Autocomplete

AutoComplete_App/backend.py

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+import sqlite3
+import json
+
+class AutoComplete:
+    """
+    It works by building a `WordMap` that stores words to word-follower-count
+    ----------------------------
+    e.g. To train the following statement:
+
+    It is not enough to just know how tools work and what they worth,
+    we have got to learn how to use them and to use them well.
+    And with all these new weapons in your arsenal, we would better
+    get those profits fired up
+
+    we create the following:
+    {   It: {is:1}
+        is: {not:1}
+        not: {enough:1}
+        enough: {to:1}
+        to: {just:1, learn:1, use:2}
+        just: {know:1}
+        .
+        .
+        profits: {fired:1}
+        fired: {up:1}
+    }
+    so the word completion for "to" will be "use".
+    For optimization, we use another store `WordPrediction` to save the
+    predictions for each word
+    """
+
+    def __init__(self):
+        """
+        Returns - None
+        Input - None
+        ----------
+        - Initialize database. we use sqlite3
+        - Check if the tables exist, if not create them
+        - maintain a class level access to the database
+          connection object
+        """
+        self.conn = sqlite3.connect("autocompleteDB.sqlite3", autocommit=True)
+        cur = self.conn.cursor()
+        res = cur.execute("SELECT name FROM sqlite_master WHERE name='WordMap'")
+        tables_exist = res.fetchone()
+
+        if not tables_exist:
+            self.conn.execute("CREATE TABLE WordMap(name TEXT, value TEXT)")
+            self.conn.execute('CREATE TABLE WordPrediction (name TEXT, value TEXT)')
+            cur.execute("INSERT INTO WordMap VALUES (?, ?)", ("wordsmap", "{}",))
+            cur.execute("INSERT INTO WordPrediction VALUES (?, ?)", ("predictions", "{}",))
+
+    def train(self, sentence):
+        """
+        Returns - string
+        Input - str: a string of words called sentence
+        ----------
+        Trains the sentence. It does this by creating a map of
+        current words to next words and their counts for each
+        time the next word appears after the current word
+        - takes in the sentence and splits it into a list of words
+        - retrieves the word map and predictions map
+        - creates the word map and predictions map together
+        - saves word map and predictions map to the database
+        """
+        cur = self.conn.cursor()
+        words_list = sentence.split(" ")
+
+        words_map = cur.execute("SELECT value FROM WordMap WHERE name='wordsmap'").fetchone()[0]
+        words_map = json.loads(words_map)
+
+        predictions = cur.execute("SELECT value FROM WordPrediction WHERE name='predictions'").fetchone()[0]
+        predictions = json.loads(predictions)
+
+        for idx in range(len(words_list)-1):
+            curr_word, next_word = words_list[idx], words_list[idx+1]
+            if curr_word not in words_map:
+                words_map[curr_word] = {}
+            if next_word not in words_map[curr_word]:
+                words_map[curr_word][next_word] = 1
+            else:
+                words_map[curr_word][next_word] += 1
+
+            # checking the completion word against the next word
+            if curr_word not in predictions:
+                predictions[curr_word] = {
+                    'completion_word': next_word,
+                    'completion_count': 1
+                }
+            else:
+                if words_map[curr_word][next_word] > predictions[curr_word]['completion_count']:
+                    predictions[curr_word]['completion_word'] = next_word
+                    predictions[curr_word]['completion_count'] = words_map[curr_word][next_word]
+
+        words_map = json.dumps(words_map)
+        predictions = json.dumps(predictions)
+
+        cur.execute("UPDATE WordMap SET value = (?) WHERE name='wordsmap'", (words_map,))
+        cur.execute("UPDATE WordPrediction SET value = (?) WHERE name='predictions'", (predictions,))
+        return("training complete")
+
+    def predict(self, word):
+        """
+        Returns - string
+        Input - string
+        ----------
+        Returns the completion word of the input word
+        - takes in a word
+        - retrieves the predictions map
+        - returns the completion word of the input word
+        """
+        cur = self.conn.cursor()
+        predictions = cur.execute("SELECT value FROM WordPrediction WHERE name='predictions'").fetchone()[0]
+        predictions = json.loads(predictions)
+        completion_word = predictions[word.lower()]['completion_word']
+        return completion_word
+
+
+
+if __name__ == "__main__":
+    input_ = "It is not enough to just know how tools work and what they worth,\
+              we have got to learn how to use them and to use them well. And with\
+              all these new weapons in your arsenal, we would better get those profits fired up"
+    ac = AutoComplete()
+    ac.train(input_)
+    print(ac.predict("to"))

AutoComplete_App/frontend.py

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+from tkinter import *
+from tkinter import messagebox
+import backend
+
+
+def train():
+    sentence = train_entry.get()
+    ac = backend.AutoComplete()
+    ac.train(sentence)
+
+def predict_word():
+    word = predict_word_entry.get()
+    ac = backend.AutoComplete()
+    print(ac.predict(word))
+
+if __name__ == "__main__":
+    root = Tk()
+    root.title("Input note")
+    root.geometry('300x300')
+
+    train_label = Label(root, text="Train")
+    train_label.pack()
+    train_entry = Entry(root)
+    train_entry.pack()
+
+    train_button = Button(root, text="train", command=train)
+    train_button.pack()
+
+    predict_word_label = Label(root, text="Input term to predict")
+    predict_word_label.pack()
+    predict_word_entry = Entry(root)
+    predict_word_entry.pack()
+
+    predict_button = Button(root, text="predict", command=predict_word)
+    predict_button.pack()
+
+    root.mainloop()