Merge pull request #512 from nik-6174/main

Create main.py

Author: Mrinank-Bhowmick

projects/Word_Predictor/main.py

@@ -0,0 +1,100 @@
+"""
+This is a word predictor algorithm, that uses you own Whatsapp chats and shows you prediction.
+It takes into account a words and their frequencies after a given word in the texts you have exchanged with a Whatsapp contact.
+
+Requirements:
+1 ) You should have pandas installed -- pip install pandas
+2 ) You should have whatsapp chats downloaded in you PC as Chats.txt
+
+## To download the whatsapp chats:
+1) Open whatsapp on your phone.
+2) Pick a contact of your coice, and open their chat
+3) Click on the 3 dots on the upper-right side of the chat
+4) Go to More -> Export Chat (The chats will the be downloaded)
+5) Put it into your PC, and replace the Chats.txt in the Code with the name of the Downloaded txt file (and directly if required)
+
+"""
+
+
+import pandas as pd
+
+class WhatsAppChat:
+    def __init__(self, filename):
+        # Initialize the object by reading in the chat file, and counting word frequency and next words
+        self.titles = self.read_chat_file(filename)
+        self.word_freq_dict = self.count_word_frequency()
+        self.next_words_dict = self.find_next_words()
+
+    def read_chat_file(self, filename):
+        """Reads in a WhatsApp chat text file and returns a list of titles."""
+        chat_df = pd.read_fwf(filename, header=None)
+        # Extract the chat titles from the DataFrame and return them as a list
+        titles = [title for title in chat_df[2]]
+        return titles
+
+    def count_word_frequency(self):
+        """Counts the frequency of each word in a list of titles."""
+        word_freq_dict = {}
+        for title in self.titles:
+            # Split each title into words and count their frequency
+            for word in str(title).split():
+                if len(word) > 1:
+                    if word in word_freq_dict.keys():
+                        word_freq_dict[word] += 1
+                    else:
+                        word_freq_dict[word] = 1
+        # Sort the word frequency dictionary by value in descending order and return it
+        sorted_word_freq_dict = dict(sorted(word_freq_dict.items(),
+                                             key=lambda x: -1*int(x[1])))
+        return sorted_word_freq_dict
+
+    def find_next_words(self):
+        """Finds the most common next word for each word in a list of titles."""
+        next_words_dict = {}
+        for title in self.titles:
+            # Split each title into words and count the frequency of each next word for each word
+            words = str(title).split()
+            for i in range(len(words)):
+                if i != len(words) - 1:
+                    current_word = words[i]
+                    next_word = words[i+1].replace("\n", "")
+                    if current_word in next_words_dict:
+                        if next_word in next_words_dict[current_word]:
+                            next_words_dict[current_word][next_word] += 1
+                        else:
+                            next_words_dict[current_word][next_word] = 1
+                    else:
+                        next_words_dict[current_word] = {next_word: 1}
+        # Return the dictionary of next words
+        return next_words_dict
+
+
+class NextWordPredictor:
+    def __init__(self, chat, num_predictions=1):
+        # Initialize the object with a WhatsAppChat object and the number of predictions to make
+        self.chat = chat
+        self.num_predictions = num_predictions
+
+    def predict_next_word(self, current_word):
+        """Predicts the next word given a current word and a dictionary of next words."""
+        if current_word in self.chat.next_words_dict:
+            # Get the dictionary of next words for the current word and sort it by frequency
+            next_word_freq_dict = self.chat.next_words_dict[current_word]
+            sorted_next_words = sorted(next_word_freq_dict.items(),
+                                        key=lambda x: x[1],
+                                        reverse=True)
+            # Get the top num_predictions words and return them
+            next_words = [word[0] for word in sorted_next_words[:self.num_predictions]]
+            return next_words
+        else:
+            # If the current word is not in the dictionary of next words, return None
+            return None
+
+# Create a WhatsAppChat object and a NextWordPredictor object after loading Chats.txt file
+# set n as the length of words you want to predict after a given word
+chat = WhatsAppChat('Chats.txt')
+n, given_word = 3, 'good'
+predictor = NextWordPredictor(chat, num_predictions=n)
+
+# This prints the next n word predictions after the given_word
+print(predictor.predict_next_word(given_word))