Update app.py

app.py

@@ -10,51 +10,36 @@ whisper = pipeline(
 # موديلك
 classifier = pipeline(
     "text-classification",
-    model="MennatullahHany/Abert",
-    return_all_scores=True
+    model="MennatullahHany/Abert"
 )
 
-label_map = {
-    "LABEL_0": "Safe",
-    "LABEL_1": "Danger"
-}
-
 def predict(audio):
+    try:
+        # تحويل الصوت إلى نص
+        transcription = whisper(audio)["text"]
 
-    # تحويل الصوت إلى نص
-    transcription = whisper(audio)["text"]
-
-    # تصنيف النص
-    scores = classifier(transcription)[0]
-
-    probabilities = {}
-
-    best_label = ""
-    best_score = 0
-
-    for item in scores:
-
-        label = label_map.get(item["label"], item["label"])
+        # تشغيل موديلك
+        result = classifier(transcription)
 
-        probabilities[label] = round(float(item["score"]), 4)
+        # اطبع النتيجة في الـ Logs
+        print(result)
 
-        if item["score"] > best_score:
-            best_score = item["score"]
-            best_label = label
+        # أرجعها كما هي
+        return {
+            "transcription": transcription,
+            "model_output": result
+        }
 
-    return {
-        "text": transcription,
-        "emotion": best_label,
-        "confidence": round(best_score, 4),
-        "probabilities": probabilities
-    }
+    except Exception as e:
+        return {
+            "error": str(e)
+        }
 
 demo = gr.Interface(
     fn=predict,
     inputs=gr.Audio(type="filepath", label="Upload Audio"),
     outputs=gr.JSON(label="Prediction"),
-    title="Voice Emotion Detection",
-    description="Upload an audio file to detect whether it is Safe or Danger."
+    title="Voice Emotion Detection"
 )
 
 demo.launch()