Update app.py

app.py

@@ -3,7 +3,6 @@ from transformers import pipeline
 import torch
 
 MODEL = "AnasAlokla/multilingual_go_emotions"
-
 classifier = pipeline(
     "text-classification",
     model=MODEL,
@@ -12,33 +11,28 @@ classifier = pipeline(
     device=0 if torch.cuda.is_available() else -1
 )
 
-def detect_gratitude(text):
+def detect_emotions(text):
     if not text or not text.strip():
-        return {"gratitude_detected": False, "gratitude_score": 0.0, "all_emotions": {}}
-
+        return {"top_emotion": "none", "top_score": 0.0, "all_emotions": {}}
+    
     out = classifier(text)[0]  # list of dicts: each with label & score
-
-    # Find 'Gratitude'
-    grat = next((e for e in out if e['label'].lower() == "gratitude"), None)
-    grat_score = grat['score'] if grat else 0.0
-
-    # Decide
-    THRESH = 0.6  # you adjust
-    detected = grat_score >= THRESH
-
+    
+    # Find the emotion with highest score
+    top_emotion = max(out, key=lambda x: x['score'])
+    
     return {
-        "gratitude_detected": detected,
-        "gratitude_score": round(grat_score, 3),
-        "all_emotions": {e['label']: round(e['score'],3) for e in out}
+        "top_emotion": top_emotion['label'],
+        "top_score": round(top_emotion['score'], 3),
+        "all_emotions": {e['label']: round(e['score'], 3) for e in out}
     }
 
 demo = gr.Interface(
-    fn=detect_gratitude,
+    fn=detect_emotions,
     inputs=gr.Textbox(lines=2, placeholder="Введите English или Russian..."),
     outputs="json",
-    title="Gratitude Detector",
-    description="Detects gratitude using multilingual_go_emotions"
+    title="Emotion Detector",
+    description="Detects emotions using multilingual_go_emotions - shows top emotion"
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()