initial upload

__init__.py

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+from .script import ScriptCog
+
+def setup(bot):
+    bot.add_cog(ScriptCog(bot))

info.json

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+{
+    "AUTHOR" : "Brandon Silva",
+    "INSTALL_MSG" : "Thanks for installing my cog!",
+    "NAME" : "ScriptCog",
+    "SHORT" : "Generates text from keras model.",
+    "DESCRIPTION" : "Uses a keras model to generate text.",
+    "TAGS" : ["mlp"],
+    "REQUIREMENTS" : ["tensorflow", "keras"],
+    "HIDDEN" : false
+}

requirements.txt

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+tensorflow==1.5
+keras

script.py

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+import discord
+from discord.ext import commands
+from keras.models import load_model
+
+import numpy as np
+import os
+import pickle
+
+
+#loads dictionary from file
+def _load_dict(path):
+    with open(path, 'rb') as file:
+         dict = pickle.load(file)
+    return dict
+
+#dictionaries for tokenizing puncuation and converting it back
+punctuation_to_tokens = {'!':' ||exclaimation_mark|| ', ',':' ||comma|| ', '"':' ||quotation_mark|| ',
+                          ';':' ||semicolon|| ', '.':' ||period|| ', '?':' ||question_mark|| ', '(':' ||left_parentheses|| ',
+                          ')':' ||right_parentheses|| ', '--':' ||dash|| ', '\n':' ||return|| ', ':':' ||colon|| '}
+
+tokens_to_punctuation = {token.strip(): punc for punc, token in punctuation_to_tokens.items()}
+
+#for all of the puncuation in replace_list, convert it to tokens
+def _tokenize_punctuation(text):
+    replace_list = ['.', ',', '!', '"', ';', '?', '(', ')', '--', '\n', ':']
+    for char in replace_list:
+        text = text.replace(char, punctuation_to_tokens[char])
+    return text
+
+#convert tokens back to puncuation
+def _untokenize_punctuation(text):
+    replace_list = ['||period||', '||comma||', '||exclaimation_mark||', '||quotation_mark||',
+                    '||semicolon||', '||question_mark||', '||left_parentheses||', '||right_parentheses||',
+                    '||dash||', '||return||', '||colon||']
+    for char in replace_list:
+        if char == '||left_parentheses||':#added this since left parentheses had an extra space
+            text = text.replace(' ' + char + ' ', tokens_to_punctuation[char])
+        text = text.replace(' ' + char, tokens_to_punctuation[char])
+    return text
+
+"""
+helper function that instead of just doing argmax for prediction, actually taking a sample of top possible words
+takes a tempature which defines how many predictions to consider. lower means the word picked will be closer to the highest predicted word.
+"""
+def _sample(prediction, temp=0):
+    if temp <= 0:
+        return np.argmax(prediction)
+    prediction = prediction[0]
+    prediction = np.asarray(prediction).astype('float64')
+    prediction = np.log(prediction) / temp
+    expo_prediction = np.exp(prediction)
+    prediction = expo_prediction / np.sum(expo_prediction)
+    probabilities = np.random.multinomial(1, prediction, 1)
+    return np.argmax(probabilities)
+
+
+"""This cog generates scripts based on imported model, I used a keras model. """
+class ScriptCog:
+
+    def __init__(self, bot):
+        self.bot = bot
+
+        os.makedirs("data/scriptcog/", exist_ok=True)
+        os.makedirs("data/scriptcog/dicts", exist_ok=True)
+        self.model_path = "data/scriptcog/model.h5"
+        self.dict_path = "data/scriptcog/dicts/"
+
+        self.model = load_model(self.model_path)
+        self.word_limit = 100
+
+        try:
+            self.word_to_int = _load_dict(self.dict_path + 'word_to_int.pkl')
+            self.int_to_word = _load_dict(self.dict_path  + 'int_to_word.pkl')
+            self.sequence_length = _load_dict(self.dict_path  + 'sequence_length.pkl')
+        except:
+            self.word_to_int = None
+            self.int_to_word = None
+            self.sequence_length = None
+
+    @commands.command(pass_context=True)
+    async def setwordlimit(self, ctx, num_words : int = 100):
+        if ctx.invoked_subcommand is None:
+            await self.bot.say("Usage: setwordlimit limit")
+            return
+        self.word_limit = num_words
+        await self.bot.say("Maximum number of words is now {}".format(self.word_limit))
+
+    @commands.command(pass_context=True)
+    async def genscript(self, ctx, num_words : int = 100, temp : double = 0.5 ):
+        if ctx.invoked_subcommand is None:
+            await self.bot.say("Usage: genscript num_words randomness(between 0 and 1)")
+            return
+        if num_words > self.word_limit:
+            await self.bot.say("Please keep script sizes to {} words or less.".format(self.word_limit))
+            return
+
+        if temp > 1.0:
+            temp = 1.0
+        elif temp < 0:
+            temp = 0
+
+        await self.bot.say("Generating script, please wait...")
+        input_text = seed
+        for _  in range(num_words):
+            #tokenize text to ints
+            int_text = _tokenize_punctuation(input_text)
+            int_text = int_text.lower()
+            int_text = int_text.split()
+            try:
+                int_text = np.array([self.word_to_int[word] for word in int_text], dtype=np.int32)
+            except KeyError:
+                await self.bot.say("Sorry, that seed word is not in my vocabulary.\nPlease try an English word from the show.\n")
+                return
+            #pad text if it is too short, pads with zeros at beginning of text, so shouldnt have too much noise added
+            int_text = pad_sequences([int_text], maxlen=self.sequence_length)
+            #predict next word:
+            prediction = self.model.predict(int_text, verbose=0)
+            output_word = self.int_to_word[_sample(prediction, temp=temp)]
+            #append to the result
+            input_text += ' ' + output_word
+        #convert tokenized punctuation and other characters back
+        result = _untokenize_punctuation(input_text)
+
+        await self.bot.say(result)
+
+def setup(bot):
+    bot.add_cog(ScriptCog(bot))