Upload 7 files

app.py

@@ -60,12 +60,15 @@ def setup_chrome():
 class ReviewAnalysisApp:
     def __init__(self):
         try:
+            logger.info("Chrome kurulumu başlatılıyor...")
             setup_chrome()  # Uygulama başlatılırken Chrome'u kur
+            
+            logger.info("ReviewAnalyzer başlatılıyor...")
             self.analyzer = ReviewAnalyzer()
             logger.info("ReviewAnalyzer başarıyla başlatıldı")
+            
         except Exception as e:
-            logger.error(f"ReviewAnalyzer başlatılırken hata: {str(e)}")
-            # Hata durumunda da analyzer'ı oluştur
+            logger.error(f"ReviewAnalyzer başlatılırken hata: {str(e)}", exc_info=True)  # Tam hata stack'ini göster
             self.analyzer = None
         
     def analyze_url(self, url):

packages.txt

@@ -1,2 +1,2 @@
 chromium
-chromium-driver
+chromium-driver 

requirements.txt

@@ -1,9 +1,9 @@
 pandas
 numpy
-matplotlib
 seaborn
-torch
-transformers
+matplotlib
+torch==2.1.2
+transformers==4.36.2
 nltk
 plotly
 gradio
@@ -13,4 +13,7 @@ tqdm
 regex
 scikit-learn
 google-generativeai
-python-dotenv
+python-dotenv
+requests
+sentencepiece
+protobuf

scripts/review_summarizer.py

@@ -120,16 +120,26 @@ class ReviewAnalyzer:
 
     def setup_sentiment_model(self):
         """Sentiment analiz modelini hazırla"""
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        print(f"Using device for sentiment: {self.device}")
-
-        model_name = "savasy/bert-base-turkish-sentiment-cased"
-        self.sentiment_tokenizer = AutoTokenizer.from_pretrained(model_name)
-        self.sentiment_model = (
-            AutoModelForSequenceClassification.from_pretrained(model_name)
-            .to(self.device)
-            .to(torch.float32)
-        )
+        try:
+            self.device = "cuda" if torch.cuda.is_available() else "cpu"
+            logger.info(f"Using device for sentiment: {self.device}")
+
+            model_name = "savasy/bert-base-turkish-sentiment-cased"
+            
+            logger.info(f"Tokenizer yükleniyor: {model_name}")
+            self.sentiment_tokenizer = AutoTokenizer.from_pretrained(model_name)
+            
+            logger.info(f"Model yükleniyor: {model_name}")
+            self.sentiment_model = (
+                AutoModelForSequenceClassification.from_pretrained(model_name)
+                .to(self.device)
+                .to(torch.float32)
+            )
+            logger.info("Sentiment model başarıyla yüklendi")
+            
+        except Exception as e:
+            logger.error(f"Sentiment model kurulumunda hata: {str(e)}", exc_info=True)
+            raise
 
     def filter_reviews(self, df):
         """Ürün ile ilgili olmayan yorumları filtrele"""