Update app.py

app.py

@@ -2,30 +2,27 @@ import gradio as gr
 from transformers import pipeline
 import os
 import numpy as np
-import torch
 import spaces
 
-# load the model
-print("Loading model...")
-model_id = "badrex/mms-300m-arabic-dialect-identifier"
-classifier = pipeline("audio-classification", model=model_id, device='cuda')
-print("Model loaded successfully")
-print("Model moved to GPU successfully")
-
-@spaces.GPU
-def predict(audio_segment, sr=16000):
-    return classifier({"sampling_rate": sr, "raw": audio_segment})
+print("=== Application Starting ===")
 
 # define dialect mapping
 dialect_mapping = {
     "MSA": "Modern Standard Arabic (MSA) - العربية الفصحى الحديثة",
-    "Egyptian": "Egyptian Arabic -  اللهجة المصرية العامية",
+    "Egyptian": "Egyptian Arabic -  اللهجة المصرية العامية", 
     "Gulf": "Peninsular Arabic - لهجة الجزيرة العربية",
     "Levantine": "Levantine Arabic - لهجة بلاد الشام",
     "Maghrebi": "Maghrebi Arabic - اللهجة المغاربية"
 }
 
+@spaces.GPU
 def predict_dialect(audio):
+    # load model inside the GPU function
+    print("Loading model on GPU...")
+    model_id = "badrex/mms-300m-arabic-dialect-identifier"
+    classifier = pipeline("audio-classification", model=model_id)  # no device specified
+    print("Model loaded successfully")
+    
     if audio is None:
         return {"Error": 1.0}
     
@@ -42,8 +39,10 @@ def predict_dialect(audio):
     
     print(f"Processing audio: sample rate={sr}, shape={audio_array.shape}")
     
-    predictions = predict(sr=sr, audio_segment=audio_array)
+    # classify the dialect
+    predictions = classifier({"sampling_rate": sr, "raw": audio_array})
     
+    # format results
     results = {}
     for pred in predictions:
         dialect_name = dialect_mapping.get(pred['label'], pred['label'])
@@ -59,17 +58,12 @@ if os.path.exists(examples_dir):
         if filename.endswith((".wav", ".mp3", ".ogg")):
             examples.append([os.path.join(examples_dir, filename)])
     print(f"Found {len(examples)} example files")
-else:
-    print("Examples directory not found")
 
-# clean description without problematic HTML
 description = """
 By <a href="https://badrex.github.io/">Badr Alabsi</a> with ❤️🤍💚
 
-This is a demo for the accurate and robust Transformer-based <a href="https://huggingface.co/badrex/mms-300m-arabic-dialect-identifier">model</a> for Spoken Arabic Dialect Identification (ADI). 
-From just a short audio clip (5-10 seconds), the model can identify Modern Standard Arabic (MSA) as well as four major regional Arabic varieties: Egyptian Arabic, Peninsular Arabic (Gulf, Yemeni, and Iraqi), Levantine Arabic, and Maghrebi Arabic.
-
-Simply **upload an audio file** 📤 or **record yourself speaking** 🎙️⏺️ to try out the model!
+This demo uses a Transformer-based model for Spoken Arabic Dialect Identification. 
+Upload an audio file or record yourself speaking to identify the Arabic dialect!
 """
 
 demo = gr.Interface(
@@ -83,4 +77,5 @@ demo = gr.Interface(
     flagging_mode=None
 )
 
-demo.launch(share=True)
+print("=== Launching demo ===")
+demo.launch()