Update app.py

app.py

@@ -2,30 +2,28 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("BenTouss/mdeberta-eurochef")
 model = AutoModelForSequenceClassification.from_pretrained("BenTouss/mdeberta-eurochef")
 
-def get_labels(text):
+def get_labels_table(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
-    
-    # Get predictions
+
     with torch.no_grad():
         outputs = model(**inputs)
         probs = torch.sigmoid(outputs.logits)[0]
-    
-    # Get predicted labels (threshold = 0.6)
-    predicted_labels = []
+
+    rows = []
     for idx, prob in enumerate(probs):
         if prob > 0.6:
-            label = model.config.id2label[idx]
-            predicted_labels.append((label, prob.item()))
-    
-    return predicted_labels
+            rows.append([model.config.id2label[idx], float(prob)])
 
+    rows.sort(key=lambda x: x[1], reverse=True)
+    return rows
 
-def greet(name):
-    return "Hello " + name + "!!"
+demo = gr.Interface(
+    fn=get_labels_table,
+    inputs=gr.Textbox(lines=3, label="Text"),
+    outputs=gr.Dataframe(headers=["label", "score"]),
+)
 
-demo = gr.Interface(fn=get_labels, inputs="text", outputs="text")
-demo.launch()
+demo.launch()