Update app.py

app.py

@@ -7,28 +7,24 @@ emotion_classifier = pipeline(
     model="Toshifumi/bert-base-multilingual-cased-finetuned-emotion"
 )
 
-# Fallback function for Bengali
-def fallback_bangla_emotion(text):
-    if any(word in text for word in ["দুঃখ", "কষ্ট", "বিরক্ত", "অসন্তুষ্ট"]):
-        return "Sad"
-    elif any(word in text for word in ["ভালো", "খুশি", "আনন্দ"]):
-        return "Happy"
-    elif any(word in text for word in ["রাগ", "গরম", "ক্ষোভ"]):
-        return "Angry"
-    else:
-        return "Neutral"
+# Mapping label_x to actual emotion name
+label_map = {
+    "label_0": "Anger",
+    "label_1": "Sadness",
+    "label_2": "Joy",
+    "label_3": "Love",
+    "label_4": "Fear",
+    "label_5": "Surprise"
+}
 
 def detect_emotion(text):
     try:
         result = emotion_classifier(text)[0]
         label = result["label"]
-
-        if label.lower() == "neutral":
-            return fallback_bangla_emotion(text)
-        else:
-            return label
-    except:
-        return fallback_bangla_emotion(text)
+        emotion = label_map.get(label, "Unknown")
+        return emotion
+    except Exception as e:
+        return "Unknown"
 
 interface = gr.Interface(
     fn=detect_emotion,