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Bot GPT 3.5 Here's an example of a simple chatbot that implements a decision tree to determine if an object is recyclable using a modular architecture, appropriate libraries, well-named variables, lists, and control structures.
To implement this code, you can use Python's `pandas` library to load and process the decision tree rules from a CSV file.
```python
import pandas as pd
class DecisionTree:
def __init__(self, rules):
self.rules = rules
def is_recyclable(self, item):
current_node = self.rules
while True:
if isinstance(current_node, str):
return current_node
key = next(key for key in current_node.keys() if key in item)
current_node = current_node[key]
if __name__ == "__main__":
# Decision tree rules stored in a CSV file
decision_tree_rules = pd.read_csv('decision_tree.csv').to_dict(orient='records')[0]
decision_tree = DecisionTree(decision_tree_rules)
while True:
user_input = input("Enter an object or type 'quit' to exit: ")
if user_input.lower() == 'quit':
break
result = decision_tree.is_recyclable(user_input)
print(f"The object is {result}")
```
In this code, we define a `DecisionTree` class that takes the decision tree rules as a parameter and contains the `is_recyclable` method to determine if an object is recyclable based on the given rules.
The decision tree rules are stored in a CSV file and loaded into a dictionary using the `pandas` library. The `DecisionTree` class uses this dictionary to navigate through the decision tree.
In the `main` section, we create an instance of the `DecisionTree` class with the loaded decision tree rules. Then, we continuously prompt the user to enter an object. The user can type 'quit' to exit the program.
Finally, we call the `is_recyclable` method on the `DecisionTree` instance using the user's input and print the result indicating whether the object is recyclable or not.