Update app.py

app.py

@@ -44,38 +44,38 @@ def load_models():
         
         # Load summarization model
         if has_pipeline:
-            # Use pipeline if available
+            # Use pipeline if available, now using the updated model
             models['summarizer'] = pipeline(
                 "summarization", 
-                model="facebook/bart-base", 
+                model="marianna13/flan-t5-base-summarization", 
                 max_length=100,
                 truncation=True
             )
         else:
-            # Fall back to basic model loading
+            # Fall back to basic model loading using the updated summarization model
             try:
-                models['summarizer_model'] = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-base")
-                models['summarizer_tokenizer'] = AutoTokenizer.from_pretrained("facebook/bart-base")
+                models['summarizer_model'] = AutoModelForSeq2SeqLM.from_pretrained("marianna13/flan-t5-base-summarization")
+                models['summarizer_tokenizer'] = AutoTokenizer.from_pretrained("marianna13/flan-t5-base-summarization")
             except Exception as e:
                 st.error(f"Error loading summarization model: {e}")
                 models['summarizer_model'] = None
                 models['summarizer_tokenizer'] = None
         
-        # Load sentiment model for evaluation
+        # Load sentiment model for evaluation - updated model
         if has_pipeline:
             # Use pipeline if available
             models['evaluator'] = pipeline(
                 "sentiment-analysis", 
-                model="lxyuan/distilbert-base-multilingual-cased-sentiments-student"
+                model="cardiffnlp/twitter-roberta-base-sentiment-latest"
             )
         else:
-            # Fall back to basic model loading
+            # Fall back to basic model loading using the updated evaluation model
             try:
                 models['evaluator_model'] = AutoModelForSequenceClassification.from_pretrained(
-                    "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+                    "cardiffnlp/twitter-roberta-base-sentiment-latest"
                 )
                 models['evaluator_tokenizer'] = AutoTokenizer.from_pretrained(
-                    "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+                    "cardiffnlp/twitter-roberta-base-sentiment-latest"
                 )
             except Exception as e:
                 st.error(f"Error loading sentiment model: {e}")