Update app.py

app.py

@@ -40,16 +40,15 @@ emotion_icons = {
     "neutral": "😐"
 }
 
-# Prediction function for multi-label output
-def get_emotions(text):
+# Prediction function with adjustable threshold
+def get_emotions(text, threshold):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
         logits = model(**inputs).logits
     probs = sigmoid(logits)[0]
 
-    threshold = 0.3  # You can adjust this threshold
     labels = [model.config.id2label[i] for i, p in enumerate(probs) if p > threshold]
-    icons = [emotion_icons.get(label, '') + ' ' + label.capitalize() for label in labels]
+    icons = [emotion_icons.get(label, '') + ' ' + label.capitalize() + f" ({probs[i]:.2f})" for i, label in enumerate(labels)]
 
     return ", ".join(icons) if icons else "No strong emotion detected."
 
@@ -86,10 +85,13 @@ body {
 
 demo = gr.Interface(
     fn=get_emotions,
-    inputs=gr.Textbox(lines=5, placeholder="Write a sentence or a full paragraph...", label="Your Text"),
+    inputs=[
+        gr.Textbox(lines=5, placeholder="Write a sentence or a full paragraph...", label="Your Text"),
+        gr.Slider(minimum=0.1, maximum=0.9, value=0.3, step=0.05, label="Threshold")
+    ],
     outputs=gr.Textbox(label="Detected Emotions", elem_classes=["output-textbox"]),
     title="🥰 Multi-Label Emotion Detector",
-    description="Enter a sentence or paragraph to detect multiple emotions present in the text.",
+    description="Enter a sentence or paragraph to detect multiple emotions present in the text. Adjust the threshold to be more or less sensitive.",
     theme="default",
     css=custom_css
 )