61 lines
No EOL
1.8 KiB
Python
61 lines
No EOL
1.8 KiB
Python
import os
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import json
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import markovify
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import pickle
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from modules.globalvars import *
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from modules.volta.main import _
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# Get file size and line count for a given file path
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def get_file_info(file_path):
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try:
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file_size = os.path.getsize(file_path)
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with open(file_path, "r") as f:
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lines = f.readlines()
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return {"file_size_bytes": file_size, "line_count": len(lines)}
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except Exception as e:
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return {"error": str(e)}
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# Load memory data from file, or use default dataset if not loaded yet
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def load_memory():
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data = []
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# Try to load data from MEMORY_FILE
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try:
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with open(MEMORY_FILE, "r") as f:
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data = json.load(f)
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except FileNotFoundError:
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pass
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return data
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# Save memory data to MEMORY_FILE
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def save_memory(memory):
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with open(MEMORY_FILE, "w") as f:
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json.dump(memory, f, indent=4)
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# Train a Markov model using memory and optional additional data
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def train_markov_model(memory, additional_data=None):
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if not memory:
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return None
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text = "\n".join(memory)
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if additional_data:
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text += "\n" + "\n".join(additional_data)
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model = markovify.NewlineText(text, state_size=2)
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return model
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# Save the Markov model to a pickle file
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def save_markov_model(model, filename='markov_model.pkl'):
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with open(filename, 'wb') as f:
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pickle.dump(model, f)
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print(f"Markov model saved to {filename}.")
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# Load the Markov model from a pickle file
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def load_markov_model(filename='markov_model.pkl'):
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try:
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with open(filename, 'rb') as f:
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model = pickle.load(f)
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print(f"{GREEN}{_('model_loaded')} {filename}.{RESET}")
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return model
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except FileNotFoundError:
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print(f"{RED}{filename} {_('not_found')}{RESET}")
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return None |