add examples and update requirements.txt

app.py

@@ -1,12 +1,18 @@
 import gradio as gr
 from transformers import pipeline
 import numpy as np
+import os
 
 # Load the model
-model_id = "badrex/mms-300m-arabic-dialect-identifier"  # Replace with your model ID
-classifier = pipeline("audio-classification", model=model_id)
-
-# Define dialect names for better display
+print("Loading model...")
+model_id = "badrex/mms-300m-arabic-dialect-identifier"
+try:
+    classifier = pipeline("audio-classification", model=model_id)
+    print("Model loaded successfully")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    
+# Define dialect mapping
 dialect_mapping = {
     "MSA": "Modern Standard Arabic",
     "Egyptian": "Egyptian Arabic",
@@ -16,43 +22,66 @@ dialect_mapping = {
 }
 
 def predict_dialect(audio):
-    # The audio input from Gradio is a tuple of (sample_rate, audio_array)
-    if isinstance(audio, tuple) and len(audio) == 2:
+    try:
+        # The audio input from Gradio is a tuple of (sample_rate, audio_array)
+        if audio is None:
+            return {"Error": 1.0}
+            
         sr, audio_array = audio
-    else:
-        # Handle error case
+        
+        # Process the audio input
+        if len(audio_array.shape) > 1:
+            audio_array = audio_array.mean(axis=1)  # Convert stereo to mono
+        
+        print(f"Processing audio: sample rate={sr}, shape={audio_array.shape}")
+        
+        # Classify the dialect
+        predictions = classifier({"sampling_rate": sr, "raw": audio_array})
+        
+        # Format results for display
+        results = {}
+        for pred in predictions:
+            dialect_name = dialect_mapping.get(pred['label'], pred['label'])
+            results[dialect_name] = float(pred['score'])
+        
+        return results
+    except Exception as e:
+        print(f"Error in prediction: {e}")
         return {"Error": 1.0}
-    
-    # Process the audio input
-    if len(audio_array.shape) > 1:
-        audio_array = audio_array.mean(axis=1)  # Convert stereo to mono
-    
-    # Classify the dialect
-    predictions = classifier({"sampling_rate": sr, "raw": audio_array})
-    
-    # Format results for display
-    results = {}
-    for pred in predictions:
-        dialect_name = dialect_mapping.get(pred['label'], pred['label'])
-        results[dialect_name] = float(pred['score'])
-    
-    return results
+
+# Find example files
+example_files = []
+examples_dir = "examples"
+if os.path.exists(examples_dir):
+    for filename in os.listdir(examples_dir):
+        if filename.endswith((".wav", ".mp3", ".ogg")):
+            example_files.append(os.path.join(examples_dir, filename))
+            
+    print(f"Found {len(example_files)} example files")
+else:
+    print("Examples directory not found")
+
+# Examples with labels
+examples = []
+if example_files:
+    for file in example_files:
+        basename = os.path.basename(file)
+        dialect = basename.split("_")[0] if "_" in basename else basename.split(".")[0]
+        label = dialect_mapping.get(dialect, dialect.capitalize())
+        examples.append([file, f"{label} Sample"])
 
 # Create the Gradio interface
 demo = gr.Interface(
     fn=predict_dialect,
-    inputs=gr.Audio(),  # Simplified audio input
+    inputs=gr.Audio(),
     outputs=gr.Label(num_top_classes=5, label="Predicted Dialect"),
     title="Arabic Dialect Identifier",
     description="""This demo identifies Arabic dialects from speech audio.
     Upload an audio file or record your voice speaking Arabic to see which dialect it matches.
     The model identifies: Modern Standard Arabic (MSA), Egyptian, Gulf, Levantine, and Maghrebi dialects.""",
-    examples=[
-        # Optional: Add example audio files here if you have them
-        # ["examples/msa_example.wav"],
-        # ["examples/egyptian_example.wav"],
-    ],
-    allow_flagging="never"
+    examples=examples if examples else None,
+    examples_per_page=5,
+    flagging_mode=None  # Updated from allow_flagging
 )
 
 # Launch the app

requirements.txt

@@ -1,4 +1,5 @@
-gradio>=3.50.2
+gradio>=5.20.0
 transformers>=4.36.0
 torch>=2.0.0
-librosa>=0.10.1
+torchaudio>=2.0.0
+librosa>=0.10.1