Update README.md

README.md

@@ -26,4 +26,180 @@ text = "" #Text with typos here!
 inputs = tokenizer(cipher_text, return_tensors="pt", padding=True, truncation=True, max_length=256).to(device)
 outputs = model.generate(inputs["input_ids"], max_length=256)
 corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+
+
+Full Pipeline Usage:
+```py
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+from string import ascii_lowercase
+import Levenshtein
+import random
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+tokenizer = AutoTokenizer.from_pretrained("Cipher-AI/Substitution-Cipher-Alphabet-Eng")
+alphabet_model = AutoModelForSeq2SeqLM.from_pretrained("Cipher-AI/Substitution-Cipher-Alphabet-Eng").to(device)
+correction_model = AutoModelForSeq2SeqLM.from_pretrained("Cipher-AI/AutoCorrect-EN-v2").to(device)
+
+def similarity_percentage(s1, s2):
+    distance = Levenshtein.distance(s1, s2)
+
+    max_len = max(len(s1), len(s2))
+
+    similarity = (1 - distance / max_len) * 100
+
+    return similarity
+
+def decode(cipher_text, key):
+  decipher_map = {ascii_lowercase[i]: j for i, j in enumerate(key[:26])}
+  decipher_map.update({ascii_lowercase[i].upper(): j.upper() for i, j in enumerate(key[:26])})
+  ans = ''.join(map(lambda x: decipher_map[x] if x in decipher_map else x, cipher_text))
+  return ans
+
+def model_pass(model, input, max_length=256):
+  inputs = tokenizer(input, return_tensors="pt", padding=True, truncation=True, max_length=256).to(device)
+  outputs = model.generate(inputs["input_ids"], max_length=max_length)
+  result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+  return result
+
+def decipher(cipher_text, key) -> str:
+  decipher_map = {ascii_lowercase[i]: j for i, j in enumerate(key[0])}
+  decipher_map.update({ascii_lowercase[i].upper(): j.upper() for i, j in enumerate(key[0])})
+
+  result = ''.join(map(lambda x: decipher_map[x] if x in decipher_map else x, cipher_text[0]))
+
+  return result
+
+def cipher(plain_text) -> tuple[str, list]:
+  alphabet_map = list(ascii_lowercase)
+  random.shuffle(alphabet_map)
+  alphabet_map = {i : j for i, j in zip(ascii_lowercase, alphabet_map)}
+
+  alphabet_map.update({i.upper() : j.upper() for i, j in alphabet_map.items()})
+
+  cipher_text = ''.join(map(lambda x: alphabet_map[x] if x in alphabet_map else x, plain_text))
+  return cipher_text, alphabet_map
+
+def correct_text(cipher_text, model_output):
+  cipher_text = cipher_text.split(' ')
+  model_output = model_output.split(' ')
+
+  letter_map = {i: {j: 0 for j in ascii_lowercase} for i in ascii_lowercase}
+
+
+  # Levenstein distance for lenghts of words
+  n = len(cipher_text)
+  m = len(model_output)
+
+  i = 0
+  j = 0
+  dp = [[0 for _ in range(m + 1)] for _ in range(n + 1)]
+
+  for i in range(n + 1):
+    dp[i][0] = i
+
+
+  for j in range(m + 1):
+    dp[0][j] = j
+
+  for i in range(1, n + 1):
+    for j in range(1, m + 1):
+      if len(cipher_text[i - 1]) == len(model_output[j - 1]):
+        dp[i][j] = dp[i - 1][j - 1]
+
+      else:
+        dp[i][j] = min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1]) + 1
+
+  i = n
+  j = m
+  while i > 0 and j > 0:
+
+    before = min([(0, dp[i - 1][j - 1]), (1, dp[i - 1][j]), (2, dp[i][j - 1])], key=lambda x: x[1])
+    match before[0]:
+      case 0:
+        if dp[i - 1][j - 1] == dp[i][j]:
+          # If the same we add them to letter map
+          cipher = cipher_text[i-1]
+          model_o = model_output[j-1]
+
+          for c_letter, m_letter in zip(cipher.lower(), model_o.lower()):
+            if c_letter in letter_map and m_letter in letter_map[c_letter]:
+              letter_map[c_letter][m_letter] += 1
+
+        i = i - 1
+        j = j - 1
+      case 1:
+        i = i - 1
+      case 2:
+        j = j - 1
+
+  for letter in ascii_lowercase:
+    letter_sum = sum(letter_map[letter].values())
+    if letter_sum == 0:
+      # That letter wasn't in the text
+      letter_map[letter] = None
+      continue
+
+    # Sorted from most accuring to least
+    letter_map[letter] = [(k, v / letter_sum) for k, v in sorted(letter_map[letter].items(), key=lambda item: item[1], reverse=True)]
+
+  change_map = {
+      i : None for i in ascii_lowercase
+  }
+
+  for i in range(len(ascii_lowercase)):
+    for letter in ascii_lowercase:
+      if letter_map[letter] is None:
+        continue  # That letter wasn't in the text
+
+      # If None then it didn't get substituted earlier
+      map_letter = letter_map[letter][i][0]
+      if (letter_map[letter][i][1] > 0 and (change_map[map_letter] is None
+          or (change_map[map_letter][2] < letter_map[letter][i][1] and change_map[map_letter][1] >= i))):
+        change_map[map_letter] = (letter, i, letter_map[letter][i][1])
+        # Letter, iteration, percentage
+
+  change_map = {i[1][0]: i[0] for i in change_map.items() if i[1] is not None}
+
+  for letter in ascii_lowercase:
+    if letter not in change_map:
+      change_map[letter] = '.'
+
+
+  # Add uppercases
+  change_map.update(
+    {
+      i[0].upper() : i[1].upper() for i in change_map.items()
+    }
+  )
+
+  new_text = []
+  for cipher in cipher_text:
+    new_word = ""
+    for c_letter in cipher:
+      if c_letter in change_map:
+        new_word += change_map[c_letter]
+
+      else:
+        new_word += c_letter
+
+
+    new_text.append(new_word)
+
+  return ' '.join(new_text)
+
+def crack_sub(cipher_text):
+  output = model_pass(alphabet_model, cipher_text, 26)
+  decoded = decode(cipher_text, output)
+  second_pass = model_pass(correction_model, decoded, len(decoded))
+  second_text = correct_text(cipher_text, second_pass)
+  third_pass = model_pass(correction_model, second_text, len(decoded))
+
+  return third_pass
+
+"""
+Use crack_sub() function to solve monoalphabetic substitution ciphers!
+"""
 ```