Update app.py

app.py

@@ -1,22 +1,20 @@
-import gradio as gr
+import gradio as gr 
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
 # Load model and tokenizer
-model_name = "King-8/confidence-classifier"  # change to your actual model path
+model_name = "King-8/confidence-classifier"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-# Label mapping (update if yours are flipped)
-id2label = model.config.id2label
-
+# Label mapping
 label_map = {
     "LABEL_0": "confident",
     "LABEL_1": "not confident"
 }
 
 def classify_confidence(text):
-    # Tokenize input (exclude token_type_ids)
+    # Tokenize input
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     if "token_type_ids" in inputs:
         del inputs["token_type_ids"]
@@ -27,17 +25,17 @@ def classify_confidence(text):
         logits = outputs.logits
         probs = torch.nn.functional.softmax(logits, dim=1)
         predicted_class = torch.argmax(probs).item()
-        label = id2label[predicted_class]
-        prediction = label_map[label]
+        label = model.config.id2label[predicted_class]
         score = round(probs[0][predicted_class].item() * 100, 2)
+        label_text = label_map[label]
         color = "green" if label == "LABEL_0" else "red"
 
-  return f"<b>Prediction:</b> <span style='color: {color}; font-weight: bold'>{label_text}</span> ({score}%)"
+    return f"<b>Prediction:</b> <span style='color: {color}; font-weight: bold'>{label_text}</span> ({score}%)"
 
 iface = gr.Interface(
     fn=classify_confidence,
     inputs=gr.Textbox(lines=3, placeholder="Enter a statement..."),
-    outputs="text",
+    outputs="html",  # changed from "text" to "html"
     title="🌟 Confidence Classifier",
     description="""
 Enter a statement, and this model will predict whether it shows confidence or not.
@@ -47,4 +45,4 @@ Enter a statement, and this model will predict whether it shows confidence or no
 """,
 )
 
-iface.launch()
+iface.launch()