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import gradio as gr
import string
import re
import pickle
import huggingface_hub
import numpy as np
import nltk
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('omw-1.4')
from nltk.corpus import stopwords
def clean_review(review):
review = review.lower()
review = re.sub(r"http\S+|www.\S+", "", review)
review = re.sub(r"<[^>]*>", "", review)
review = review.replace(".", " ")
review = "".join([c for c in review if c not in string.punctuation])
review = " ".join([word for word in re.split('\W+', review)
if word not in stopwords.words('english')])
wn = nltk.WordNetLemmatizer()
review = " ".join([wn.lemmatize(word, 'r') for word in re.split('\W+', review)])
return review
def find_occurrence(frequency, word, label):
n = 0
if (word, label) in frequency:
n = frequency[(word, label)]
return n
def classify_text(freqs, logprior, text):
loglikelihood = {}
p_w_pos = {}
p_w_neg = {}
# calculate V, the number of unique words in the vocabulary
vocab = set([word for word, label in freqs.keys()])
V = len(vocab)
#calculate num_pos and num_neg - the total number of positive and negative words for all documents
num_pos = num_neg = 0
for word, label in freqs.keys():
# if the label is positive (greater than zero)
if label > 0:
# Increment the number of positive words by the count for this (word, label) pair
num_pos += freqs[(word, label)]
# else, the label is negative
else:
# increment the number of negative words by the count for this (word,label) pair
num_neg += freqs[(word, label)]
# process the review to get a list of words
word_l = clean_review(text).split()
# initialize probability to zero
total_prob = 0
# add the logprior
total_prob += logprior
# For each word in the vocabulary...
for word in word_l:
# get the positive and negative frequency of the word
freq_pos = find_occurrence(freqs, word, 1)
freq_neg = find_occurrence(freqs, word, 0)
# calculate the probability that each word is positive, and negative
p_w_pos[word] = (freq_pos + 1) / (num_pos + V)
p_w_neg[word] = (freq_neg + 1) / (num_neg + V)
if freq_pos + freq_neg > 0:
# calculate the log likelihood of the word
loglikelihood[word] = np.log(p_w_pos[word] / p_w_neg[word])
# add the log likelihood of that word to the probability
total_prob += loglikelihood[word]
else:
loglikelihood[word] = ''
if total_prob > 0:
total_prob = 1
else:
total_prob = 0
return total_prob
model_path = huggingface_hub.hf_hub_download("ajaykarthick/naive-bayes-review-classify-model", "naive-bayes-text-classifier-model")
model_params = pickle.load(open(model_path, mode='rb'))
freqs = model_params['freqs_dict']
logprior = model_params['logprior']
def greet(name):
total_prob = classify_text(freqs, logprior, name)
print(name, str(total_prob))
return 'POSITIVE' if total_prob == 0 else 'NEGATIVE'
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |