v.1.24

app.py

@@ -63,12 +63,12 @@ class ProcessControl:
 
 class EventDetector:
     def __init__(self):
+        """Initialize models with GPU support"""
         try:
-            # Initialize sentiment models with GPU support
+            # Initialize sentiment models
             device = "cuda" if torch.cuda.is_available() else "cpu"
             logger.info(f"Initializing models on device: {device}")
             
-            # Initialize sentiment models
             self.finbert = pipeline(
                 "sentiment-analysis",
                 model="ProsusAI/finbert",
@@ -91,7 +91,7 @@ class EventDetector:
                 max_length=512
             )
             
-            # Initialize MT5 model for event detection
+            # Initialize MT5 model
             self.model_name = "google/mt5-small"
             self.tokenizer = AutoTokenizer.from_pretrained(
                 self.model_name,
@@ -101,13 +101,19 @@ class EventDetector:
             
             self.device = device
             self.initialized = True
-            logger.info(f"All models initialized successfully on {device}")
+            logger.info("All models initialized successfully")
             
         except Exception as e:
             logger.error(f"Error in EventDetector initialization: {str(e)}")
             raise
 
+    @spaces.GPU(duration=30)
+    def initialize_models(self):
+        """Keep this method for compatibility, now just returns initialization status"""
+        return self.initialized
+
     def analyze_sentiment(self, text):
+        """Rest of the analyze_sentiment method remains the same"""
         try:
             if not text or not isinstance(text, str):
                 return "Neutral"
@@ -149,6 +155,7 @@ class EventDetector:
             return "Neutral"
 
     def detect_events(self, text, entity):
+        """Rest of the detect_events method remains the same"""
         if not text or not entity:
             return "Нет", "Invalid input"
             
@@ -201,7 +208,7 @@ Summary: [2-3 sentence summary]</s>"""
                     do_sample=False,
                     pad_token_id=self.tokenizer.pad_token_id,
                     eos_token_id=self.tokenizer.eos_token_id,
-                    no_repeat_ngram_size=3  # Prevent repetition
+                    no_repeat_ngram_size=3
                 )
                 
                 response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
@@ -209,10 +216,8 @@ Summary: [2-3 sentence summary]</s>"""
                 # Extract summary
                 if "Summary:" in response:
                     summary = response.split("Summary:")[1].strip()
-                    # Clean up any remaining prompt artifacts
                     summary = summary.replace('<s>', '').replace('</s>', '').strip()
                 else:
-                    # Create a structured summary based on event type
                     if detected_event == 'Отчетность':
                         summary = f"Компания {entity} опубликовала финансовые показатели."
                     elif detected_event == 'РЦБ':
@@ -222,7 +227,6 @@ Summary: [2-3 sentence summary]</s>"""
                 
                 return detected_event, summary
             
-            # If no keywords matched
             return "Нет", "No significant event detected"
             
         except Exception as e:
@@ -380,7 +384,7 @@ def create_interface():
     control = ProcessControl()
     
     with gr.Blocks(theme=gr.themes.Soft()) as app:
-        gr.Markdown("# AI-анализ мониторинга новостей v.1.23")
+        gr.Markdown("# AI-анализ мониторинга новостей v.1.24")
         
         with gr.Row():
             file_input = gr.File(