I can't import packages which needs to be installed

[ksd1221]

I tried to run plugin including import packages which needs to be installed (pytorch, pytorchvision, pytorch-tabnet).
But, there is an error like below

[my code]

from taskweaver.plugin import Plugin, register_plugin
import pandas as pd
import numpy as np
from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error
from sklearn.preprocessing import LabelEncoder


@register_plugin
class RunTabnetModel(Plugin):
    def __call__(self, df_train: pd.DataFrame, df_test: pd.DataFrame, target_col_name: str,
                 categorical_columns: list = None,
                 learning_rate: float = 0.01, batch_size: int = 1024, virtual_batch_size: int = 128):
        try:
            import torch
            from pytorch_tabnet.tab_model import TabNetRegressor
            from pytorch_tabnet.pretraining import TabNetPretrainer
        except ImportError as e:
            raise ImportError(
                f"Required packages are missing: {str(e)}. "
                f"Please install the necessary libraries with `pip install torch torchvision pytorch-tabnet`.")

        # Handle default mutable argument
        if categorical_columns is None:
            categorical_columns = []
....

[terminal]
/

  Human   ▶  run tabnet model to predict 'rate' column as target. Use df_train for training and df_test for test
there are categorical columns ['maker', 'main_class', 'class', 'model', 'trim', 'model_year', 'type', 'color_grp',
                               'trans', 'fuel', 'displacement', 'drive_mode', 'accident_grd',
                               'accident_yn', 'in_distance_yn', 'auto_yn', 'main_color_yn']
 ╭───< Planner >
 ├─► [init_plan]
 │   1. Run TabNet model with df_train for training and df_test for testing, targeting the 'rate' column
 │   2. Use the specified categorical columns for the model
 │   3. Report the model performance to the user <interactively depends on 1>
 ├─► [plan]
 │   1. Run TabNet model with df_train for training and df_test for testing, targeting the 'rate' column and using the specified categorical columns
 │   2. Report the model performance to the user
 ├─► [current_plan_step] 1. Run TabNet model with df_train for training and df_test for testing, targeting the 'rate' column and using the specified categorical 
 │   columns
 ├──● Please run the TabNet model to predict the 'rate' column as the target. Use df_train for training and df_test for testing. The categorical columns are 
 │   ['maker', 'main_class', 'class', 'model', 'trim', 'model_year', 'type', 'color_grp', 'trans', 'fuel', 'displacement', 'drive_mode', 'accident_grd', 
 │   'accident_yn', 'in_distance_yn', 'auto_yn', 'main_color_yn'].
 ├─► [board]
 │   I have drawn up a plan: 
 │   1. Run TabNet model with df_train for training and df_test for testing, targeting the 'rate' column and using the specified categorical columns
 │   2. Report the model performance to the user
 │   
 │   Please proceed with this step of this plan: Please run the TabNet model to predict the 'rate' column as the target. Use df_train for training and df_test for 
 │   testing. The categorical columns are ['maker', 'main_class', 'class', 'model', 'trim', 'model_year', 'type', 'color_grp', 'trans', 'fuel', 'displacement', 
 │   'drive_mode', 'accident_grd', 'accident_yn', 'in_distance_yn', 'auto_yn', 'main_color_yn'].
 ╰──● sending message to CodeInterpreter
 ╭───< CodeInterpreter >
 ├─► [thought] ProgramApe will now call the 'tabnet_time_series_modeling' plugin to train and evaluate a TabNet model using the preprocessed training and testing 
 │   datasets, targeting the 'rate' column, and specifying the categorical columns as instructed.
 ├─► [python]
 │   categorical_columns = ['maker', 'main_class', 'class', 'model', 'trim', 'model_year', 'type', 'color_grp', 'trans', 'fuel', 'displacement', 'drive_mode', 
 │   'accident_grd', 'accident_yn', 'in_distance_yn', 'auto_yn', 'main_color_yn']
 │   
 │   model_description, df_train_predicted, df_test_predicted = tabnet_time_series_modeling(preprocessed_df_train, preprocessed_df_test, 'rate', 
 │   categorical_columns=categorical_columns, learning_rate=0.02, batch_size=1024, virtual_batch_size=128)
 │   
 │   (model_description, df_train_predicted, df_test_predicted)
 ├─► [verification] NONE
 ├─► [code_error] No code verification is performed.
 ├─► [execution_status] FAILURE
 ├─► [execution_result]
 │   The execution of the generated python code above has failed
 │   
 │   During execution, the following messages were logged:
 │   Traceback (most recent call last):
 │   
 │     File /tmp/tmpk_w4x1v3/tabnet_time_series_modeling.py:14 in __call__
 │   
 │   ModuleNotFoundError: No module named 'torch'
 │   
 │   
 │   During handling of the above exception, another exception occurred:
 │   
 │   Traceback (most recent call last):
 │   
 │     Cell In[3], line 3
 │       model_description, df_train_predicted, df_test_predicted = tabnet_time_series_modeling(preprocessed_df_train, preprocessed_df_test, 'rate', 
 │   categorical_columns=categorical_columns, learning_rate=0.02, batch_size=1024, virtual_batch_size=128)
 │   
 │     File /tmp/tmpk_w4x1v3/tabnet_time_series_modeling.py:18 in __call__
 │   
 │   ImportError: Required packages are missing: No module named 'torch'. Please install the necessary libraries with `pip install torch torchvision 
 │   pytorch-tabnet`.
 │   
 │   
 ├─► [artifact_paths] []
 ├──● The following python code has been executed:
 │   ```python
 │   categorical_columns = ['maker', 'main_class', 'class', 'model', 'trim', 'model_year', 'type', 'color_grp', 'trans', 'fuel', 'displacement', 'drive_mode', 
 │   'accident_grd', 'accident_yn', 'in_distance_yn', 'auto_yn', 'main_color_yn']
 │   
 │   model_description, df_train_predicted, df_test_predicted = tabnet_time_series_modeling(preprocessed_df_train, preprocessed_df_test, 'rate', 
 │   categorical_columns=categorical_columns, learning_rate=0.02, batch_size=1024, virtual_batch_size=128)
 │   
 │   (model_description, df_train_predicted, df_test_predicted)
 │   ```
 │   
 │   
 │   The execution of the generated python code above has failed
 │   
 │   During execution, the following messages were logged:
 │   Traceback (most recent call last):
 │   
 │     File /tmp/tmpk_w4x1v3/tabnet_time_series_modeling.py:14 in __call__
 │   
 │   ModuleNotFoundError: No module named 'torch'
 │   
 │   
 │   During handling of the above exception, another exception occurred:
 │   
 │   Traceback (most recent call last):
 │   
 │     Cell In[3], line 3
 │       model_description, df_train_predicted, df_test_predicted = tabnet_time_series_modeling(preprocessed_df_train, preprocessed_df_test, 'rate', 
 │   categorical_columns=categorical_columns, learning_rate=0.02, batch_size=1024, virtual_batch_size=128)
 │   
 │     File /tmp/tmpk_w4x1v3/tabnet_time_series_modeling.py:18 in __call__
 │   
 │   ImportError: Required packages are missing: No module named 'torch'. Please install the necessary libraries with `pip install torch torchvision 
 │   pytorch-tabnet`.
 │   
 │   
 ├─► [revise_message]
 │   The execution of the previous generated code has failed. If you think you can fix the problem by rewriting the code, please generate code and run it again.
 │   Otherwise, please explain the problem to me.
 ╰──● sending message to CodeInterpreter
 ╭───< CodeInterpreter >
 ├─► [thought] The error indicates that the PyTorch library, which is a dependency for running the TabNet model, is not installed. To resolve this issue, 
 │   ProgramApe will first attempt to import the required modules directly. If the direct import fails, as indicated by the user's instructions, ProgramApe will 
 │   then proceed to install the necessary libraries.
 ├─► [python]
 │   !pip install torch torchvision pytorch-tabnet
 │   
 │   categorical_columns = ['maker', 'main_class', 'class', 'model', 'trim', 'model_year', 'type', 'color_grp', 'trans', 'fuel', 'displacement', 'drive_mode', 
 │   'accident_grd', 'accident_yn', 'in_distance_yn', 'auto_yn', 'main_color_yn']
 │   
 │   model_description, df_train_predicted, df_test_predicted = tabnet_time_series_modeling(preprocessed_df_train, preprocessed_df_test, 'rate', 
 │   categorical_columns=categorical_columns, learning_rate=0.02, batch_size=1024, virtual_batch_size=128)
 │   
 │   (model_description, df_train_predicted, df_test_predicted)
 ├─► [verification] NONE
 ├─► [code_error] No code verification is performed.
 ├─► [execution_status] FAILURE
 ├─► [execution_result]
 │   The execution of the generated python code above has failed
 │   
 │   During execution, the following messages were logged:
 │   Traceback (most recent call last):
 │   
 │     File /tmp/tmpk_w4x1v3/tabnet_time_series_modeling.py:14 in __call__
 │   
 │   ModuleNotFoundError: No module named 'torch'
 │   
 │   
 │   During handling of the above exception, another exception occurred:
 │   
 │   Traceback (most recent call last):
 │   
 │     Cell In[4], line 5
 │       model_description, df_train_predicted, df_test_predicted = tabnet_time_series_modeling(preprocessed_df_train, preprocessed_df_test, 'rate', 
 │   categorical_columns=categorical_columns, learning_rate=0.02, batch_size=1024, virtual_batch_size=128)
 │   
 │     File /tmp/tmpk_w4x1v3/tabnet_time_series_modeling.py:18 in __call__
 │   
 │   ImportError: Required packages are missing: No module named 'torch'. Please install the necessary libraries with `pip install torch torchvision 
 │   pytorch-tabnet`.
 │   
 │   Defaulting to user installation because normal site-packages is not writeable
 │   
 │   Collecting torch
 │   
 │     Downloading torch-2.3.0-cp310-cp310-manylinux1_x86_64.whl.metadata (26 kB)
 │   
 │   Collecting torchvision
 │   
 │     Downloading torchvision-0.18.0-cp310-cp310-manylinux1_x86_64.whl.metadata (6.6 kB)
 │   
 │   Collecting pytorch-tabnet
 │   
 │     Downloading pytorch_tabnet-4.1.0-py3-none-any.whl.metadata (15 kB)
 │   
 │   Requirement already satisfied: filelock in /usr/local/lib/python3.10/site-packages (from torch) (3.13.4)
 │   
 │   Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/site-packages (from torch) (4.11.0)
 │   
 │   Collecting sympy (from torch)
 │   
 │     Downloading sympy-1.12-py3-none-any.whl.metadata (12 kB)
 │   
 │   Collecting networkx (from torch)
 │   
 │     Downloading networkx-3.3-py3-none-any.whl.metadata (5.1 kB)
 │   
 │   Collecting jinja2 (from torch)
 │   
 │     Downloading Jinja2-3.1.3-py3-none-any.whl.metadata (3.3 kB)
 │   
 │   Collecting fsspec (from torch)
 │   
 │     Downloading fsspec-2024.3.1-py3-none-any.whl.metadata (6.8 kB)
 │   
 │   Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)
 │   
 │     Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
 │   
 │   Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)
 │   
 │     Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
 │   
 │   Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)
 │   
 │     Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
 │   
 │   Collecting nvidia-cu
 │   
 ├─► [artifact_paths] []
 ├──● The following python code has been executed:
 │   ```python
 │   !pip install torch torchvision pytorch-tabnet
 │   
 │   categorical_columns = ['maker', 'main_class', 'class', 'model', 'trim', 'model_year', 'type', 'color_grp', 'trans', 'fuel', 'displacement', 'drive_mode', 
 │   'accident_grd', 'accident_yn', 'in_distance_yn', 'auto_yn', 'main_color_yn']
 │   
 │   model_description, df_train_predicted, df_test_predicted = tabnet_time_series_modeling(preprocessed_df_train, preprocessed_df_test, 'rate', 
 │   categorical_columns=categorical_columns, learning_rate=0.02, batch_size=1024, virtual_batch_size=128)
 │   
 │   (model_description, df_train_predicted, df_test_predicted)
 │   ```
 │   
 │   
 │   The execution of the generated python code above has failed
 │   
 │   During execution, the following messages were logged:
 │   Traceback (most recent call last):
 │   
 │     File /tmp/tmpk_w4x1v3/tabnet_time_series_modeling.py:14 in __call__
 │   
 │   ModuleNotFoundError: No module named 'torch'
 │   
 │   
 │   During handling of the above exception, another exception occurred:
 │   
 │   Traceback (most recent call last):
 │   
 │     Cell In[4], line 5
 │       model_description, df_train_predicted, df_test_predicted = tabnet_time_series_modeling(preprocessed_df_train, preprocessed_df_test, 'rate', 
 │   categorical_columns=categorical_columns, learning_rate=0.02, batch_size=1024, virtual_batch_size=128)
 │   
 │     File /tmp/tmpk_w4x1v3/tabnet_time_series_modeling.py:18 in __call__
 │   
 │   ImportError: Required packages are missing: No module named 'torch'. Please install the necessary libraries with `pip install torch torchvision 
 │   pytorch-tabnet`.
 │   
 │   Defaulting to user installation because normal site-packages is not writeable
 │   
 │   Collecting torch
 │   
 │     Downloading torch-2.3.0-cp310-cp310-manylinux1_x86_64.whl.metadata (26 kB)
 │   
 │   Collecting torchvision
 │   
 │     Downloading torchvision-0.18.0-cp310-cp310-manylinux1_x86_64.whl.metadata (6.6 kB)
 │   
 │   Collecting pytorch-tabnet
 │   
 │     Downloading pytorch_tabnet-4.1.0-py3-none-any.whl.metadata (15 kB)
 │   
 │   Requirement already satisfied: filelock in /usr/local/lib/python3.10/site-packages (from torch) (3.13.4)
 │   
 │   Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/site-packages (from torch) (4.11.0)
 │   
 │   Collecting sympy (from torch)
 │   
 │     Downloading sympy-1.12-py3-none-any.whl.metadata (12 kB)
 │   
 │   Collecting networkx (from torch)
 │   
 │     Downloading networkx-3.3-py3-none-any.whl.metadata (5.1 kB)
 │   
 │   Collecting jinja2 (from torch)
 │   
 │     Downloading Jinja2-3.1.3-py3-none-any.whl.metadata (3.3 kB)
 │   
 │   Collecting fsspec (from torch)
 │   
 │     Downloading fsspec-2024.3.1-py3-none-any.whl.metadata (6.8 kB)
 │   
 │   Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)
 │   
 │     Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
 │   
 │   Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)
 │   
 │     Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)
 │   
 │   Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)
 │   
 │     Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)
 │   
 │   Collecting nvidia-cu
 │   
 ├─► [revise_message]
 │   The execution of the previous generated code has failed. If you think you can fix the problem by rewriting the code, please generate code and run it again.
 │   Otherwise, please explain the problem to me.
 ╰──● sending message to CodeInterpreter
 ╭───< CodeInterpreter >
 ├─► [text] Given the constraints and the error encountered, it seems the execution environment does not have the necessary libraries installed to run the TabNet 
 │   model, and my attempt to install them directly in the code did not succeed due to execution environment restrictions. Unfortunately, without the ability to 
 │   ensure the required libraries (torch, torchvision, pytorch-tabnet) are installed, I cannot proceed with running the TabNet model as requested. The best course
 │    of action would be to ensure these libraries are installed in the execution environment before attempting to run the model again.
 ├──● Given the constraints and the error encountered, it seems the execution environment does not have the necessary libraries installed to run the TabNet model, 
 │   and my attempt to install them directly in the code did not succeed due to execution environment restrictions. Unfortunately, without the ability to ensure 
 │   the required libraries (torch, torchvision, pytorch-tabnet) are installed, I cannot proceed with running the TabNet model as requested. The best course of 
 │   action would be to ensure these libraries are installed in the execution environment before attempting to run the model again.
 ├─► [verification] NONE
 ├─► [code_error] No code verification is performed.
 ├─► [execution_status] NONE
 ├─► [execution_result] No code is executed.
 ╰──● sending message to Planner
 ╭───< Planner >
 ├─► [init_plan]
 │   1. Run TabNet model with df_train for training and df_test for testing, targeting the 'rate' column
 │   2. Use the specified categorical columns for the model
 │   3. Report the model performance to the user <interactively depends on 1>
 ├─► [plan]
 │   1. Run TabNet model with df_train for training and df_test for testing, targeting the 'rate' column and using the specified categorical columns
 │   2. Report the model performance to the user
 ├─► [current_plan_step] Inform the user about the issue and suggest a solution
 ├──● Unfortunately, due to the current execution environment restrictions, we are unable to run the TabNet model because the necessary libraries (torch, 
 │   torchvision, pytorch-tabnet) are not installed, and I cannot install them directly. To proceed with running the TabNet model, these libraries need to be 
 │   installed in the execution environment. Please ensure these libraries are installed and try again.
 ├─► [board]
 │   I have drawn up a plan: 
 │   1. Run TabNet model with df_train for training and df_test for testing, targeting the 'rate' column and using the specified categorical columns
 │   2. Report the model performance to the user
 │   
 │   Please proceed with this step of this plan: Unfortunately, due to the current execution environment restrictions, we are unable to run the TabNet model 
 │   because the necessary libraries (torch, torchvision, pytorch-tabnet) are not installed, and I cannot install them directly. To proceed with running the TabNet
 │    model, these libraries need to be installed in the execution environment. Please ensure these libraries are installed and try again.
 ╰──● sending message to User
 TaskWeaver ▶  Unfortunately, due to the current execution environment restrictions, we are unable to run the TabNet model because the necessary libraries (torch, torchvision, pytorch-tabnet) are not installed, and I cannot install them directly. To proceed with running the TabNet model, these libraries need to be installed in the execution environment. Please ensure these libraries are installed and try again.

Thank you.

[liqul]

@ksd1221 I think your goal is to run the plugin successfully with all dependencies installed. So, I would consider the agent's auto dependency installation as the plan B. Sometimes, the LLM model behaves more conservatively to reject the request to make changes to the environment, and sometimes not.