Add application

app.py

@@ -1,6 +1,18 @@
 import gradio as gr
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-
+import shap
+from transformers import (AutoTokenizer,
+                          AutoModelForSequenceClassification,
+                          TextClassificationPipeline)
+import requests
+import re
+from collections import Counter
+from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
+from bs4 import BeautifulSoup
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+from keybert import KeyBERT
+import torch
 # Buraya İngilizce modelinizi yazın
 model = AutoModelForSequenceClassification.from_pretrained("OsBaran/Roberta-Classification-Model")
 tokenizer = AutoTokenizer.from_pretrained("roberta-base")
@@ -40,10 +52,7 @@ def explain_roberta_prediction(model, tokenizer, input_text):
     explanation += "Modelin kararı aşağıdaki anahtar kelimelere dayanıyor:\n" + ', '.join(important_tokens)
 
     return explanation
-import shap
-from transformers import (AutoTokenizer,
-                          AutoModelForSequenceClassification,
-                          TextClassificationPipeline)
+
 pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device=device)
 def score_and_visualize(text):
   prediction = pipe([text])
@@ -52,13 +61,7 @@ def score_and_visualize(text):
   explainer = shap.Explainer(pipe)
   shap_values = explainer([text])
   shap.plots.text(shap_values)
-import requests
-import re
-from collections import Counter
-from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
-from bs4 import BeautifulSoup
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
+
 api_key = '764e3b45715b449a8aedb8cd8018dfed'
 def fetch_news_from_api(api_key, query, page_size=100):
     url = f'https://newsapi.org/v2/everything?q={query}&pageSize={page_size}&apiKey={api_key}'
@@ -87,7 +90,7 @@ def extract_keywords(text, top_n=5):
 
     return [keyword for keyword, _ in most_common_keywords]
 
-from keybert import KeyBERT
+
 
 kw_model = KeyBERT('all-mpnet-base-v2')  # SBERT kullanarak modeli yükleyin