gpt-4"], label="Which model?", interactive=True)
                 option_choice = gr.Radio(["bottom-up", "top-down"], label="Which mode?", interactive=True)
                 inp_instruction = gr.Textbox(label='Target Task Name (if top-down)', lines=1)
                 info_setup = gr.Markdown(label='Setup Info')
@@ -169,7 +174,7 @@ if __name__ == '__main__':
                 video_run = gr.Video(label='Video of Last Instruction', autoplay=True)
         btn_setup.click(
             setup,
-            inputs=[inp_api_key, option_choice, inp_instruction],
+            inputs=[inp_api_key, option_choice, model_choice, inp_instruction],
             outputs=[info_setup, state]
         )
         btn_run.click(

cliport/environments/environment.py

@@ -76,7 +76,6 @@ class Environment(gym.Env):
         self.position_bounds = gym.spaces.Box(
             low=np.array([0.25, -0.5, 0.], dtype=np.float32),
             high=np.array([0.75, 0.5, 0.28], dtype=np.float32),
-            shape=(3,),
             dtype=np.float32)
         self.bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.3]])
 

gensim/agent.py

@@ -125,7 +125,7 @@ class Agent:
             code_reference_cmd = extract_list(res, prefix='code_reference')
             exec(code_reference_cmd, globals())
             task_code_reference_replace_prompt = ''
-            for key in code_reference:
+            for key in code_reference_cmd:
                 if key in self.memory.online_code_buffer:
                     task_code_reference_replace_prompt += f'```\n{self.memory.online_code_buffer[key]}\n```\n\n'
                 else:

temp/BuildWheel_error.txt

@@ -1,4 +1,10 @@
 Traceback (most recent call last):
-  File "/home/baochen/Desktop/projects/GPT-CLIPort/gensim/sim_runner.py", line 339, in simulate_task
-    yield "Task Generated ==> Asset Generated ==> API Reviewed ==> Error Reviewed ==> Code Generated ==> Simulation Running completed", self.generated_code, env.video_path
-AttributeError: 'Environment' object has no attribute 'video_path'
+  File "/home/baochen/Desktop/projects/GPT-CLIPort/gensim/sim_runner.py", line 273, in simulate_task
+    task, dataset, env, expert = self.setup_env()
+  File "/home/baochen/Desktop/projects/GPT-CLIPort/gensim/sim_runner.py", line 199, in setup_env
+    env = Environment(
+  File "/home/baochen/Desktop/projects/GPT-CLIPort/cliport/environments/environment.py", line 76, in __init__
+    self.position_bounds = gym.spaces.Box(
+  File "/home/baochen/anaconda3/envs/Metaworld/lib/python3.8/site-packages/gym/spaces/box.py", line 33, in __init__
+    assert np.isscalar(low) and np.isscalar(high), 'box requires scalar bounds. '
+AssertionError: box requires scalar bounds.