Upload suggestion.ipynb

suggestion.ipynb

@@ -0,0 +1,265 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sData = []  # sinhala data tuple\n",
+    "eData = []  # english data tuple\n",
+    "\n",
+    "lstword = []\n",
+    "final = []\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import pickle\n",
+    "import TranslaterLogic\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clearlstword():\n",
+    "    global lstword\n",
+    "    lstword = []\n",
+    "\n",
+    "\n",
+    "def translate(txt,t):\n",
+    "    global lstword\n",
+    "    lstword.clear()\n",
+    "    if len(txt)==0:\n",
+    "        return lstword\n",
+    "    else:\n",
+    "        l=t.printAutoSuggestions(txt, 2)\n",
+    "    #if len(txt) >= 5:\n",
+    "       # t.printAutoSuggestions(txt, 1)\n",
+    "    #else:\n",
+    "       # t.printAutoSuggestions(txt, 2)\n",
+    "    #print(lstword)\n",
+    "        return l\n",
+    "\n",
+    "\n",
+    "class TrieNode():\n",
+    "    def __init__(self):\n",
+    "        # Initialising one node for trie\n",
+    "        self.children = {}\n",
+    "        self.last = False\n",
+    "\n",
+    "\n",
+    "class Trie():\n",
+    "\n",
+    "    def __init__(self):\n",
+    "\n",
+    "        # Initialising the trie structure.\n",
+    "        self.root = TrieNode()\n",
+    "\n",
+    "    def formTrie(self, keys):\n",
+    "\n",
+    "        # Forms a trie structure with the given set of strings\n",
+    "        # if it does not exists already else it merges the key\n",
+    "        # into it by extending the structure as required\n",
+    "        for key in keys:\n",
+    "            self.insert(key)  # inserting one key to the trie.\n",
+    "\n",
+    "    def insert(self, key):\n",
+    "\n",
+    "        # Inserts a key into trie if it does not exist already.\n",
+    "        # And if the key is a prefix of the trie node, just\n",
+    "        # marks it as leaf node.\n",
+    "        node = self.root\n",
+    "\n",
+    "        for a in key:\n",
+    "            if not node.children.get(a):\n",
+    "                node.children[a] = TrieNode()\n",
+    "\n",
+    "            node = node.children[a]\n",
+    "\n",
+    "        node.last = True\n",
+    "\n",
+    "    def suggestionsRec(self, node, word):\n",
+    "\n",
+    "        # Method to recursively traverse the trie\n",
+    "        # and return a whole word.\n",
+    "        lstword=[]\n",
+    "        \n",
+    "        if node.last:\n",
+    "            sin_indexes = [n for n, x in enumerate(eData) if x == word]\n",
+    "            for i in sin_indexes:\n",
+    "                y = int(i)\n",
+    "                if sData[y] not in lstword:\n",
+    "                    # print(sData[y])\n",
+    "                    lstword.append(sData[y])\n",
+    "        #adding the rule\n",
+    "        #txt=str(TranslaterLogic.convertText(word))\n",
+    "       # print(txt)\n",
+    "        #lstword.append(txt)\n",
+    "        return lstword\n",
+    "                    \n",
+    "    def suggestionsRecsuffix(self, node, word):\n",
+    "\n",
+    "        # Method to recursively traverse the trie\n",
+    "        # and return a whole word.\n",
+    "        if node.last:\n",
+    "            sin_indexes = [n for n, x in enumerate(eData) if x == word]\n",
+    "            for i in sin_indexes:\n",
+    "                y = int(i)\n",
+    "                if sData[y] not in lstword:\n",
+    "                    # print(sData[y])\n",
+    "                    lstword.append(sData[y])\n",
+    "\n",
+    "        for a, n in node.children.items():\n",
+    "            self.suggestionsRec(n, word + a)\n",
+    "\n",
+    "    def printAutoSuggestions(self, key, para):\n",
+    "\n",
+    "        # adding text using rule\n",
+    "        # lstword.append(str(TranslaterLogic.convertText(key)))\n",
+    "\n",
+    "        # Returns all the words in the trie whose common\n",
+    "        # prefix is the given key thus listing out all\n",
+    "        # the suggestions for autocomplete.\n",
+    "\n",
+    "        node = self.root\n",
+    "\n",
+    "        for a in key:\n",
+    "            # no string in the Trie has this prefix\n",
+    "            if not node.children.get(a):\n",
+    "                return 0\n",
+    "            node = node.children[a]\n",
+    "\n",
+    "        # If prefix is present as a word, but\n",
+    "        # there is no subtree below the last\n",
+    "        # matching node.\n",
+    "        if not node.children:\n",
+    "            return -1\n",
+    "        if para == 1:\n",
+    "            lst=self.suggestionsRecsuffix(node, key)\n",
+    "            return lst\n",
+    "        else:\n",
+    "            lst=self.suggestionsRec(node, key)\n",
+    "            return lst"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code for creating trie\n",
+    "\n",
+    "textFile = open(\"singlishtrainsuggest.txt\",\n",
+    "                mode='r', encoding='utf-8')\n",
+    "for i in textFile:\n",
+    "    txt = i.split(\"/\")\n",
+    "    eData.append(txt[0])\n",
+    "    sData.append(txt[1].strip('\\n'))\n",
+    "\n",
+    "keys = eData\n",
+    "# keys to form the trie structure.\n",
+    "# creating trie object\n",
+    "t = Trie()\n",
+    "\n",
+    "# creating the trie structure with the\n",
+    "# given set of strings.\n",
+    "# t.formTrie(keys)\n",
+    "# print(\"Trie generated .\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trie saved.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import pickle\n",
+    "\n",
+    "# # Saving the Trie object\n",
+    "# with open('trie.pkl', 'wb') as f:\n",
+    "#     pickle.dump(t, f)\n",
+    "\n",
+    "# print(\"Trie saved.\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trie loaded.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "# Loading the Trie object\n",
+    "with open('trie.pkl', 'rb') as f:\n",
+    "    loaded_t = pickle.load(f)\n",
+    "\n",
+    "print(\"Trie loaded.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ආදරය', 'ආදාරය', 'අදාරය', 'ආධාරය', 'අදරය']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(translate(\"adaraya\",loaded_t))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}