64 lines
1.4 KiB
Python
64 lines
1.4 KiB
Python
import pymongo
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import markovify
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import sys
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import random
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import os
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def log(msg, err=False, tabs=0):
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if (not err):
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print("[*] " + "\t" * tabs + msg)
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else:
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print("[X] " + "\t" * tabs + msg)
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def dbg(msg, tabs=0):
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print("[D] " + "\t" * tabs + msg)
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if (len(sys.argv) < 3):
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log("Not enough arguments!", err=True)
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sys.exit(1)
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# Load the model for the markov chains
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file = open("bee-movie.txt", "r")
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text = file.read()
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file.close()
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# Create the model using Markov Chains
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model = markovify.Text(text)
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# Generate 25 levels
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log("Generating levels...")
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levels = []
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vocab = list(range(1, 125))
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level = 1
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for i in range(25):
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name = model.make_short_sentence(50, tries=100)
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description = " ".join([model.make_sentence(tries=100) for x in range(4)])
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level_vocab = []
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# Get random vocabulary
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for v in range(4):
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index = random.randint(0, len(vocab) - 1)
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level_vocab.append(index)
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del vocab[index]
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log("Level {}: {}".format(level, name))
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levels.append({
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"level": level,
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"name": name,
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"description": description,
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"vocab": level_vocab
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})
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level += 1
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if os.getenv("DEBUG") != None:
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sys.exit(0)
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log("Connecting to database...")
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client = pymongo.MongoClient(sys.argv[1])
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log("Getting DB...")
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db = client[sys.argv[2]]
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log("Inserting...")
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res = db["levels"].insert_many(levels)
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log("Success", tabs=1)
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