create app.py

app.py

@@ -0,0 +1,77 @@
+import pandas as pd
+import numpy as np
+from simpletransformers.classification import ClassificationModel
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+from collections import Counter
+import nltk
+from nltk.corpus import stopwords
+import re
+import string
+import gradio as gr
+
+
+nltk.download('stopwords')
+stop_words_list = stopwords.words('turkish')
+false_text = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
+
+
+def preprocess_text(text):
+    # Küçük harflere çevirme
+    text = text.lower()
+    # Satır sonu karakterlerini kaldırma
+    import re
+    text = re.sub(r'\n', ' ', text)
+    # Rakamları kaldırma
+    text = re.sub(r'\d', '', text)
+    # Noktalama işaretlerini kaldırma
+    import string
+    text = text.translate(str.maketrans("", "", string.punctuation))
+    # Stop-words'leri kaldırma
+    words = text.split()
+    words = [word for word in words if not word in stop_words_list]
+    # Veri setindeki hatalı verilerin kaldırılması
+    words = [word for word in words if not word in false_text]
+    # Tekrarlanan karakterlerin kaldırılması
+    words = [re.sub(r'(.)\1{1,}', r'\1\1', word) for word in words]
+    # Tekrarlanan boşlukların kaldırılması
+    words = [word.strip() for word in words if len(word.strip()) > 1]
+
+    text = " ".join(words)
+    return text
+
+
+def predict(texts):
+    model_path = "bert_model"
+    model = ClassificationModel('bert', model_path, use_cuda=False)
+    predictions, _ = model.predict(texts)
+    return [result_predict(prediction) for prediction in predictions]
+
+def result_predict(num):
+    if num == 4:
+        return 'OTHER'
+    elif num == 1:
+        return 'RACIST'
+    elif num == 0:
+        return 'INSULT'
+    elif num == 3:
+        return 'PROFANITY'
+    elif num == 2:
+        return 'SEXIST'
+
+def gradio_comment(comment):
+  text_to_predict = ["hayvan gibi iş yapma öküz"]
+  results = predict(text_to_predict)
+
+  for text, result in zip(text_to_predict, results):
+    print(f"Metin: {text}\nTahmin: {result}\n")
+
+
+GradioGUI = gr.Interface(
+    fn=gradio_comment,
+    inputs='text',
+    outputs='text',
+    title='Aşağılayıcı Yorum Tespiti',
+    css='''span{text-transform: uppercase} p{text-align: center}''')
+
+GradioGUI.launch()