from nltk.corpus import wordnet import re from nltk.stem import WordNetLemmatizer stop_words = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't", "its", "whats", "im", "youre", "hes", "shes", "were", "theyre", "cant", "dont", "wont", "isnt", "arent", "wasnt", "werent", "couldnt", "shouldnt", "wouldnt", "ive", "youve", "weve", "theyve", "id", "youd", "lets", "thats", "theres", "heres", "ill", "hell", "shell", "mustnt", "mightnt", "shant", "neednt", "oclock", "cause", "gimme", "wanna", "gonna", "kinda", "sorta", "lemme", "aint", "dunno", "gotta", "yall"] # Create a lemmatizer object lemmatizer = WordNetLemmatizer() #from english_words import get_english_words_set #web2lowerset = get_english_words_set(['web2'], lower=True) # Define the Unicode range for Hindi letters HINDI_UNICODE_RANGE = (0x0900, 0x097F) # Function to check if a given character is a Hindi letter def is_hindi_letter(c): return ord(c) >= HINDI_UNICODE_RANGE[0] and ord(c) <= HINDI_UNICODE_RANGE[1] # In[8]: def en_hi_detection(text): text = re.sub(r'[^\w\s]', ' ', text) words = text.lower().strip().split() count_en = 0 # Lemmatize words for all POS for word in words: for pos in [wordnet.NOUN, wordnet.VERB, wordnet.ADJ, wordnet.ADV]: # print(f"{word} ({pos}): {lemmatizer.lemmatize(word, pos)}") lem_word = lemmatizer.lemmatize(word, pos) if lem_word in wordnet.words(): print("wordnet :",lem_word) count_en+=1 break elif lem_word in stop_words: print("stop_words :",lem_word) count_en+=1 break #print("total english words found :", count_en) #print("length of sentence :", len(words)) #print(count_en/len(words)*100, "% english words found") count = 0 # Check each word for Hindi letters and print the results for word in words: hindi_letters = [] for c in word: if is_hindi_letter(c): hindi_letters.append(c) if hindi_letters: #print(f"Word '{word}' contains Hindi letters: {' '.join(hindi_letters)}") count+=1 else: pass #print(f"Word '{word}' does not contain any Hindi letters.") #print(count/len(words)*100, "% Hindi words found") if count_en/len(words)*100>70: return "eng" elif count/len(words)*100>75: return "hi" else : return "unknown"