Update app.py

app.py

@@ -59,6 +59,11 @@ bert_tokenizer = AutoTokenizer.from_pretrained("my_finetuned_model")
 bert_model = AutoModelForSequenceClassification.from_pretrained("my_finetuned_model")
 bert_model.eval()
 
+# CroSlo model/tokenizer
+croslo_tokenizer = AutoTokenizer.from_pretrained("CroSlo")
+croslo_model = AutoModelForSequenceClassification.from_pretrained("CroSlo")
+croslo_model.eval()
+
 label_names = {0: 'pozitivno', 1: 'neutralno', 2: 'negativno'}
 
 def text_to_indices(text, max_len=100):
@@ -103,19 +108,28 @@ def predict_bert(text):
         confidence = probs[0][pred].item()
     return f"{label_names[pred]} (p={confidence:.2f})"
 
+def predict_croslo(text):
+    inputs = croslo_tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = croslo_model(**inputs)
+        probs = F.softmax(outputs.logits, dim=1)
+        pred = torch.argmax(probs, dim=1).item()
+        confidence = probs[0][pred].item()
+    return f"{label_names[pred]} (p={confidence:.2f})"
+
 def predict_all(text):
     return (
         predict_svm(text),
         predict_cnn(text),
         predict_gru(text),
         predict_bert(text),
+        predict_croslo(text),
     )
 
 def clear_all():
     return "", "", "", "", ""
 
 with gr.Blocks() as demo:
-    # Naslov veći, centriran
     gr.Markdown(
         """
         <h1 style="text-align: center; font-size: 48px; margin-bottom: 5px;">Analiza sentimenta</h1>
@@ -141,9 +155,10 @@ with gr.Blocks() as demo:
         with gr.Column():
             gr.Markdown("### Transformers")
             bert_output = gr.Textbox(label="BERTić")
+            croslo_output = gr.Textbox(label="CroSlo BERT")  
 
-    submit_btn.click(fn=predict_all, inputs=input_text, outputs=[svm_output, cnn_output, gru_output, bert_output])
-    clear_btn.click(fn=clear_all, inputs=None, outputs=[input_text, svm_output, cnn_output, gru_output, bert_output])
+    submit_btn.click(fn=predict_all, inputs=input_text, outputs=[svm_output, cnn_output, gru_output, bert_output, croslo_output])
+    clear_btn.click(fn=clear_all, inputs=None, outputs=[input_text, svm_output, cnn_output, gru_output, bert_output, croslo_output])
 
 if __name__ == "__main__":
     demo.launch(share=True)