AlxndrJhn
commented
1 year ago same error for me
System: Windows
SYSTEM: Command browse_website returned: ("Answer gathered from website: Yes, the Robotics Toolbox library is suitable for generating 3D visualizations of robot trajectories. It leverages Python's capabilities for graphics and provides tools for representing the kinematics and dynamics of serial-link manipulators, as well as mobile robots with functions for robot motion models, path planning algorithms, and map building. It also includes a web-based visualizer called Swift. \n \n Links: ['Skip to main content (https://pypi.org/project/roboticstoolbox-python/#content)', '\\n\\n (https://pypi.org/)', 'Help (https://pypi.org/help/)', 'Sponsors (https://pypi.org/sponsors/)', 'Log in (https://pypi.org/account/login/)']", <selenium.webdriver.chrome.webdriver.WebDriver (session="c8523ab7562da5de799dc64b132fcd76")>)
Traceback (most recent call last):
File "/usr/local/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/local/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/app/autogpt/__main__.py", line 5, in <module>
autogpt.cli.main()
File "/usr/local/lib/python3.10/site-packages/click/core.py", line 1130, in __call__
return self.main(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/click/core.py", line 1055, in main
rv = self.invoke(ctx)
File "/usr/local/lib/python3.10/site-packages/click/core.py", line 1635, in invoke
rv = super().invoke(ctx)
File "/usr/local/lib/python3.10/site-packages/click/core.py", line 1404, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/usr/local/lib/python3.10/site-packages/click/core.py", line 760, in invoke
return __callback(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/click/decorators.py", line 26, in new_func
return f(get_current_context(), *args, **kwargs)
File "/app/autogpt/cli.py", line 90, in main
run_auto_gpt(
File "/app/autogpt/main.py", line 171, in run_auto_gpt
agent.start_interaction_loop()
File "/app/autogpt/agent/agent.py", line 112, in start_interaction_loop
assistant_reply = chat_with_ai(
File "/app/autogpt/llm/chat.py", line 167, in chat_with_ai
agent.summary_memory = update_running_summary(
File "/app/autogpt/memory_management/summary_memory.py", line 76, in update_running_summary
content_dict = json.loads(event["content"])
File "/usr/local/lib/python3.10/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/local/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/local/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
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⚠️ Search for existing issues first ⚠️
Which Operating System are you using?
Windows
Which version of Auto-GPT are you using?
Latest Release
GPT-3 or GPT-4?
GPT-4
Steps to reproduce 🕹
Auto-GPT-Plugin-SystemInfo: 0.1.2 - This is system info plugin for Auto-GPT. Welcome back! Would you like me to return to being AutoLearnGPT? Asking user via keyboard... Continue with the last settings? Name: AutoLearnGPT Role: an autonomous agent that leverages machine learning algorithms to learn and adapt to your writing style, preferences, and patterns on your Windows PC. Goals: ['Continuously analyze and learn from your writing patterns and preferences to provide personalized suggestions and predictions that enhance your writing experience.', 'Automatically adjust its algorithms and models to improve accuracy and relevance based on your feedback and usage patterns.', "Provide a user-friendly interface that allows you to easily customize and fine-tune the AI's learning and prediction capabilities to suit your needs.", 'Ensure data privacy and security by implementing robust encryption and protection mechanisms to safeguard your personal information and writing data.', 'Continuously monitor and optimize system performance to ensure fast and efficient learning and prediction without compromising system resources or stability.'] API Budget: infinite Continue (y/n): n Welcome to Auto-GPT! run with '--help' for more information. Create an AI-Assistant: input '--manual' to enter manual mode. Asking user via keyboard... I want Auto-GPT to: create a machine learning ai on my windows pc, i want to write with it and it should be able to learn autonomously MLWPGPT has been created with the following details: Name: MLWPGPT Role: an AI assistant that specializes in machine learning on Windows PCs. It helps users to create and train machine learning models, and provides autonomous learning capabilities to improve its own performance. Goals:
Current behavior 😯
No response
Expected behavior 🤔
No response
Your prompt 📝
Your Logs 📒
NEXT ACTION: COMMAND = message_agent ARGUMENTS = {'key': 0, 'message': 'To handle missing values, you can use the fillna method to replace missing values with the mean or median of the feature. To encode categorical variables, you can use the get_dummies method to create one-hot encoded features. To scale numerical features, you can use the StandardScaler or MinMaxScaler classes from the sklearn.preprocessing module. To split the data into training and testing sets, you can use the train_test_split function from the sklearn.model_selection module. To select a machine learning algorithm, you can use the following steps:\n- Choose a model: For regression problems, you can use linear regression, decision trees, or neural networks. For classification problems, you can use logistic regression, decision trees, or support vector machines.\n- Train the model: You can train the model on the training set using the fit method of the model object.\n- Evaluate the model: You can evaluate the performance of the model on the testing set using metrics such as mean squared error or R-squared. Would you like me to provide you with an example implementation of these steps?'} SYSTEM: Command message_agent returned: I apologize for the repeated response. Here's an example implementation of these steps for the Boston Housing dataset using decision trees:", line 198, in _run_module_as_main
File "", line 88, in _run_code
File "C:\Users\goc24\OneDrive\Desktop\Auto-GPT-0.3.0\autogpt__main.py", line 5, in
autogpt.cli.main()
File "C:\Users\goc24\AppData\Local\Programs\Python\Python311\Lib\site-packages\click\core.py", line 1130, in call
return self.main(*args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\goc24\AppData\Local\Programs\Python\Python311\Lib\site-packages\click\core.py", line 1055, in main
rv = self.invoke(ctx)
^^^^^^^^^^^^^^^^
File "C:\Users\goc24\AppData\Local\Programs\Python\Python311\Lib\site-packages\click\core.py", line 1635, in invoke
rv = super().invoke(ctx)
^^^^^^^^^^^^^^^^^^^
File "C:\Users\goc24\AppData\Local\Programs\Python\Python311\Lib\site-packages\click\core.py", line 1404, in invoke
return ctx.invoke(self.callback, ctx.params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\goc24\AppData\Local\Programs\Python\Python311\Lib\site-packages\click\core.py", line 760, in invoke
return callback(*args, *kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\goc24\AppData\Local\Programs\Python\Python311\Lib\site-packages\click\decorators.py", line 26, in new_func
return f(get_current_context(), args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\goc24\OneDrive\Desktop\Auto-GPT-0.3.0\autogpt\cli.py", line 90, in main
run_auto_gpt(
File "C:\Users\goc24\OneDrive\Desktop\Auto-GPT-0.3.0\autogpt\main.py", line 171, in run_auto_gpt
agent.start_interaction_loop()
File "C:\Users\goc24\OneDrive\Desktop\Auto-GPT-0.3.0\autogpt\agent\agent.py", line 94, in start_interaction_loop
assistant_reply = chat_with_ai(
^^^^^^^^^^^^^
File "C:\Users\goc24\OneDrive\Desktop\Auto-GPT-0.3.0\autogpt\llm\chat.py", line 166, in chat_with_ai
agent.summary_memory = update_running_summary(
^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\goc24\OneDrive\Desktop\Auto-GPT-0.3.0\autogpt\memory_management\summary_memory.py", line 76, in update_running_summary
content_dict = json.loads(event["content"])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\goc24\AppData\Local\Programs\Python\Python311\Lib\json__init__.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\goc24\AppData\Local\Programs\Python\Python311\Lib\json\decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\goc24\AppData\Local\Programs\Python\Python311\Lib\json\decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
^^^^^^^^^^^^^^^^^^^^^^
json.decoder.JSONDecodeError: Expecting property name enclosed in double quotes: line 8 column 5 (char 2201)
import pandas as pd from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeRegressor from sklearn.metrics import mean_squared_error from sklearn.preprocessing import StandardScaler # Load the dataset df = pd.read_csv('boston_housing.csv') # Handle missing values df.fillna(df.mean(), inplace=True) # Encode categorical variables df = pd.get_dummies(df) # Scale numerical features scaler = StandardScaler() df_scaled = scaler.fit_transform(df) # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(df_scaled[:, :-1], df_scaled[:, -1], test_size=0.2, random_state=42) # Choose a model model = DecisionTreeRegressor() # Train the model model.fit(X_train, y_train) # Evaluate the model y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) print('Mean squared error:', mse)This code loads the Boston Housing dataset, handles missing values by replacing them with the mean, encodes categorical variables using one-hot encoding, scales numerical features using standardization, and splits the data into training and testing sets. Traceback (most recent call last): File "