Update app.py

app.py

@@ -6,10 +6,29 @@ model = AutoModelForSequenceClassification.from_pretrained("gsar78/Greek_Sentime
 tokenizer = AutoTokenizer.from_pretrained("gsar78/Greek_Sentiment")
 
 def predict(text):
+    # Tokenize the input text
     inputs = tokenizer(text, return_tensors="pt")
+    # Get the model outputs
     outputs = model(**inputs)
+    # Apply softmax to the logits to get probabilities
     scores = torch.nn.functional.softmax(outputs.logits, dim=1)
-    return {"Positive": scores[:, 1].item(), "Negative": scores[:, 0].item()}
+    # Get the predicted label
+    predicted_label_idx = scores.argmax(dim=1).item()
+    labels = ["negative", "neutral", "positive"]
+    predicted_label = labels[predicted_label_idx]
+    confidence_score = scores[0, predicted_label_idx].item()
+    # Create a dictionary with the prediction and scores
+    result = {
+        "text": text,
+        "label": predicted_label,
+        "score": confidence_score,
+        "scores": {
+            "positive": scores[0, 2].item(),
+            "neutral": scores[0, 1].item(),
+            "negative": scores[0, 0].item()
+        }
+    }
+    return result
 
 iface = gr.Interface(
     fn=predict,